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81 Commits

Author SHA1 Message Date
allegroai
2c3f0e4ba3 Update AWS images 0.12.1 2019-12-14 23:46:21 +02:00
allegroai
c48eb34d8d Add resource monitoring 2019-12-14 23:35:42 +02:00
allegroai
49515e06e1 Optimize thread processing 2019-12-14 23:35:18 +02:00
allegroai
4a1d97c02f typo 2019-12-14 23:34:00 +02:00
allegroai
6c6c1c3f41 Add server resource monitoring 2019-12-14 23:33:36 +02:00
allegroai
0ad687008c Improve server update checks 2019-12-14 23:33:04 +02:00
Allegro AI
fe3dbc92dc Update README.md 2019-11-19 00:14:45 +02:00
Allegro AI
dc53970ff0 Update README.md 2019-11-19 00:01:12 +02:00
Allegro AI
73592b991b Update README.md 2019-11-16 00:10:19 +02:00
Allegro AI
47b981a993 Update README.md 2019-11-16 00:08:36 +02:00
Allegro AI
b500bcab0b Update faq.md 2019-11-16 00:07:30 +02:00
allegroai
59e910db1a Add docker-compose Windows support 2019-11-16 00:04:04 +02:00
allegroai
2ecb430f02 Documentation 2019-11-10 00:23:45 +02:00
Allegro AI
a08722e394 Update README.md 2019-11-10 00:18:16 +02:00
Allegro AI
67c210d9d7 Update README.md 2019-11-10 00:14:30 +02:00
Allegro AI
101ba540f4 Update README.md 2019-11-10 00:08:52 +02:00
Allegro AI
82fc28d477 Update README.md 2019-11-10 00:06:12 +02:00
Allegro AI
7b73f699d2 Update README.md 2019-11-10 00:05:21 +02:00
allegroai
a7e5380f67 Add configuration example, experiments watchdog 2019-11-10 00:03:57 +02:00
allegroai
bcade31786 Add configuration example, limit user login 2019-11-09 23:59:08 +02:00
Allegro AI
6b902f85f4 Update README.md 2019-11-09 23:54:59 +02:00
allegroai
6d4c974045 Documentation 2019-11-09 23:45:12 +02:00
allegroai
2346c6f3f5 Documentation 2019-11-09 23:19:21 +02:00
Allegro AI
82e51b4d36 Update README.md 2019-11-09 23:07:43 +02:00
allegroai
e63599254e Documentation 2019-11-09 21:32:30 +02:00
allegroai
8e7e234161 Add finer control for mongo/elastic/redis host configuration 2019-11-09 21:29:23 +02:00
allegroai
17d94b26c3 Documentation 2019-11-06 12:25:39 +02:00
allegroai
1e701becd3 Upgrade to v0.12 2019-10-29 20:43:46 +02:00
allegroai
18c8dd449d Fix jupyter support 2019-10-29 20:43:40 +02:00
allegroai
50031c4d6d Upgrade to v0.12 2019-10-29 20:37:29 +02:00
allegroai
6101dc4f11 Add check for server updates 2019-10-28 21:49:16 +02:00
allegroai
5d17059cbe Improve docker compose support 2019-10-27 00:10:08 +03:00
allegroai
b93e843143 Add schema files 2019-10-26 01:14:47 +03:00
allegroai
1a732ccd8e Add API version 2.4 with new trains-server capabilities including DevOps and scheduling 2019-10-25 15:36:58 +03:00
allegroai
2ea25e498f Removed redundant license file 2019-10-22 22:54:20 +03:00
allegroai
1b1cdb34ad Fix docker compose file, replaced deprecated 'links' with 'depends_on' 2019-10-22 18:43:27 +03:00
allegroai
e171a8b523 Update auto update AMI images 2019-10-16 23:28:59 +03:00
allegroai
539b76d362 Fix increase mongodb memory limit for large queries 2019-10-12 21:37:24 +03:00
Allegro AI
64b5e1f1f0 Update faq.md 2019-10-07 14:04:08 +03:00
Allegro AI
6a1eb9cea0 Update docker_setup.md 2019-10-07 14:03:08 +03:00
Allegro AI
24907b4eaa Update README.md 2019-10-07 14:00:51 +03:00
allegroai
efc540b837 Documentation 2019-09-25 17:52:41 +03:00
allegroai
96ffc89c64 Update AMI's to new version 2019-09-24 21:46:07 +03:00
allegroai
4f2564d33a Add Artifacts support, changed tags to system_tags and added user tags
Add hyper parameter sorting
Add min/max value for all time series metrics
2019-09-24 21:35:41 +03:00
allegroai
70ae090cc0 Documentation 2019-09-19 23:04:45 +03:00
Allegro AI
4f01778961 Merge pull request #26 from jayanthkoushik/patch-1
Bind conf for apiserver in docker-compose.yml
2019-09-12 21:53:17 +03:00
Jayanth Koushik
596bdd06ec Bind conf for apiserver in docker-compose.yml 2019-09-12 14:20:30 -04:00
allegroai
6c56d0fc33 Documentation 2019-09-02 01:00:45 +03:00
allegroai
5f0213d2de Update AWS images 2019-08-22 13:48:07 +03:00
Allegro AI
15eb00a931 Update LICENSE 2019-08-21 00:19:59 +03:00
allegroai
becc4fb6a2 Documentation 2019-08-15 23:56:19 +03:00
allegroai
32476a216a Documentation 2019-08-14 04:01:41 +03:00
allegroai
a9ba1580dc Documentation 2019-08-09 03:44:17 +03:00
allegroai
cfcd0b22a0 Documentation 2019-08-09 03:40:28 +03:00
allegroai
780355250c Documentation 2019-08-09 03:33:45 +03:00
allegroai
fd65ad38bc Documentation 2019-08-09 03:24:47 +03:00
allegroai
e29973a0b2 Typo 2019-08-09 00:30:16 +03:00
allegroai
c259d0883e Documentation 2019-08-08 12:02:30 +03:00
allegroai
9eab017a31 Documentation 2019-08-08 12:01:11 +03:00
allegroai
68c7f307a2 Documentation 2019-08-08 11:58:19 +03:00
allegroai
0aa5694b58 Documentation 2019-08-08 02:22:36 +03:00
allegroai
639d72c5d6 Documentation 2019-08-08 02:08:39 +03:00
allegroai
70708ecdcc Documentation 2019-08-08 02:01:59 +03:00
allegroai
dacdd5e965 Documentation 2019-08-08 02:00:15 +03:00
allegroai
c199976f70 Improved docker-compose installation process 2019-08-08 01:51:40 +03:00
allegroai
c3e2bc5ad7 Add FAQ 2019-08-01 19:36:58 +03:00
allegroai
f0c900c174 Documentation 2019-07-29 23:47:52 +03:00
allegroai
1bdbc44720 Fix, always restart trains-server container 2019-07-25 02:29:39 +03:00
allegroai
c6e765bd07 renamed 2019-07-25 02:26:07 +03:00
allegroai
c037ddd044 Add unified docker compose (all three trains-server services running on the same docker). Used for easier installation, such as on OS X. 2019-07-25 02:15:34 +03:00
allegroai
ffe4764f20 Add automatically updating AMIs 2019-07-22 11:48:42 +03:00
allegroai
1681fd6bf4 Fix AMI image ids 2019-07-21 19:18:59 +03:00
allegroai
e55ce5536a Add fixed users mode documentation 2019-07-17 18:46:12 +03:00
allegroai
b714952ab1 Add v0.10.1 pre-built AMI 2019-07-17 18:18:56 +03:00
allegroai
07fd8b9f2f Changed, web serving through NGINX 2019-07-17 18:18:33 +03:00
allegroai
d24f633a8e Add easier sub-domains configuration 2019-07-17 18:17:27 +03:00
allegroai
bed714890d Add File server CORS support 2019-07-17 18:16:43 +03:00
allegroai
02671910b2 Add support for fix user list credentials 2019-07-17 18:16:27 +03:00
allegroai
1a00f29415 Add support for fix user list credentials 2019-07-17 18:15:58 +03:00
allegroai
b7614622fc Changed webserver is deprecated, Web UI served through NGINX 2019-07-17 18:15:19 +03:00
allegroai
bc2cbe9a91 Documentation 2019-07-12 01:05:07 +03:00
135 changed files with 9710 additions and 3260 deletions

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.gitignore vendored
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static/dashboard/node_modules
static/webapp/node_modules
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scripts/
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Server Side Public License
VERSION 1, OCTOBER 16, 2018
Copyright © 2018 MongoDB, Inc.
Copyright © 2019 allegro.ai, Inc.
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.

369
README.md
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@@ -11,7 +11,7 @@
The **trains-server** is the backend service infrastructure for [TRAINS](https://github.com/allegroai/trains).
It allows multiple users to collaborate and manage their experiments.
By default, TRAINS is set up to work with the TRAINS demo server, which is open to anyone and resets periodically.
By default, TRAINS is set up to work with the TRAINS demo server, which is open to anyone and resets periodically.
In order to host your own server, you will need to install **trains-server** and point TRAINS to it.
**trains-server** contains the following components:
@@ -22,287 +22,242 @@ In order to host your own server, you will need to install **trains-server** and
* Querying experiments history, logs and results
* Locally-hosted file server for storing images and models making them easily accessible using the Web-App
You can quickly setup your **trains-server** using a pre-built Docker image (see [Installation](#installation)).
You can quickly setup your **trains-server** using:
- [Docker Installation](#installation)
- Pre-built Amazon [AWS image](#aws)
- [Kubernetes Helm](https://github.com/allegroai/trains-server-helm#trains-server-for-kubernetes-clusters-using-helm)
or manual [Kubernetes installation](https://github.com/allegroai/trains-server-k8s#trains-server-for-kubernetes-clusters)
When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)).
## System diagram
## System design
![Alt Text](https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true)
**trains-server** has two supported configurations:
- Single IP (domain) with the following open ports
- Web application on port 8080
- API service on port 8008
- File storage service on port 8081
## Install / Upgrade - AWS
- Sub-Domain configuration with default http/s ports (80 or 443)
- Web application on sub-domain: app.\*.\*
- API service on sub-domain: api.\*.\*
- File storage service on sub-domain: files.\*.\*
Use our pre-installed Amazon Machine Image for easy deployment in AWS.
## Install / Upgrade - AWS <a name="aws"></a>
Details and instructions can be found [here](docs/install_aws.md).
Use one of our pre-installed Amazon Machine Images for easy deployment in AWS.
## Installation - Docker
For details and instructions, see [TRAINS-server: AWS pre-installed images](docs/install_aws.md).
This section contains the instructions to setup and launch a pre-built Docker image for the **trains-server**.
This is the quickest way to get started with your own server.
Alternatively, you can build the entire trains-server architecture using the code available in our repositories.
## Docker Installation - Linux, macOS, and Windows <a name="installation"></a>
**Please Note**:
* This Docker image was tested with Linux, only. For Windows users, we recommend running the server
on a Linux virtual machine.
Use our pre-built Docker image for easy deployment in Linux and macOS. <br>
For [Windows](https://github.com/allegroai/trains-server/blob/master/docs/faq.md#docker_compose_win10), please see detailed docker-compose installation instructions on our [FAQ](https://github.com/allegroai/trains-server/blob/master/docs/faq.md#docker_compose_win10).<br>
Latest docker images can be found [here](https://hub.docker.com/r/allegroai/trains).
* All command-line instructions below assume you're using `bash`.
1. Setup Docker (docker-compose installation details: [Ubuntu](docs/faq.md#ubuntu) / [macOS](docs/faq.md#mac-osx))
### Prerequisites
Make sure you are logged in as a user with sudo privileges.
### Setup
#### Step 1: Install Docker CE
In order to run the pre-packaged **trains-server**, install Docker.
* See [Supported platforms](https://docs.docker.com/install//#support) in the Docker documentation for instructions
* For example, to install in [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) / Mint (x86_64/amd64):
<details>
<summary>Make sure ports 8080/8081/8008 are available for the TRAINS-server services:</summary>
For example, to see if port `8080` is in use:
```bash
sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
. /etc/os-release
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $UBUNTU_CODENAME stable"
sudo apt-get update
sudo apt-get install -y docker-ce
$ sudo lsof -Pn -i4 | grep :8080 | grep LISTEN
```
</details>
Increase vm.max_map_count for `ElasticSearch` docker
#### Step 2: Setup the Docker daemon
- Linux
```bash
$ echo "vm.max_map_count=262144" > /tmp/99-trains.conf
$ sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
$ sudo sysctl -w vm.max_map_count=262144
$ sudo service docker restart
```
- macOS
```bash
$ screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
$ sysctl -w vm.max_map_count=262144
```
To run the ElasticSearch Docker container, setup the Docker daemon by modifying the default
values required by Elastic in your Docker configuration file (see [Notes for production use and defaults](https://www.elastic.co/guide/en/elasticsearch/reference/master/docker.html#_notes_for_production_use_and_defaults)). We provide instructions for the most common Docker configuration files.
Edit or create the Docker configuration file:
* If your system contains a `/etc/sysconfig/docker` Docker configuration file, edit it.
Add the options in quotes to the available arguments in the `OPTIONS` section:
1. Create local directories for the databases and storage.
```bash
OPTIONS="--default-ulimit nofile=1024:65536 --default-ulimit memlock=-1:-1"
$ sudo mkdir -p /opt/trains/data/elastic
$ sudo mkdir -p /opt/trains/data/mongo/db
$ sudo mkdir -p /opt/trains/data/mongo/configdb
$ sudo mkdir -p /opt/trains/data/redis
$ sudo mkdir -p /opt/trains/logs
$ sudo mkdir -p /opt/trains/data/fileserver
$ sudo mkdir -p /opt/trains/config
```
* Otherwise, edit `/etc/docker/daemon.json` (if it exists) or create it (if it does not exist).
Set folder permissions
- Linux
```bash
$ sudo chown -R 1000:1000 /opt/trains
```
- macOS
```bash
$ sudo chown -R $(whoami):staff /opt/trains
```
Add or modify the `defaults-ulimits` section as shown below. Be sure the `defaults-ulimits` section contains the `nofile` and `memlock` sub-sections and values shown.
**Note**: Your configuration file may contain other sections. If so, confirm that the sections are separated by commas (valid JSON format). For more information about Docker configuration files, see [Daemon configuration file](https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file) in the Docker documentation.
The **trains-server** required defaults values are:
```json
{
"default-ulimits": {
"nofile": {
"name": "nofile",
"hard": 65536,
"soft": 1024
},
"memlock":
{
"name": "memlock",
"soft": -1,
"hard": -1
}
}
}
```
#### Step 3: Restart the Docker daemon
After modifying the configuration file, restart the Docker daemon:
```bash
sudo service docker stop
sudo service docker start
```
#### Step 4: Set the Maximum Number of Memory Map Areas
The maximum number of memory map areas a process can use is defined
using the `vm.max_map_count` kernel setting.
Elastic requires that `vm.max_map_count` is at least 262144 (see [Production mode](https://www.elastic.co/guide/en/elasticsearch/reference/master/docker.html#docker-cli-run-prod-mode)).
* For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19 users, we tested the following commands to set
`vm.max_map_count`:
1. Download the `docker-compose.yml` file, either download [manually](https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml) or execute:
```bash
sudo echo "vm.max_map_count=262144" > /tmp/99-trains.conf
sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
sudo sysctl -w vm.max_map_count=262144
$ curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml
```
* For information about setting this parameter on other systems, see the [elastic](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode) documentation.
1. Launch the Docker containers <a name="launch-docker"></a>
#### Step 5: Choose a Data Directory
```bash
$ docker-compose -f docker-compose.yml up
```
Choose a directory on your system in which all data maintained by the **trains-server** is stored.
Create this directory, and set its owner and group to `uid` 1000. The data stored in this directory will include the database, uploaded files and logs.
1. Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
For example, if your data directory is `/opt/trains`, then use the following command:
* Web server on port `8080`
* API server on port `8008`
* File server on port `8081`
```bash
sudo mkdir -p /opt/trains/data/elastic && sudo chown -R 1000:1000 /opt/trains
```
**\* If something went wrong along the way, check our FAQ: [Docker Setup](docs/docker_setup.md#setup-docker), [Ubuntu Support](docs/faq.md#ubuntu), [macOS Support](docs/faq.md#mac-osx)**
### Configuration
## Optional Configuration
The **trains-server** default configuration can be easily overridden using external configuration files. By default, the server will look for these files in `/opt/trains/config`.
If the configuration is changed while the server is running, the server should be restarted for changes to take effect.
In order to apply the new configuration, you must restart the server (see [Restarting trains-server](#restart-server)).
<!---
#### Fixed users mode (basic users management)
### Adding Web Login Authentication
In this mode, the server authenticates users based on a pre-configured users list.
By default anyone can login to the **trains-server** Web-App.
You can configure the **trains-server** to allow only a specific set of users to access the system.
Enable this feature by placing an `apiserver.conf` file under `/opt/trains/config`, containing for example:
Enable this feature by placing `apiserver.conf` file under `/opt/trains/config`.
fixed_users {
enabled: true
users: [
{
username: "jane"
password: "123456"
name: "Jane Doe"
},
{
username: "john"
password: "abcdef"
name: "John Doe"
}
]
}
-->
#### Non-responsive experiments watchdog
Sample `apiserver.conf` configuration file can be found [here](https://github.com/allegroai/trains-server/blob/master/docs/apiserver.conf)
This watchdog monitors experiments that were not updated for a given period of time, and marks them as `stopped`. The watchdog is always active.
To apply the changes, you must [restart the *trains-server*](#restart-server).
To change the watchdog's timeouts, place a `services.conf` file under `/opt/trains/config`, containing for example:
### Configuring the Non-Responsive Experiments Watchdog
tasks {
non_responsive_tasks_watchdog {
# In-progress tasks that haven't been updated for at least 'value' seconds will be stopped by the watchdog
threshold_sec: 7200
# Watchdog will sleep for this number of seconds after each cycle
watch_interval_sec: 900
The non-responsive experiment watchdog, monitors experiments that were not updated for a given period of time,
and marks them as `aborted`. The watchdog is always active with a default of 7200 seconds (2 hours) of inactivity threshold.
To change the watchdog's timeouts, place a `services.conf` file under `/opt/trains/config`.
Sample watchdog `services.conf` configuration file can be found [here](https://github.com/allegroai/trains-server/blob/master/docs/services.conf)
To apply the changes, you must [restart the *trains-server*](#restart-server).
### Restarting trains-server <a name="restart-server"></a>
To restart the **trains-server**, you must first stop the containers, and then restart them.
```bash
$ docker-compose down
$ docker-compose -f docker-compose.yml up
```
## Configuring **TRAINS** client
Once you have installed the **trains-server**, make sure to configure **TRAINS** [client](https://github.com/allegroai/trains)
to use your locally installed server (and not the demo server).
- Run the `trains-init` command for an interactive setup
- Or manually edit `~/trains.conf` file, making sure the `api_server` value is configured correctly, for example:
api {
# API server on port 8008
api_server: "http://localhost:8008"
# web_server on port 8080
web_server: "http://localhost:8080"
# file server on port 8081
files_server: "http://localhost:8081"
}
}
### Launching Docker Containers
* Notice that if you setup **trains-server** in a sub-domain configuration, there is no need to specify a port number,
it will be inferred from the http/s scheme.
**Note**:
* If your data directory is not `/opt/trains`, please find and replace `/opt/trains` in the following commands with your data directory path
* Make sure ports `8008`, `8080` and `8081` are not in use before starting the docker containers, as the containers will fail to initialize if these ports are already taken. If the following commands shows no output, the ports are available:
```bash
sudo netstat -tplna | egrep "8008|8080|8081"
```
To launch the Docker containers, use the following commands:
```bash
sudo docker run -d --restart="always" --name="trains-elastic" -e "ES_JAVA_OPTS=-Xms2g -Xmx2g" -e "bootstrap.memory_lock=true" -e "cluster.name=trains" -e "discovery.zen.minimum_master_nodes=1" -e "node.name=trains" -e "script.inline=true" -e "script.update=true" -e "thread_pool.bulk.queue_size=2000" -e "thread_pool.search.queue_size=10000" -e "xpack.security.enabled=false" -e "xpack.monitoring.enabled=false" -e "cluster.routing.allocation.node_initial_primaries_recoveries=500" -e "node.ingest=true" -e "http.compression_level=7" -e "reindex.remote.whitelist=*.*" -e "script.painless.regex.enabled=true" --network="host" -v /opt/trains/data/elastic:/usr/share/elasticsearch/data docker.elastic.co/elasticsearch/elasticsearch:5.6.16
```
```bash
sudo docker run -d --restart="always" --name="trains-mongo" -v /opt/trains/data/mongo/db:/data/db -v /opt/trains/data/mongo/configdb:/data/configdb --network="host" mongo:3.6.5
```
```bash
sudo docker run -d --restart="always" --name="trains-fileserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/data/fileserver:/mnt/fileserver allegroai/trains:latest fileserver
```
```bash
sudo docker run -d --restart="always" --name="trains-apiserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/config:/opt/trains/config allegroai/trains:latest apiserver
```
```bash
sudo docker run -d --restart="always" --name="trains-webserver" --network="host" -v /opt/trains/logs:/var/log/trains allegroai/trains:latest webserver
```
After the **trains-server** Dockers are up, the following are available:
* API server on port `8008`
* Web server on port `8080`
* File server on port `8081`
### Configuring **trains**
Once you have installed the **trains-server**, make sure to configure **trains** to use your locally installed server (and not the demo server).
If you have already installed **trains**, run the `trains-init` command for an interactive setup or edit your `trains.conf` file and make sure the `api.host` value is configured as follows:
api {
host: "http://localhost:8008"
}
See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#configuration) for more details.
## What next?
Now that the **trains-server** is installed, and TRAINS is configured to use it,
you can [use](https://github.com/allegroai/trains#using-trains) TRAINS in your experiments and view them in the web server,
Now that the **trains-server** is installed, and TRAINS is configured to use it,
you can [use](https://github.com/allegroai/trains#using-trains) TRAINS in your experiments and view them in the web server,
for example http://localhost:8080
## Upgrade
## Upgrading <a name="upgrade"></a>
We are constantly updating, improving and adding to the **trains-server**.
New releases will include new pre-built Docker images.
When we release a new version and include a new pre-built Docker image for it, upgrade as follows:
1. Shut down and remove each of your Docker instances using the following commands:
1. Shut down the docker containers
```bash
$ docker-compose down
```
sudo docker stop <docker-name>
sudo docker rm -v <docker-name>
1. We highly recommend backing up your data directory before upgrading.
The Docker names are (see [Launching Docker Containers](#launching-docker-containers)):
Assuming your data directory is `/opt/trains`, to archive all data into `~/trains_backup.tgz` execute:
* `trains-elastic`
* `trains-mongo`
* `trains-fileserver`
* `trains-apiserver`
* `trains-webserver`
```bash
$ sudo tar czvf ~/trains_backup.tgz /opt/trains/data
```
2. Pull the new **trains-server** docker image using the following command:
<details>
<summary>Restore instructions:</summary>
sudo docker pull allegroai/trains:latest
If you wish to pull a different version, replace `latest` with the required version number, for example:
To restore this example backup, execute:
```bash
$ sudo rm -R /opt/trains/data
$ sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
```
</details>
sudo docker pull allegroai/trains:0.10.0
3. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
1. Download the latest `docker-compose.yml` file, either [manually](https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml) or execute:
For example, if your data directory is `/opt/trains`, use the following command:
```bash
$ curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml
```
sudo tar czvf ~/trains_backup.tgz /opt/trains/data
1. Spin up the docker containers, it will automatically pull the latest trains-server build
```bash
$ docker-compose -f docker-compose.yml pull
$ docker-compose -f docker-compose.yml up
```
This back ups all data to an archive in your home directory.
**\* If something went wrong along the way, check our FAQ: [Docker Upgrade](docs/docker_setup.md#common-docker-upgrade-errors)**
To restore this example backup, use the following command:
sudo rm -R /opt/trains/data
sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
## Community & Support
4. Launch the newly released Docker image (see [Launching Docker Containers](#launching-docker-containers)).
If you have any questions, look to the TRAINS-server [FAQ](https://github.com/allegroai/trains-server/blob/master/docs/faq.md), or
tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/trains) with '**trains**' tag.
For feature requests or bug reports, please use [GitHub issues](https://github.com/allegroai/trains-server/issues).
Additionally, you can always find us at *trains@allegro.ai*
## License
[Server Side Public License v1.0](https://github.com/mongodb/mongo/blob/master/LICENSE-Community.txt)
**trains-server** relies on both [MongoDB](https://github.com/mongodb/mongo) and [ElasticSearch](https://github.com/elastic/elasticsearch).
With the recent changes in both MongoDB's and ElasticSearch's OSS license, we feel it is our responsibility as a
With the recent changes in both MongoDB's and ElasticSearch's OSS license, we feel it is our responsibility as a
member of the community to support the projects we love and cherish.
We believe the cause for the license change in both cases is more than just,
We believe the cause for the license change in both cases is more than just,
and chose [SSPL](https://www.mongodb.com/licensing/server-side-public-license) because it is the more general and flexible of the two licenses.
This is our way to say - we support you guys!

View File

@@ -0,0 +1,87 @@
version: "3.6"
services:
trainsserver:
command:
- -c
- "echo \"#!/bin/bash\" > /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh webserver&\" >> /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh fileserver&\" >> /opt/trains/all.sh && echo \"/opt/trains/wrapper.sh apiserver\" >> /opt/trains/all.sh && cat /opt/trains/all.sh && chmod +x /opt/trains/all.sh && /opt/trains/all.sh"
entrypoint: /bin/bash
container_name: trains-server
image: allegroai/trains:latest
ports:
- 8008:8008
- 8080:80
- 8081:8081
restart: unless-stopped
volumes:
- /opt/trains/logs:/var/log/trains
- /opt/trains/data/fileserver:/mnt/fileserver
depends_on:
- redis
- mongo
- elasticsearch
environment:
ELASTIC_SERVICE_HOST: elasticsearch
MONGODB_SERVICE_HOST: mongo
REDIS_SERVICE_HOST: redis
networks:
- backend
elasticsearch:
networks:
- backend
container_name: trains-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
bootstrap.memory_lock: "true"
cluster.name: trains
cluster.routing.allocation.node_initial_primaries_recoveries: "500"
discovery.zen.minimum_master_nodes: "1"
http.compression_level: "7"
node.ingest: "true"
node.name: trains
reindex.remote.whitelist: '*.*'
script.inline: "true"
script.painless.regex.enabled: "true"
script.update: "true"
thread_pool.bulk.queue_size: "2000"
thread_pool.search.queue_size: "10000"
xpack.monitoring.enabled: "false"
xpack.security.enabled: "false"
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
restart: unless-stopped
volumes:
- /opt/trains/data/elastic:/usr/share/elasticsearch/data
ports:
- "9200:9200"
mongo:
networks:
- backend
container_name: trains-mongo
image: mongo:3.6.5
restart: unless-stopped
command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
volumes:
- /opt/trains/data/mongo/db:/data/db
- /opt/trains/data/mongo/configdb:/data/configdb
ports:
- "27017:27017"
redis:
networks:
- backend
container_name: trains-redis
image: redis:5.0
restart: unless-stopped
volumes:
- /opt/trains/data/redis:/data
ports:
- "6379:6379"
networks:
backend:
driver: bridge

117
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@@ -0,0 +1,117 @@
version: "3.6"
services:
apiserver:
command:
- apiserver
container_name: trains-apiserver
image: allegroai/trains:latest
restart: unless-stopped
volumes:
- c:/opt/trains/logs:/var/log/trains
- c:/opt/trains/config:/opt/trains/config
depends_on:
- redis
- mongo
- elasticsearch
- fileserver
environment:
ELASTIC_SERVICE_HOST: elasticsearch
MONGODB_SERVICE_HOST: mongo
REDIS_SERVICE_HOST: redis
ports:
- "8008:8008"
networks:
- backend
elasticsearch:
networks:
- backend
container_name: trains-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
bootstrap.memory_lock: "true"
cluster.name: trains
cluster.routing.allocation.node_initial_primaries_recoveries: "500"
discovery.zen.minimum_master_nodes: "1"
http.compression_level: "7"
node.ingest: "true"
node.name: trains
reindex.remote.whitelist: '*.*'
script.inline: "true"
script.painless.regex.enabled: "true"
script.update: "true"
thread_pool.bulk.queue_size: "2000"
thread_pool.search.queue_size: "10000"
xpack.monitoring.enabled: "false"
xpack.security.enabled: "false"
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
restart: unless-stopped
volumes:
- c:/opt/trains/data/elastic:/usr/share/elasticsearch/data
ports:
- "9200:9200"
fileserver:
networks:
- backend
command:
- fileserver
container_name: trains-fileserver
image: allegroai/trains:latest
restart: unless-stopped
volumes:
- c:/opt/trains/logs:/var/log/trains
- c:/opt/trains/data/fileserver:/mnt/fileserver
ports:
- "8081:8081"
mongo:
networks:
- backend
container_name: trains-mongo
image: mongo:3.6.5
restart: unless-stopped
command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
volumes:
- mongodata:/data
ports:
- "27017:27017"
redis:
networks:
- backend
container_name: trains-redis
image: redis:5.0
restart: unless-stopped
volumes:
- c:/opt/trains/data/redis:/data
ports:
- "6379:6379"
webserver:
command:
- webserver
container_name: trains-webserver
image: allegroai/trains:latest
restart: unless-stopped
volumes:
- c:/trains/logs:/var/log/trains
depends_on:
- apiserver
ports:
- "8080:80"
networks:
backend:
driver: bridge
volumes:
mongodata:

View File

@@ -1,17 +1,32 @@
version: "3.6"
services:
apiserver:
command:
- apiserver
container_name: trains-apiserver
image: allegroai/trains:latest
network_mode: host
restart: always
restart: unless-stopped
volumes:
- type: bind
source: /opt/trains/logs
target: /var/log/trains
- /opt/trains/logs:/var/log/trains
- /opt/trains/config:/opt/trains/config
depends_on:
- redis
- mongo
- elasticsearch
- fileserver
environment:
ELASTIC_SERVICE_HOST: elasticsearch
MONGODB_SERVICE_HOST: mongo
REDIS_SERVICE_HOST: redis
ports:
- "8008:8008"
networks:
- backend
elasticsearch:
networks:
- backend
container_name: trains-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
@@ -30,47 +45,71 @@ services:
thread_pool.search.queue_size: "10000"
xpack.monitoring.enabled: "false"
xpack.security.enabled: "false"
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
network_mode: host
restart: always
restart: unless-stopped
volumes:
- type: bind
source: /opt/trains/data/elastic
target: /usr/share/elasticsearch/data
- /opt/trains/data/elastic:/usr/share/elasticsearch/data
ports:
- "9200:9200"
fileserver:
networks:
- backend
command:
- fileserver
container_name: trains-fileserver
image: allegroai/trains:latest
network_mode: host
restart: always
restart: unless-stopped
volumes:
- type: bind
source: /opt/trains/logs
target: /var/log/trains
- type: bind
source: /opt/trains/data/fileserver
target: /mnt/fileserver
- /opt/trains/logs:/var/log/trains
- /opt/trains/data/fileserver:/mnt/fileserver
ports:
- "8081:8081"
mongo:
networks:
- backend
container_name: trains-mongo
image: mongo:3.6.5
network_mode: host
restart: always
restart: unless-stopped
command: --setParameter internalQueryExecMaxBlockingSortBytes=196100200
volumes:
- type: bind
source: /opt/trains/data/mongo/db
target: /data/db
- type: bind
source: /opt/trains/data/mongo/configdb
target: /data/configdb
- /opt/trains/data/mongo/db:/data/db
- /opt/trains/data/mongo/configdb:/data/configdb
ports:
- "27017:27017"
redis:
networks:
- backend
container_name: trains-redis
image: redis:5.0
restart: unless-stopped
volumes:
- /opt/trains/data/redis:/data
ports:
- "6379:6379"
webserver:
command:
- webserver
container_name: trains-webserver
image: allegroai/trains:latest
network_mode: host
restart: always
restart: unless-stopped
volumes:
- type: bind
source: /opt/trains/logs
target: /var/log/trains
- /opt/trains/logs:/var/log/trains
depends_on:
- apiserver
ports:
- "8080:80"
networks:
backend:
driver: bridge

19
docs/apiserver.conf Normal file
View File

@@ -0,0 +1,19 @@
auth {
# Fixed users login credetials
# No other user will be able to login
fixed_users {
enabled: true
users: [
{
username: "jane"
password: "12345678"
name: "Jane Doe"
},
{
username: "john"
password: "12345678"
name: "John Doe"
},
]
}
}

166
docs/docker_setup.md Normal file
View File

@@ -0,0 +1,166 @@
# TRAINS-server: Using Docker Pre-Built Images
The pre-built Docker image for the **trains-server** is the quickest way to get started with your own **TRAINS** server.
You can also build the entire **trains-server** architecture using the code available in the [trains-server](https://github.com/allegroai/trains-server) repository.
**Note**: We tested this pre-built Docker image with Linux, only. For Windows users, we recommend installing the pre-built image on a Linux virtual machine.
## Prerequisites
* You must be logged in as a user with sudo privileges
* Use `bash` for all command-line instructions in this installation
## Setup Docker
### Step 1: Install Docker CE
You must first install Docker. For instructions about installing Docker, see [Supported platforms](https://docs.docker.com/install//#support) in the Docker documentation.
For example, to [install in Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) / Mint (x86_64/amd64):
```bash
sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
. /etc/os-release
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $UBUNTU_CODENAME stable"
sudo apt-get update
sudo apt-get install -y docker-ce
```
### Step 2: Set the Maximum Number of Memory Map Areas
Elastic requires that the `vm.max_map_count` kernel setting, which is the maximum number of memory map areas a process can use, is set to at least 262144.
For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19.x, we tested the following commands to set `vm.max_map_count`:
```bash
echo "vm.max_map_count=262144" > /tmp/99-trains.conf
sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
sudo sysctl -w vm.max_map_count=262144
```
For information about setting this parameter on other systems, see the [elastic](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode) documentation.
### Step 3: Restart the Docker daemon
Restart the Docker daemon.
```bash
sudo service docker restart
```
### Step 4: Choose a Data Directory
Choose a directory on your system in which all data maintained by the **trains-server** is stored.
Create this directory, and set its owner and group to `uid` 1000. The data stored in this directory includes the database, uploaded files and logs.
For example, if your data directory is `/opt/trains`, then use the following command:
```bash
sudo mkdir -p /opt/trains/data/elastic
sudo mkdir -p /opt/trains/data/mongo/db
sudo mkdir -p /opt/trains/data/mongo/configdb
sudo mkdir -p /opt/trains/data/redis
sudo mkdir -p /opt/trains/logs
sudo mkdir -p /opt/trains/data/fileserver
sudo mkdir -p /opt/trains/config
sudo chown -R 1000:1000 /opt/trains
```
## TRAINS-server: Manually Launching Docker Containers <a name="launch"></a>
You can manually launch the Docker containers using the following commands.
If your data directory is not `/opt/trains`, then in the five `docker run` commands below, you must replace all occurrences of `/opt/trains` with your data directory path.
1. Launch the **trains-elastic** Docker container.
sudo docker run -d --restart="always" --name="trains-elastic" -e "bootstrap.memory_lock=true" --ulimit memlock=-1:-1 -e "ES_JAVA_OPTS=-Xms2g -Xmx2g" -e "bootstrap.memory_lock=true" -e "cluster.name=trains" -e "discovery.zen.minimum_master_nodes=1" -e "node.name=trains" -e "script.inline=true" -e "script.update=true" -e "thread_pool.bulk.queue_size=2000" -e "thread_pool.search.queue_size=10000" -e "xpack.security.enabled=false" -e "xpack.monitoring.enabled=false" -e "cluster.routing.allocation.node_initial_primaries_recoveries=500" -e "node.ingest=true" -e "http.compression_level=7" -e "reindex.remote.whitelist=*.*" -e "script.painless.regex.enabled=true" --network="host" -v /opt/trains/data/elastic:/usr/share/elasticsearch/data docker.elastic.co/elasticsearch/elasticsearch:5.6.16
1. Launch the **trains-mongo** Docker container.
sudo docker run -d --restart="always" --name="trains-mongo" -v /opt/trains/data/mongo/db:/data/db -v /opt/trains/data/mongo/configdb:/data/configdb --network="host" mongo:3.6.5
1. Launch the **trains-redis** Docker container.
sudo docker run -d --restart="always" --name="trains-redis" -v /opt/trains/data/redis:/data --network="host" redis:5.0
1. Launch the **trains-fileserver** Docker container.
sudo docker run -d --restart="always" --name="trains-fileserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/data/fileserver:/mnt/fileserver allegroai/trains:latest fileserver
1. Launch the **trains-apiserver** Docker container.
sudo docker run -d --restart="always" --name="trains-apiserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/config:/opt/trains/config allegroai/trains:latest apiserver
1. Launch the **trains-webserver** Docker container.
sudo docker run -d --restart="always" --name="trains-webserver" -p 8080:80 allegroai/trains:latest webserver
1. Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
* API server on port `8008`
* Web server on port `8080`
* File server on port `8081`
## Manually Upgrading TRAINS-server Containers <a name="upgrade"></a>
We are constantly updating, improving and adding to the **trains-server**.
New releases will include new pre-built Docker images.
When we release a new version and include a new pre-built Docker image for it, upgrade as follows:
1. Shut down and remove each of your Docker instances using the following commands:
```bash
$ sudo docker stop <docker-name>
$ sudo docker rm -v <docker-name>
```
The Docker names are (see [Launching Docker Containers](#launch-docker)):
* `trains-elastic`
* `trains-mongo`
* `trains-redis`
* `trains-fileserver`
* `trains-apiserver`
* `trains-webserver`
2. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
For example, if your data directory is `/opt/trains`, use the following command:
```bash
$ sudo tar czvf ~/trains_backup.tgz /opt/trains/data
```
This backups all data to an archive in your home directory.
To restore this example backup, use the following command:
```bash
$ sudo rm -R /opt/trains/data
$ sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
```
3. Pull the new **trains-server** docker image using the following command:
```bash
$ sudo docker pull allegroai/trains:latest
```
If you wish to pull a different version, replace `latest` with the required version number, for example:
```bash
$ sudo docker pull allegroai/trains:0.11.0
```
4. Launch the newly released Docker image (see [Launching Docker Containers](#trains-server-manually-launching-docker-containers-)).
#### Common Docker Upgrade Errors
* In case of a docker error: "... The container name "/trains-???" is already in use by ..."
Try removing deprecated images with:
```bash
$ docker rm -f $(docker ps -a -q)
```

224
docs/faq.md Normal file
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@@ -0,0 +1,224 @@
# TRAINS-server FAQ
* [Deploying trains-server on Kubernetes clusters](#kubernetes)
* [Creating a Helm Chart for trains-server Kubernetes deployment](#helm)
* [Running trains-server on Mac OS X](#mac-osx)
* [Running trains-server on Windows 10](#docker_compose_win10)
* [Installing trains-server on stand alone Linux Ubuntu systems ](#ubuntu)
* [Resolving port conflicts preventing fixed users mode authentication and login](#port-conflict)
* [Configuring trains-server for sub-domains and load balancers](#sub-domains)
### Deploying trains-server on Kubernetes clusters <a name="kubernetes"></a>
**trains-server** supports Kubernetes. See [trains-server-k8s](https://github.com/allegroai/trains-server-k8s)
which contains the YAML files describing the required services and detailed instructions for deploying
**trains-server** to a Kubernetes clusters.
### Creating a Helm Chart for trains-server Kubernetes deployment <a name="helm"></a>
**trains-server** supports creating a Helm chart for Kubernetes deployment. See [trains-server-helm](https://github.com/allegroai/trains-server-helm)
which you can use to create a Helm chart for **trains-server** and contains detailed instructions for deploying
**trains-server** to a Kubernetes clusters using Helm.
### Running trains-server on Mac OS X <a name="mac-osx"></a>
To install and configure **trains-server** on Mac OS X, follow the steps below.
1. Install [docker for OS X](https://docs.docker.com/docker-for-mac/install/).
1. Configure [Docker](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode).
$ screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
$ sysctl -w vm.max_map_count=262144
1. Create local directories for the databases and storage.
$ sudo mkdir -p /opt/trains/data/elastic
$ sudo mkdir -p /opt/trains/data/mongo/db
$ sudo mkdir -p /opt/trains/data/mongo/configdb
$ sudo mkdir -p /opt/trains/data/redis
$ sudo mkdir -p /opt/trains/logs
$ sudo mkdir -p /opt/trains/config
$ sudo mkdir -p /opt/trains/data/fileserver
$ sudo chown -R $(whoami):staff /opt/trains
1. Open the Docker app, select **Preferences**, and then on the **File Sharing** tab, add `/opt/trains`.
1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
$ git clone https://github.com/allegroai/trains-server.git
$ cd trains-server
1. Run `docker-compose` with the unified docker image.
$ docker-compose -f docker-compose-unified.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### Running trains-server on Windows 10 <a name="docker_compose_win10"></a>
You can run **trains-server** on Windows 10 using Docker Desktop for Windows (see the Docker [System Requirements](https://docs.docker.com/docker-for-windows/install/#system-requirements)).
To run **trains-server** on Windows 10, follow the steps below.
1. Install the Docker Desktop for Windows application by either:
* Following the [Install Docker Desktop on Windows](https://docs.docker.com/docker-for-windows/install/) instructions.
* Running the Docker installation [wizard](https://hub.docker.com/?overlay=onboarding).
1. Increase the memory allocation in Docker Desktop to `4GB`.
1. In your Windows notification area (system tray), right click the Docker icon.
1. Click *Settings*, *Advanced*, and then set the memory to at least `4096`.
1. Click *Apply*.
1. Create local directories for data and logs. Open PowerShell and execute the following commands:
mkdir c:\opt\trains\logs
mkdir c:\opt\trains\config
mkdir c:\opt\trains\data
mkdir c:\opt\trains\data\elastic
mkdir c:\opt\trains\data\redis
mkdir c:\opt\trains\data\fileserver
1. Save the **trains-server** docker-compose YAML file [docker-compose-win10.yml](https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose-win10.yml) as `c:\opt\trains\docker-compose.yml`.
1. Run `docker-compose`. In PowerShell, execute the following commands:
cd c:\opt\trains\
docker-compose up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### Installing trains-server on stand alone Linux Ubuntu systems <a name="ubuntu"></a>
To install **trains-server** on a stand alone Linux Ubuntu, follow the steps belows.
1. Install [docker for Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
1. Install `docker-compose` using the following commands (for more detailed information, see the [Install Docker Compose](https://docs.docker.com/compose/install/) in the Docker documentation):
sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
1. Remove the previous installation of **trains-server**.
**WARNING**: This clears all existing **TRAINS** databases.
$ sudo rm -R /opt/trains/
1. Create local directories for the databases and storage.
$ sudo mkdir -p /opt/trains/data/elastic
$ sudo mkdir -p /opt/trains/data/mongo/db
$ sudo mkdir -p /opt/trains/data/mongo/configdb
$ sudo mkdir -p /opt/trains/logs
$ sudo mkdir -p /opt/trains/config
$ sudo mkdir -p /opt/trains/data/fileserver
$ sudo chown -R 1000:1000 /opt/trains
1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
$ git clone https://github.com/allegroai/trains-server.git
$ cd trains-server
1. Run `docker-compose`
$ /usr/local/bin/docker-compose -f docker-compose.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### Resolving port conflicts preventing fixed users mode authentication and login <a name="port-conflict"></a>
A port conflict may occur between the **trains-server** MongoDB and Elastic instances and other
instances running on your system. **trains-server** uses the following default ports which may be in conflict with other instances:
* MongoDB port `27017`
* Elastic port `9200`
You can check for port conflicts in the logs in `/opt/trains/log`.
If a port conflict occurs, first change the port in your **trains-server** `/opt/trains/server/config/default/hosts.conf` file to the new port and then
run the `docker run` command with the `port` option specifying the new port to restart the **trains-server** instance.
For example, to resolve a MongoDB port conflict change port `27017` to `27018`:
1. Modify `/opt/trains/server/config/default/hosts.conf` changing the ports in the `mongo` section:
elastic {
events {
hosts: [{host: "127.0.0.1", port: 9200}]
args {
timeout: 60
dead_timeout: 10
max_retries: 5
retry_on_timeout: true
}
index_version: "1"
}
}
mongo {
backend {
host: "mongodb://127.0.0.1:27018/backend"
}
auth {
host: "mongodb://127.0.0.1:27018/auth"
}
}
2. Start the **trains-server** MongoDB container using `--port 27018`.
sudo docker run -d --restart="always" --name="trains-mongo" -v /opt/trains/data/mongo/db:/data/db -v /opt/trains/data/mongo/configdb:/data/configdb --network="host" mongo:3.6.5 mongod --port 27018
In a future version of **trains-server**, to start the API server, environment variables will be available to use instead of modifying the configuration file (instead of Step 1 above).
The environment variables will be available to set different ports for both MongoDB and Elastic instances:
* `MONGODB_SERVICE_PORT` (e.g., `MONGODB_SERVICE_PORT=27018`)
* `ELASTIC_SERVICE_POST` (e.g., `ELASTIC_SERVICE_POST=9201`)
### Configuring trains-server for sub-domains and load balancers <a name="sub-domains"></a>
You can configure **trains-server** for sub-domains and a load balancer.
For example, if your domain is `trains.mydomain.com` and your sub-domains are `app` and `api`, then do the following:
1. If you are not using the current **trains-server** version, [upgrade](https://github.com/allegroai/trains-server#upgrade) **trains-server**.
1. Add the following to `/opt/trains/config/apiserver.conf`:
auth {
cookies {
httponly: true
secure: true
domain: ".trains.mydomain.com"
max_age: 99999999999
}
}
1. Use the following load balancer configuration:
* Listeners:
* Optional: HTTP listener, that redirects all traffic to HTTPS.
* HTTPS listener for `app.` forwarded to `AppTargetGroup`
* HTTPS listener for `api.` forwarded to `ApiTargetGroup`
* HTTPS listener for `files.` forwarded to `FilesTargetGroup`
* Target groups:
* `AppTargetGroup`: HTTP based target group, port `8080`
* `ApiTargetGroup`: HTTP based target group, port `8008`
* `FilesTargetGroup`: HTTP based target group, port `8081`
* Security and routing:
* Load balancer: make sure the load balancers are able to receive traffic from the relevant IP addresses (Security groups and Subnets definitions).
* Instances: make sure the load balancers are able to access the instances, using the relevant ports (Security groups definitions).
1. Run the Docker containers with our updated `docker run` commands (see [Launching Docker Containers](#https://github.com/allegroai/trains-server#launching-docker-containers)).

View File

@@ -21,10 +21,123 @@ The minimum recommended instance type is **t3a.large**
In order to upgrade **trains-server** on an existing EC2 instance based on one of these AMIs, SSH into the instance and follow the [upgrade instructions](../README.md#upgrade) for **trains-server**.
### Upgrading AMI's to v0.12
**Including the automatically updated AMI**
Version 0.12 introduced an additional REDIS docker to the trains-server setup.
AMI upgrading instructions:
1. SSH to the EC2 machine running one of the `Latest Version AMI's`
2. Execute the following bash commands
```bash
sudo bash
echo "" >> /usr/bin/start_or_update_server.sh
echo "sudo mkdir -p \${datadir}/redis" >> /usr/bin/start_or_update_server.sh
echo "sudo docker stop trains-redis || true && sudo docker rm -v trains-redis || true" >> /usr/bin/start_or_update_server.sh
echo "echo never | sudo tee -a /sys/kernel/mm/transparent_hugepage/enabled" >> /usr/bin/start_or_update_server.sh
echo "sudo sysctl vm.overcommit_memory=1" >> /usr/bin/start_or_update_server.sh
echo "sudo docker run -d --restart=always --name=trains-redis -v \${datadir}/redis:/data --network=host redis:5 redis-server" >> /usr/bin/start_or_update_server.sh
```
3. Reboot the EC2 machine
## Released versions
The following sections provide a list containing AMI Image ID per region for each released **trains-server** version.
### Latest Version AMI <a name="autoupdate"></a>
**For easier upgrades: The following AMI automatically update to the latest release every reboot**
* **eu-north-1** : ami-055909c1b9471451d
* **ap-south-1** : ami-0476123cc77226faf
* **eu-west-3** : ami-01df7d35ab63cca70
* **eu-west-2** : ami-00e8004c11fd0228e
* **eu-west-1** : ami-04293fbba6d3acad1
* **ap-northeast-2** : ami-004331f9c5eb13e94
* **ap-northeast-1** : ami-08cc80e2049b30e61
* **sa-east-1** : ami-06d814a0b6ffa3153
* **ca-central-1** : ami-069210ff757e9c1b7
* **ap-southeast-1** : ami-0d12cc70d6e9c0f39
* **ap-southeast-2** : ami-0b4615aa76c055267
* **eu-central-1** : ami-06537f431e52e4763
* **us-east-2** : ami-0c3cfbcb8e72ecfc5
* **us-west-1** : ami-0d83de031b83b6880
* **us-west-2** : ami-06968633c4f7187c4
* **us-east-1** : ami-07ff2f5f7ef99e8f6
### v0.12.1
* **eu-north-1** : ami-003118a8103286d84
* **ap-south-1** : ami-02dfe86baa48e096f
* **eu-west-3** : ami-0cc1f01267d2a780d
* **eu-west-2** : ami-0e4c8332e5ce09585
* **eu-west-1** : ami-03459a2f0b0a3b1ab
* **ap-northeast-2** : ami-08f6c2aed3a53f24c
* **ap-northeast-1** : ami-0b798eab95a7c5435
* **sa-east-1** : ami-0d3ee166c09f0d1b2
* **ca-central-1** : ami-00a758c56bd63acd5
* **ap-southeast-1** : ami-0be64d4988cd03fbb
* **ap-southeast-2** : ami-02087310d43a63f31
* **eu-central-1** : ami-097bbefeac0c74225
* **us-east-2** : ami-07eda256712b90f4d
* **us-west-1** : ami-02ef2b55cbd01c7df
* **us-west-2** : ami-037c6176ef4735360
* **us-east-1** : ami-08715c20c0e3f1c15
### v0.12.0
* **eu-north-1** : ami-03ff8ab48cd43e77e
* **ap-south-1** : ami-079c1a41ff836487c
* **eu-west-3** : ami-0121ef0398ae87ab0
* **eu-west-2** : ami-09f0f97654d8c79de
* **eu-west-1** : ami-0b7ba303f757bfcd9
* **ap-northeast-2** : ami-053f416517b5f40a6
* **ap-northeast-1** : ami-056dff06c698c2d9d
* **sa-east-1** : ami-017ab655119258639
* **ca-central-1** : ami-03bf5fa1d86ac97f6
* **ap-southeast-1** : ami-0e667958002b0360c
* **ap-southeast-2** : ami-091f1b69cb43b1933
* **eu-central-1** : ami-068ec2f0e98c26541
* **us-east-2** : ami-0524bbdc1b64ff83f
* **us-west-1** : ami-0b4facd7534e393c9
* **us-west-2** : ami-0018d5a7e58966848
* **us-east-1** : ami-08f24178fc14a84d2
### v0.11.0
* **eu-north-1** : ami-0cbe338f058018c97
* **ap-south-1** : ami-06d72ff894f7a5e5d
* **eu-west-3** : ami-00f2a45d67df2d2f3
* **eu-west-2** : ami-0627ae688f4533237
* **eu-west-1** : ami-00bf924ccb0354418
* **ap-northeast-2** : ami-0800edf1d1dec1da8
* **ap-northeast-1** : ami-07b2ed9709cdc4b15
* **sa-east-1** : ami-0012c1648618b812c
* **ca-central-1** : ami-02870b965d002fc8a
* **ap-southeast-1** : ami-068ec23abf2473192
* **ap-southeast-2** : ami-06664624728b5e01a
* **eu-central-1** : ami-05f2a9304f237a6f0
* **us-east-2** : ami-0ec242e6dca2b72b9
* **us-west-1** : ami-050b6577acf246ceb
* **us-west-2** : ami-0e384b6f78bf96ebe
* **us-east-1** : ami-0a7b46f907d5d9c4a
### v0.10.1
* **eu-north-1** : ami-09937ec4d18350c32
* **ap-south-1** : ami-089d6ba7541ec4c7f
* **eu-west-3** : ami-0accb1a94bdd5c5c1
* **eu-west-2** : ami-0dd2c97bc678b8570
* **eu-west-1** : ami-07a38865cbe7ca3cb
* **ap-northeast-2** : ami-09aa0b7fe1cf3dd55
* **ap-northeast-1** : ami-0905e7d1543e5ed36
* **sa-east-1** : ami-08c0627daa67d7372
* **ca-central-1** : ami-034add081712ff648
* **ap-southeast-1** : ami-0c6caee3689b6e066
* **ap-southeast-2** : ami-04994afd8dae5b417
* **eu-central-1** : ami-06b10f8c30e1434f1
* **us-east-2** : ami-0d3abe7a1fec535cc
* **us-west-1** : ami-02bb610b70c55018b
* **us-west-2** : ami-0d1cb8ba7de246ff0
* **us-east-1** : ami-049ccba6abdb40cba
### v0.10.0
* **eu-north-1** : ami-05ba33c763877e54e
* **ap-south-1** : ami-0529eec569161cae5

9
docs/services.conf Normal file
View File

@@ -0,0 +1,9 @@
tasks {
non_responsive_tasks_watchdog {
# In-progress tasks that haven't been updated for at least 'value' seconds will be stopped by the watchdog
threshold_sec: 7200
# Watchdog will sleep for this number of seconds after each cycle
watch_interval_sec: 900
}
}

View File

@@ -1,7 +1,7 @@
Server Side Public License
VERSION 1, OCTOBER 16, 2018
Copyright © 2018 MongoDB, Inc.
Copyright © 2019 allegro.ai, Inc.
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.

View File

@@ -0,0 +1,8 @@
download {
# Add response headers requesting no caching for served files
disable_browser_caching: false
}
cors {
origins: "*"
}

View File

@@ -1,16 +1,18 @@
""" A Simple file server for uploading and downloading files """
import json
import logging.config
import os
from argparse import ArgumentParser
from pathlib import Path
from flask import Flask, request, send_from_directory, safe_join
from pyhocon import ConfigFactory
from flask_compress import Compress
from flask_cors import CORS
logging.config.dictConfig(ConfigFactory.parse_file("logging.conf"))
from config import config
app = Flask(__name__)
CORS(app, **config.get("fileserver.cors"))
Compress(app)
@app.route("/", methods=["POST"])
@@ -29,7 +31,15 @@ def upload():
@app.route("/<path:path>", methods=["GET"])
def download(path):
return send_from_directory(app.config["UPLOAD_FOLDER"], path)
response = send_from_directory(app.config["UPLOAD_FOLDER"], path)
if config.get("fileserver.download.disable_browser_caching", False):
headers = response.headers
headers["Pragma-directive"] = "no-cache"
headers["Cache-directive"] = "no-cache"
headers["Cache-control"] = "no-cache"
headers["Pragma"] = "no-cache"
headers["Expires"] = "0"
return response
def main():

View File

@@ -1,2 +1,4 @@
Flask
Flask-Cors>=3.0.5
Flask-Compress>=1.4.0
pyhocon>=0.3.35

View File

@@ -0,0 +1,18 @@
from pymongo.database import Database, Collection
from database.utils import partition_tags
def migrate_backend(db: Database):
for name in ("project", "task", "model"):
collection: Collection = db[name]
for doc in collection.find(projection=["tags", "system_tags"]):
tags = doc.get("tags")
if tags is not None:
user_tags, system_tags = partition_tags(
name, tags, doc.get("system_tags", [])
)
collection.update_one(
{"_id": doc["_id"]},
{"$set": {"system_tags": system_tags, "tags": user_tags}}
)

View File

@@ -1,7 +1,7 @@
Server Side Public License
VERSION 1, OCTOBER 16, 2018
Copyright © 2018 MongoDB, Inc.
Copyright © 2019 allegro.ai, Inc.
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.

View File

@@ -48,7 +48,6 @@ _error_codes = {
129: ('task_publish_in_progress', 'Task publish in progress'),
130: ('task_not_found', 'task not found'),
# Models
200: ('model_error', 'general task error'),
201: ('invalid_model_id', 'invalid model id'),
@@ -70,9 +69,26 @@ _error_codes = {
403: ('project_not_found', 'project not found'),
405: ('project_has_models', 'project has associated models'),
# Queues
701: ('invalid_queue_id', 'invalid queue id'),
702: ('queue_not_empty', 'queue is not empty'),
703: ('invalid_queue_or_task_not_queued', 'invalid queue id or task not in queue'),
704: ('removed_during_reposition', 'task was removed by another party during reposition'),
705: ('failed_adding_during_reposition', 'failed adding task back to queue during reposition'),
706: ('task_already_queued', 'failed adding task to queue since task is already queued'),
707: ('no_default_queue', 'no queue is tagged as the default queue for this company'),
708: ('multiple_default_queues', 'more than one queue is tagged as the default queue for this company'),
# Database
800: ('data_validation_error', 'data validation error'),
801: ('expected_unique_data', 'value combination already exists'),
# Workers
1001: ('invalid_worker_id', 'invalid worker id'),
1002: ('worker_registration_failed', 'worker registration failed'),
1003: ('worker_registered', 'worker is already registered'),
1004: ('worker_not_registered', 'worker is not registered'),
1005: ('worker_stats_not_found', 'worker stats not found'),
},
(401, 'unauthorized'): {
@@ -83,7 +99,8 @@ _error_codes = {
21: ('bad_credentials', 'unauthorized (malformed credentials)'),
22: ('invalid_credentials', 'unauthorized (invalid credentials)'),
30: ('invalid_token', 'invalid token'),
31: ('blocked_token', 'token is blocked')
31: ('blocked_token', 'token is blocked'),
40: ('invalid_fixed_user', 'fixed user ID was not found')
},
(403, 'forbidden'): {

View File

@@ -4,11 +4,10 @@ from enum import Enum
from typing import Union, Type, Iterable
import jsonmodels.errors
import jsonmodels.validators
import six
import validators
from jsonmodels import fields
from jsonmodels.fields import _LazyType
from jsonmodels.fields import _LazyType, NotSet
from jsonmodels.models import Base as ModelBase
from jsonmodels.validators import Enum as EnumValidator
from luqum.parser import parser, ParseError
@@ -25,6 +24,12 @@ def make_default(field_cls, default_value):
class ListField(fields.ListField):
def __init__(self, items_types=None, *args, default=NotSet, **kwargs):
if default is not NotSet and callable(default):
default = default()
super(ListField, self).__init__(items_types, *args, default=default, **kwargs)
def _cast_value(self, value):
try:
return super(ListField, self)._cast_value(value)
@@ -144,6 +149,46 @@ class EnumField(fields.StringField):
return super().parse_value(value)
class ActualEnumField(fields.StringField):
@property
def types(self):
return (self.__enum,)
def __init__(
self,
enum_class: Type[Enum],
*args,
validators=None,
required=False,
default=None,
**kwargs
):
self.__enum = enum_class
# noinspection PyTypeChecker
choices = list(enum_class)
validator_cls = EnumValidator if required else NullableEnumValidator
validators = [*(validators or []), validator_cls(*choices)]
super().__init__(
default=default and self.parse_value(default),
*args,
required=required,
validators=validators,
**kwargs
)
def parse_value(self, value):
if value is None and not self.required:
return self.get_default_value()
try:
# noinspection PyArgumentList
return self.__enum(value)
except ValueError:
return value
def to_struct(self, value):
return super().to_struct(value.value)
class EmailField(fields.StringField):
def validate(self, value):
super().validate(value)
@@ -160,3 +205,12 @@ class DomainField(fields.StringField):
return
if validators.domain(value) is not True:
raise errors.bad_request.InvalidDomainName()
class StringEnum(Enum):
def __str__(self):
return self.value
# noinspection PyMethodParameters
def _generate_next_value_(name, start, count, last_values):
return name

View File

@@ -1,4 +1,4 @@
from jsonmodels.fields import IntField, StringField, BoolField, EmbeddedField
from jsonmodels.fields import IntField, StringField, BoolField, EmbeddedField, DateTimeField
from jsonmodels.models import Base
from jsonmodels.validators import Max, Enum
@@ -79,6 +79,7 @@ class Credentials(Base):
class CredentialsResponse(Credentials):
secret_key = StringField()
last_used = DateTimeField(default=None)
class CreateCredentialsResponse(Base):

View File

@@ -0,0 +1,20 @@
from typing import Sequence
from jsonmodels.fields import StringField
from jsonmodels.models import Base
from apimodels import ListField, IntField, ActualEnumField
from bll.event.scalar_key import ScalarKeyEnum
class HistogramRequestBase(Base):
samples: int = IntField(default=10000)
key: ScalarKeyEnum = ActualEnumField(ScalarKeyEnum, default=ScalarKeyEnum.iter)
class ScalarMetricsIterHistogramRequest(HistogramRequestBase):
task: str = StringField(required=True)
class MultiTaskScalarMetricsIterHistogramRequest(HistogramRequestBase):
tasks: Sequence[str] = ListField(items_types=str)

View File

@@ -11,6 +11,7 @@ class CreateModelRequest(models.Base):
uri = fields.StringField(required=True)
labels = DictField(value_types=string_types+(int,), required=True)
tags = ListField(items_types=string_types)
system_tags = ListField(items_types=string_types)
comment = fields.StringField()
public = fields.BoolField(default=False)
project = fields.StringField()

View File

@@ -0,0 +1,16 @@
from jsonmodels import models, fields
class ProjectReq(models.Base):
project = fields.StringField()
class GetHyperParamReq(ProjectReq):
page = fields.IntField(default=0)
page_size = fields.IntField(default=500)
class GetHyperParamResp(models.Base):
parameters = fields.ListField(str)
remaining = fields.IntField()
total = fields.IntField()

View File

@@ -0,0 +1,60 @@
from jsonmodels import validators
from jsonmodels.fields import StringField, IntField, BoolField, FloatField
from jsonmodels.models import Base
from apimodels import ListField
class GetDefaultResp(Base):
id = StringField(required=True)
name = StringField(required=True)
class CreateRequest(Base):
name = StringField(required=True)
tags = ListField(items_types=[str])
system_tags = ListField(items_types=[str])
class QueueRequest(Base):
queue = StringField(required=True)
class DeleteRequest(QueueRequest):
force = BoolField(default=False)
class UpdateRequest(QueueRequest):
name = StringField()
tags = ListField(items_types=[str])
system_tags = ListField(items_types=[str])
class TaskRequest(QueueRequest):
task = StringField(required=True)
class MoveTaskRequest(TaskRequest):
count = IntField(default=1)
class MoveTaskResponse(Base):
position = IntField()
class GetMetricsRequest(Base):
queue_ids = ListField([str])
from_date = FloatField(required=True, validators=validators.Min(0))
to_date = FloatField(required=True, validators=validators.Min(0))
interval = IntField(required=True, validators=validators.Min(1))
class QueueMetrics(Base):
queue = StringField()
dates = ListField(int)
avg_waiting_times = ListField([float, int])
queue_lengths = ListField(int)
class GetMetricsResponse(Base):
queues = ListField(QueueMetrics)

View File

@@ -0,0 +1,14 @@
from jsonmodels.fields import BoolField, DateTimeField, StringField
from jsonmodels.models import Base
class ReportStatsOptionRequest(Base):
enabled = BoolField(default=None, nullable=True)
class ReportStatsOptionResponse(Base):
supported = BoolField(default=True)
enabled = BoolField()
enabled_time = DateTimeField(nullable=True)
enabled_version = StringField(nullable=True)
enabled_user = StringField(nullable=True)

View File

@@ -13,8 +13,17 @@ class StartedResponse(UpdateResponse):
started = IntField()
class EnqueueResponse(UpdateResponse):
queued = IntField()
class DequeueResponse(UpdateResponse):
dequeued = IntField()
class ResetResponse(UpdateResponse):
deleted_indices = ListField(items_types=six.string_types)
dequeued = DictField()
frames = DictField()
events = DictField()
model_deleted = IntField()
@@ -30,6 +39,10 @@ class UpdateRequest(TaskRequest):
force = BoolField(default=False)
class EnqueueRequest(UpdateRequest):
queue = StringField()
class DeleteRequest(UpdateRequest):
move_to_trash = BoolField(default=True)
@@ -57,5 +70,5 @@ class CreateRequest(TaskData):
type = StringField(required=True, validators=Enum(*get_options(TaskType)))
class PingRequest(models.Base):
task = StringField(required=True)
class PingRequest(TaskRequest):
pass

183
server/apimodels/workers.py Normal file
View File

@@ -0,0 +1,183 @@
import json
from enum import Enum
import six
from jsonmodels import validators
from jsonmodels.fields import (
StringField,
EmbeddedField,
DateTimeField,
IntField,
FloatField,
BoolField,
)
from jsonmodels.models import Base
from apimodels import make_default, ListField, EnumField
DEFAULT_TIMEOUT = 10 * 60
class WorkerRequest(Base):
worker = StringField(required=True)
class RegisterRequest(WorkerRequest):
timeout = make_default(
IntField, DEFAULT_TIMEOUT
)() # registration timeout in seconds (default is 10min)
queues = ListField(six.string_types) # list of queues this worker listens to
class MachineStats(Base):
cpu_usage = ListField(six.integer_types + (float,))
cpu_temperature = ListField(six.integer_types + (float,))
gpu_usage = ListField(six.integer_types + (float,))
gpu_temperature = ListField(six.integer_types + (float,))
gpu_memory_free = ListField(six.integer_types + (float,))
gpu_memory_used = ListField(six.integer_types + (float,))
memory_used = FloatField()
memory_free = FloatField()
network_tx = FloatField()
network_rx = FloatField()
disk_free_home = FloatField()
disk_free_temp = FloatField()
disk_read = FloatField()
disk_write = FloatField()
class StatusReportRequest(WorkerRequest):
task = StringField() # task the worker is running on
queue = StringField() # queue from which task was taken
queues = ListField(
str
) # list of queues this worker listens to. if None, this will not update the worker's queues list.
timestamp = IntField(required=True)
machine_stats = EmbeddedField(MachineStats)
class IdNameEntry(Base):
id = StringField(required=True)
name = StringField()
class WorkerEntry(Base):
key = StringField() # not required due to migration issues
id = StringField(required=True)
user = EmbeddedField(IdNameEntry)
company = EmbeddedField(IdNameEntry)
ip = StringField()
task = EmbeddedField(IdNameEntry)
queue = StringField() # queue from which current task was taken
queues = ListField(str) # list of queues this worker listens to
register_time = DateTimeField(required=True)
register_timeout = IntField(required=True)
last_activity_time = DateTimeField(required=True)
last_report_time = DateTimeField()
def to_json(self):
return json.dumps(self.to_struct())
@classmethod
def from_json(cls, s):
return cls(**json.loads(s))
class CurrentTaskEntry(IdNameEntry):
running_time = IntField()
last_iteration = IntField()
class QueueEntry(IdNameEntry):
next_task = EmbeddedField(IdNameEntry)
num_tasks = IntField()
class WorkerResponseEntry(WorkerEntry):
task = EmbeddedField(CurrentTaskEntry)
queue = EmbeddedField(QueueEntry)
queues = ListField(QueueEntry)
class GetAllRequest(Base):
last_seen = IntField(default=3600)
class GetAllResponse(Base):
workers = ListField(WorkerResponseEntry)
class StatsBase(Base):
worker_ids = ListField(str)
class StatsReportBase(StatsBase):
from_date = FloatField(required=True, validators=validators.Min(0))
to_date = FloatField(required=True, validators=validators.Min(0))
interval = IntField(required=True, validators=validators.Min(1))
class AggregationType(Enum):
avg = "avg"
min = "min"
max = "max"
class StatItem(Base):
key = StringField(required=True)
aggregation = EnumField(AggregationType, default=AggregationType.avg)
class GetStatsRequest(StatsReportBase):
items = ListField(
StatItem, required=True, validators=validators.Length(minimum_value=1)
)
split_by_variant = BoolField(default=False)
class AggregationStats(Base):
aggregation = EnumField(AggregationType)
values = ListField(float)
class MetricStats(Base):
metric = StringField()
variant = StringField()
dates = ListField(int)
stats = ListField(AggregationStats)
class WorkerStatistics(Base):
worker = StringField()
metrics = ListField(MetricStats)
class GetStatsResponse(Base):
workers = ListField(WorkerStatistics)
class GetMetricKeysRequest(StatsBase):
pass
class MetricCategory(Base):
name = StringField()
metric_keys = ListField(str)
class GetMetricKeysResponse(Base):
categories = ListField(MetricCategory)
class GetActivityReportRequest(StatsReportBase):
pass
class ActivityReportSeries(Base):
dates = ListField(int)
counts = ListField(int)
class GetActivityReportResponse(Base):
total = EmbeddedField(ActivityReportSeries)
active = EmbeddedField(ActivityReportSeries)

View File

@@ -1,24 +1,25 @@
from collections import defaultdict
from contextlib import closing
from datetime import datetime
from enum import Enum
from operator import attrgetter
from typing import Sequence
import attr
import six
from elasticsearch import helpers
from enum import Enum
from mongoengine import Q
from nested_dict import nested_dict
import database.utils as dbutils
import es_factory
from apierrors import errors
from bll.event.event_metrics import EventMetrics
from bll.task import TaskBLL
from database.errors import translate_errors_context
from database.model.task.task import Task
from database.model.task.metrics import MetricEvent
from database.model.task.task import Task, TaskStatus
from timing_context import TimingContext
from utilities.dicts import flatten_nested_items
class EventType(Enum):
@@ -33,6 +34,9 @@ class EventType(Enum):
EVENT_TYPES = set(map(attrgetter("value"), EventType))
LOCKED_TASK_STATUSES = (TaskStatus.publishing, TaskStatus.published)
@attr.s
class TaskEventsResult(object):
events = attr.ib(type=list, default=attr.Factory(list))
@@ -44,9 +48,14 @@ class EventBLL(object):
id_fields = ["task", "iter", "metric", "variant", "key"]
def __init__(self, events_es=None):
self.es = events_es if events_es is not None else es_factory.connect("events")
self.es = events_es or es_factory.connect("events")
self._metrics = EventMetrics(self.es)
def add_events(self, company_id, events, worker):
@property
def metrics(self) -> EventMetrics:
return self._metrics
def add_events(self, company_id, events, worker, allow_locked_tasks=False):
actions = []
task_ids = set()
task_iteration = defaultdict(lambda: 0)
@@ -94,7 +103,7 @@ class EventBLL(object):
event["value"] = event["values"]
del event["values"]
index_name = EventBLL.get_index_name(company_id, event_type)
index_name = EventMetrics.get_index_name(company_id, event_type)
es_action = {
"_op_type": "index", # overwrite if exists with same ID
"_index": index_name,
@@ -127,11 +136,16 @@ class EventBLL(object):
if task_ids:
# verify task_ids
with translate_errors_context(), TimingContext("mongo", "task_by_ids"):
res = Task.objects(id__in=task_ids, company=company_id).only("id")
extra_msg = None
query = Q(id__in=task_ids, company=company_id)
if not allow_locked_tasks:
query &= Q(status__nin=LOCKED_TASK_STATUSES)
extra_msg = "or task published"
res = Task.objects(query).only("id")
if len(res) < len(task_ids):
invalid_task_ids = tuple(set(task_ids) - set(r.id for r in res))
raise errors.bad_request.InvalidTaskId(
company=company_id, ids=invalid_task_ids
extra_msg, company=company_id, ids=invalid_task_ids
)
errors_in_bulk = []
@@ -154,13 +168,6 @@ class EventBLL(object):
else:
errors_in_bulk.append(info)
last_metrics = {
t.id: t.to_proper_dict().get("last_metrics", {})
for t in Task.objects(id__in=task_ids, company=company_id).only(
"last_metrics"
)
}
remaining_tasks = set()
now = datetime.utcnow()
for task_id in task_ids:
@@ -171,9 +178,8 @@ class EventBLL(object):
company_id=company_id,
task_id=task_id,
now=now,
iter=task_iteration.get(task_id),
iter_max=task_iteration.get(task_id),
last_events=task_last_events.get(task_id),
last_metrics=last_metrics.get(task_id),
)
if not updated:
@@ -210,9 +216,7 @@ class EventBLL(object):
if timestamp is None or timestamp < event["timestamp"]:
last_events[metric_hash][variant_hash] = event
def _update_task(
self, company_id, task_id, now, iter=None, last_events=None, last_metrics=None
):
def _update_task(self, company_id, task_id, now, iter_max=None, last_events=None):
"""
Update task information in DB with aggregated results after handling event(s) related to this task.
@@ -222,27 +226,17 @@ class EventBLL(object):
"""
fields = {}
if iter is not None:
fields["last_iteration"] = iter
if iter_max is not None:
fields["last_iteration_max"] = iter_max
if last_events:
def get_metric_event(ev):
me = MetricEvent.from_dict(**ev)
if "timestamp" in ev:
me.timestamp = datetime.utcfromtimestamp(ev["timestamp"] / 1000)
return me
new_last_metrics = nested_dict(2, MetricEvent)
new_last_metrics.update(last_metrics)
for metric_hash, variants in last_events.items():
for variant_hash, event in variants.items():
new_last_metrics[metric_hash][variant_hash] = get_metric_event(
event
)
fields["last_metrics"] = new_last_metrics.to_dict()
fields["last_values"] = list(
flatten_nested_items(
last_events,
nesting=2,
include_leaves=["value", "metric", "variant"],
)
)
if not fields:
return False
@@ -270,7 +264,7 @@ class EventBLL(object):
if event_type is None:
event_type = "*"
es_index = EventBLL.get_index_name(company_id, event_type)
es_index = EventMetrics.get_index_name(company_id, event_type)
if not self.es.indices.exists(es_index):
return [], None, 0
@@ -290,6 +284,125 @@ class EventBLL(object):
return events, next_scroll_id, total_events
def get_last_iterations_per_event_metric_variant(
self, es_index: str, task_id: str, num_last_iterations: int, event_type: str
):
if not self.es.indices.exists(es_index):
return []
es_req: dict = {
"size": 0,
"aggs": {
"metrics": {
"terms": {"field": "metric"},
"aggs": {
"variants": {
"terms": {"field": "variant"},
"aggs": {
"iters": {
"terms": {
"field": "iter",
"size": num_last_iterations,
"order": {"_term": "desc"},
}
}
},
}
},
}
},
"query": {"bool": {"must": [{"term": {"task": task_id}}]}},
}
if event_type:
es_req["query"]["bool"]["must"].append({"term": {"type": event_type}})
with translate_errors_context(), TimingContext(
"es", "task_last_iter_metric_variant"
):
es_res = self.es.search(index=es_index, body=es_req, routing=task_id)
if "aggregations" not in es_res:
return []
return [
(metric["key"], variant["key"], iter["key"])
for metric in es_res["aggregations"]["metrics"]["buckets"]
for variant in metric["variants"]["buckets"]
for iter in variant["iters"]["buckets"]
]
def get_task_plots(
self,
company_id: str,
tasks: Sequence[str],
last_iterations_per_plot: int = None,
sort=None,
size: int = 500,
scroll_id: str = None,
):
if scroll_id:
with translate_errors_context(), TimingContext("es", "get_task_events"):
es_res = self.es.scroll(scroll_id=scroll_id, scroll="1h")
else:
event_type = "plot"
es_index = EventMetrics.get_index_name(company_id, event_type)
if not self.es.indices.exists(es_index):
return TaskEventsResult()
query = {"bool": defaultdict(list)}
if last_iterations_per_plot is None:
must = query["bool"]["must"]
must.append({"terms": {"task": tasks}})
else:
should = query["bool"]["should"]
for i, task_id in enumerate(tasks):
last_iters = self.get_last_iterations_per_event_metric_variant(
es_index, task_id, last_iterations_per_plot, event_type
)
if not last_iters:
continue
for metric, variant, iter in last_iters:
should.append(
{
"bool": {
"must": [
{"term": {"task": task_id}},
{"term": {"metric": metric}},
{"term": {"variant": variant}},
{"term": {"iter": iter}},
]
}
}
)
if not should:
return TaskEventsResult()
if sort is None:
sort = [{"timestamp": {"order": "asc"}}]
es_req = {"sort": sort, "size": min(size, 10000), "query": query}
routing = ",".join(tasks)
with translate_errors_context(), TimingContext("es", "get_task_plots"):
es_res = self.es.search(
index=es_index,
body=es_req,
ignore=404,
routing=routing,
scroll="1h",
)
events = [doc["_source"] for doc in es_res.get("hits", {}).get("hits", [])]
# scroll id may be missing when queering a totally empty DB
next_scroll_id = es_res.get("_scroll_id")
total_events = es_res["hits"]["total"]
return TaskEventsResult(
events=events, next_scroll_id=next_scroll_id, total_events=total_events
)
def get_task_events(
self,
company_id,
@@ -311,7 +424,7 @@ class EventBLL(object):
if event_type is None:
event_type = "*"
es_index = EventBLL.get_index_name(company_id, event_type)
es_index = EventMetrics.get_index_name(company_id, event_type)
if not self.es.indices.exists(es_index):
return TaskEventsResult()
@@ -374,7 +487,7 @@ class EventBLL(object):
def get_metrics_and_variants(self, company_id, task_id, event_type):
es_index = EventBLL.get_index_name(company_id, event_type)
es_index = EventMetrics.get_index_name(company_id, event_type)
if not self.es.indices.exists(es_index):
return {}
@@ -405,7 +518,7 @@ class EventBLL(object):
return metrics
def get_task_latest_scalar_values(self, company_id, task_id):
es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
es_index = EventMetrics.get_index_name(company_id, "training_stats_scalar")
if not self.es.indices.exists(es_index):
return {}
@@ -488,147 +601,9 @@ class EventBLL(object):
metrics.append(metric_summary)
return metrics, max_timestamp
def compare_scalar_metrics_average_per_iter(
self, company_id, task_ids, allow_public=True
):
assert isinstance(task_ids, list)
task_name_by_id = {}
with translate_errors_context():
task_objs = Task.get_many(
company=company_id,
query=Q(id__in=task_ids),
allow_public=allow_public,
override_projection=("id", "name"),
return_dicts=False,
)
if len(task_objs) < len(task_ids):
invalid = tuple(set(task_ids) - set(r.id for r in task_objs))
raise errors.bad_request.InvalidTaskId(company=company_id, ids=invalid)
task_name_by_id = {t.id: t.name for t in task_objs}
es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
if not self.es.indices.exists(es_index):
return {}
es_req = {
"size": 0,
"_source": {"excludes": []},
"query": {"terms": {"task": task_ids}},
"aggs": {
"iters": {
"histogram": {"field": "iter", "interval": 1, "min_doc_count": 1},
"aggs": {
"metric_and_variant": {
"terms": {
"script": "doc['metric'].value +'/'+ doc['variant'].value",
"size": 10000,
},
"aggs": {
"tasks": {
"terms": {"field": "task"},
"aggs": {"avg_val": {"avg": {"field": "value"}}},
}
},
}
},
}
},
}
with translate_errors_context(), TimingContext("es", "task_stats_comparison"):
es_res = self.es.search(index=es_index, body=es_req)
if "aggregations" not in es_res:
return
metrics = {}
for iter_bucket in es_res["aggregations"]["iters"]["buckets"]:
iteration = int(iter_bucket["key"])
for metric_bucket in iter_bucket["metric_and_variant"]["buckets"]:
metric_name = metric_bucket["key"]
if metrics.get(metric_name) is None:
metrics[metric_name] = {}
metric_data = metrics[metric_name]
for task_bucket in metric_bucket["tasks"]["buckets"]:
task_id = task_bucket["key"]
value = task_bucket["avg_val"]["value"]
if metric_data.get(task_id) is None:
metric_data[task_id] = {
"x": [],
"y": [],
"name": task_name_by_id[
task_id
], # todo: lookup task name from id
}
metric_data[task_id]["x"].append(iteration)
metric_data[task_id]["y"].append(value)
return metrics
def get_scalar_metrics_average_per_iter(self, company_id, task_id):
es_index = EventBLL.get_index_name(company_id, "training_stats_scalar")
if not self.es.indices.exists(es_index):
return {}
es_req = {
"size": 0,
"_source": {"excludes": []},
"query": {"term": {"task": task_id}},
"aggs": {
"iters": {
"histogram": {"field": "iter", "interval": 1, "min_doc_count": 1},
"aggs": {
"metrics": {
"terms": {
"field": "metric",
"size": 200,
"order": {"_term": "desc"},
},
"aggs": {
"variants": {
"terms": {
"field": "variant",
"size": 500,
"order": {"_term": "desc"},
},
"aggs": {"avg_val": {"avg": {"field": "value"}}},
}
},
}
},
}
},
"version": True,
}
with translate_errors_context(), TimingContext("es", "task_stats_scalar"):
es_res = self.es.search(index=es_index, body=es_req, routing=task_id)
metrics = {}
if "aggregations" in es_res:
for iter_bucket in es_res["aggregations"]["iters"]["buckets"]:
iteration = int(iter_bucket["key"])
for metric_bucket in iter_bucket["metrics"]["buckets"]:
metric_name = metric_bucket["key"]
if metrics.get(metric_name) is None:
metrics[metric_name] = {}
metric_data = metrics[metric_name]
for variant_bucket in metric_bucket["variants"]["buckets"]:
variant = variant_bucket["key"]
value = variant_bucket["avg_val"]["value"]
if metric_data.get(variant) is None:
metric_data[variant] = {"x": [], "y": [], "name": variant}
metric_data[variant]["x"].append(iteration)
metric_data[variant]["y"].append(value)
return metrics
def get_vector_metrics_per_iter(self, company_id, task_id, metric, variant):
es_index = EventBLL.get_index_name(company_id, "training_stats_vector")
es_index = EventMetrics.get_index_name(company_id, "training_stats_vector")
if not self.es.indices.exists(es_index):
return [], []
@@ -684,8 +659,20 @@ class EventBLL(object):
return [b["key"] for b in es_res["aggregations"]["iters"]["buckets"]]
def delete_task_events(self, company_id, task_id):
es_index = EventBLL.get_index_name(company_id, "*")
def delete_task_events(self, company_id, task_id, allow_locked=False):
with translate_errors_context():
extra_msg = None
query = Q(id=task_id, company=company_id)
if not allow_locked:
query &= Q(status__nin=LOCKED_TASK_STATUSES)
extra_msg = "or task published"
res = Task.objects(query).only("id").first()
if not res:
raise errors.bad_request.InvalidTaskId(
extra_msg, company=company_id, id=task_id
)
es_index = EventMetrics.get_index_name(company_id, "*")
es_req = {"query": {"term": {"task": task_id}}}
with translate_errors_context(), TimingContext("es", "delete_task_events"):
es_res = self.es.delete_by_query(
@@ -693,8 +680,3 @@ class EventBLL(object):
)
return es_res.get("deleted", 0)
@staticmethod
def get_index_name(company_id, event_type):
event_type = event_type.lower().replace(" ", "_")
return "events-%s-%s" % (event_type, company_id)

View File

@@ -0,0 +1,398 @@
import itertools
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from operator import itemgetter
from elasticsearch import Elasticsearch
from typing import Sequence, Tuple, Callable
from mongoengine import Q
from apierrors import errors
from bll.event.scalar_key import ScalarKey, ScalarKeyEnum
from config import config
from database.errors import translate_errors_context
from database.model.task.task import Task
from timing_context import TimingContext
from utilities import safe_get
log = config.logger(__file__)
class EventMetrics:
MAX_TASKS_COUNT = 100
MAX_METRICS_COUNT = 200
MAX_VARIANTS_COUNT = 500
def __init__(self, es: Elasticsearch):
self.es = es
@staticmethod
def get_index_name(company_id, event_type):
event_type = event_type.lower().replace(" ", "_")
return f"events-{event_type}-{company_id}"
def get_scalar_metrics_average_per_iter(
self, company_id: str, task_id: str, samples: int, key: ScalarKeyEnum
) -> dict:
"""
Get scalar metric histogram per metric and variant
The amount of points in each histogram should not exceed
the requested samples
"""
return self._run_get_scalar_metrics_as_parallel(
company_id,
task_ids=[task_id],
samples=samples,
key=ScalarKey.resolve(key),
get_func=self._get_scalar_average,
)
def compare_scalar_metrics_average_per_iter(
self,
company_id,
task_ids: Sequence[str],
samples,
key: ScalarKeyEnum,
allow_public=True,
):
"""
Compare scalar metrics for different tasks per metric and variant
The amount of points in each histogram should not exceed the requested samples
"""
task_name_by_id = {}
with translate_errors_context():
task_objs = Task.get_many(
company=company_id,
query=Q(id__in=task_ids),
allow_public=allow_public,
override_projection=("id", "name"),
return_dicts=False,
)
if len(task_objs) < len(task_ids):
invalid = tuple(set(task_ids) - set(r.id for r in task_objs))
raise errors.bad_request.InvalidTaskId(company=company_id, ids=invalid)
task_name_by_id = {t.id: t.name for t in task_objs}
ret = self._run_get_scalar_metrics_as_parallel(
company_id,
task_ids=task_ids,
samples=samples,
key=ScalarKey.resolve(key),
get_func=self._get_scalar_average_per_task,
)
for metric_data in ret.values():
for variant_data in metric_data.values():
for task_id, task_data in variant_data.items():
task_data["name"] = task_name_by_id[task_id]
return ret
TaskMetric = Tuple[str, str, str]
MetricInterval = Tuple[int, Sequence[TaskMetric]]
MetricData = Tuple[str, dict]
def _run_get_scalar_metrics_as_parallel(
self,
company_id: str,
task_ids: Sequence[str],
samples: int,
key: ScalarKey,
get_func: Callable[
[MetricInterval, Sequence[str], str, ScalarKey], Sequence[MetricData]
],
) -> dict:
"""
Group metrics per interval length and execute get_func for each group in parallel
:param company_id: id of the company
:params task_ids: ids of the tasks to collect data for
:param samples: maximum number of samples per metric
:param get_func: callable that given metric names for the same interval
performs histogram aggregation for the metrics and return the aggregated data
"""
es_index = self.get_index_name(company_id, "training_stats_scalar")
if not self.es.indices.exists(es_index):
return {}
intervals = self._get_metric_intervals(
es_index=es_index, task_ids=task_ids, samples=samples, field=key.field
)
if not intervals:
return {}
with ThreadPoolExecutor(len(intervals)) as pool:
metrics = list(
itertools.chain.from_iterable(
pool.map(
partial(
get_func, task_ids=task_ids, es_index=es_index, key=key
),
intervals,
)
)
)
ret = defaultdict(dict)
for metric_key, metric_values in metrics:
ret[metric_key].update(metric_values)
return ret
def _get_metric_intervals(
self, es_index, task_ids: Sequence[str], samples: int, field: str = "iter"
) -> Sequence[MetricInterval]:
"""
Calculate interval per task metric variant so that the resulting
amount of points does not exceed sample.
Return metric variants grouped by interval value with 10% rounding
For samples==0 return empty list
"""
default_intervals = [(1, [])]
if not samples:
return default_intervals
es_req = {
"size": 0,
"query": {"terms": {"task": task_ids}},
"aggs": {
"tasks": {
"terms": {"field": "task", "size": self.MAX_TASKS_COUNT},
"aggs": {
"metrics": {
"terms": {
"field": "metric",
"size": self.MAX_METRICS_COUNT,
},
"aggs": {
"variants": {
"terms": {
"field": "variant",
"size": self.MAX_VARIANTS_COUNT,
},
"aggs": {
"count": {"value_count": {"field": field}},
"min_index": {"min": {"field": field}},
"max_index": {"max": {"field": field}},
},
}
},
}
},
}
},
}
with translate_errors_context(), TimingContext("es", "task_stats_get_interval"):
es_res = self.es.search(
index=es_index, body=es_req, routing=",".join(task_ids)
)
aggs_result = es_res.get("aggregations")
if not aggs_result:
return default_intervals
intervals = [
(
task["key"],
metric["key"],
variant["key"],
self._calculate_metric_interval(variant, samples),
)
for task in aggs_result["tasks"]["buckets"]
for metric in task["metrics"]["buckets"]
for variant in metric["variants"]["buckets"]
]
metric_intervals = []
upper_border = 0
interval_metrics = None
for task, metric, variant, interval in sorted(intervals, key=itemgetter(3)):
if not interval_metrics or interval > upper_border:
interval_metrics = []
metric_intervals.append((interval, interval_metrics))
upper_border = interval + int(interval * 0.1)
interval_metrics.append((task, metric, variant))
return metric_intervals
@staticmethod
def _calculate_metric_interval(metric_variant: dict, samples: int) -> int:
"""
Calculate index interval per metric_variant variant so that the
total amount of intervals does not exceeds the samples
"""
count = safe_get(metric_variant, "count/value")
if not count or count < samples:
return 1
min_index = safe_get(metric_variant, "min_index/value", default=0)
max_index = safe_get(metric_variant, "max_index/value", default=min_index)
return max(1, int(max_index - min_index + 1) // samples)
def _get_scalar_average(
self,
metrics_interval: MetricInterval,
task_ids: Sequence[str],
es_index: str,
key: ScalarKey,
) -> Sequence[MetricData]:
"""
Retrieve scalar histograms per several metric variants that share the same interval
Note: the function works with a single task only
"""
assert len(task_ids) == 1
interval, task_metrics = metrics_interval
aggregation = self._add_aggregation_average(key.get_aggregation(interval))
aggs = {
"metrics": {
"terms": {
"field": "metric",
"size": self.MAX_METRICS_COUNT,
"order": {"_term": "desc"},
},
"aggs": {
"variants": {
"terms": {
"field": "variant",
"size": self.MAX_VARIANTS_COUNT,
"order": {"_term": "desc"},
},
"aggs": aggregation,
}
},
}
}
aggs_result = self._query_aggregation_for_metrics_and_tasks(
es_index, aggs=aggs, task_ids=task_ids, task_metrics=task_metrics
)
if not aggs_result:
return {}
metrics = [
(
metric["key"],
{
variant["key"]: {
"name": variant["key"],
**key.get_iterations_data(variant),
}
for variant in metric["variants"]["buckets"]
},
)
for metric in aggs_result["metrics"]["buckets"]
]
return metrics
def _get_scalar_average_per_task(
self,
metrics_interval: MetricInterval,
task_ids: Sequence[str],
es_index: str,
key: ScalarKey,
) -> Sequence[MetricData]:
"""
Retrieve scalar histograms per several metric variants that share the same interval
"""
interval, task_metrics = metrics_interval
aggregation = self._add_aggregation_average(key.get_aggregation(interval))
aggs = {
"metrics": {
"terms": {"field": "metric", "size": self.MAX_METRICS_COUNT},
"aggs": {
"variants": {
"terms": {"field": "variant", "size": self.MAX_VARIANTS_COUNT},
"aggs": {
"tasks": {"terms": {"field": "task"}, "aggs": aggregation}
},
}
},
}
}
aggs_result = self._query_aggregation_for_metrics_and_tasks(
es_index, aggs=aggs, task_ids=task_ids, task_metrics=task_metrics
)
if not aggs_result:
return {}
metrics = [
(
metric["key"],
{
variant["key"]: {
task["key"]: key.get_iterations_data(task)
for task in variant["tasks"]["buckets"]
}
for variant in metric["variants"]["buckets"]
},
)
for metric in aggs_result["metrics"]["buckets"]
]
return metrics
@staticmethod
def _add_aggregation_average(aggregation):
average_agg = {"avg_val": {"avg": {"field": "value"}}}
return {
key: {**value, "aggs": {**value.get("aggs", {}), **average_agg}}
for key, value in aggregation.items()
}
def _query_aggregation_for_metrics_and_tasks(
self,
es_index: str,
aggs: dict,
task_ids: Sequence[str],
task_metrics: Sequence[TaskMetric],
) -> dict:
"""
Return the result of elastic search query for the given aggregation filtered
by the given task_ids and metrics
"""
if task_metrics:
condition = {
"should": [
self._build_metric_terms(task, metric, variant)
for task, metric, variant in task_metrics
]
}
else:
condition = {"must": [{"terms": {"task": task_ids}}]}
es_req = {
"size": 0,
"_source": {"excludes": []},
"query": {"bool": condition},
"aggs": aggs,
"version": True,
}
with translate_errors_context(), TimingContext("es", "task_stats_scalar"):
es_res = self.es.search(
index=es_index, body=es_req, routing=",".join(task_ids)
)
return es_res.get("aggregations")
@staticmethod
def _build_metric_terms(task: str, metric: str, variant: str) -> dict:
"""
Build query term for a metric + variant
"""
return {
"bool": {
"must": [
{"term": {"task": task}},
{"term": {"metric": metric}},
{"term": {"variant": variant}},
]
}
}

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"""
Module for polymorphism over different types of X axes in scalar aggregations
"""
from abc import ABC, abstractmethod
from enum import auto
from apimodels import StringEnum
from bll.util import extract_properties_to_lists
from config import config
log = config.logger(__file__)
class ScalarKeyEnum(StringEnum):
"""
String enum representing X axes key
"""
iter = auto()
timestamp = auto()
iso_time = auto()
class ScalarKey(ABC):
"""
Abstract scalar key
"""
_enum_to_key = {}
bucket_key_key = "key"
@property
@abstractmethod
def enum_value(self) -> ScalarKeyEnum:
"""
Enum value accepted in API requests
"""
pass
@property
@abstractmethod
def name(self) -> str:
"""
Key name. Used as arbitrary internal key in elasticsearch queries
"""
pass
@property
@abstractmethod
def field(self) -> str:
"""
Event key to aggregate by
"""
pass
@abstractmethod
def get_aggregation(self, interval: int) -> dict:
"""
Get aggregation for this type of key
:param interval: elasticsearch aggregation interval
"""
pass
def __init_subclass__(cls, **kwargs):
"""
Save a mapping from enum values to key class
"""
if cls.enum_value not in ScalarKeyEnum:
raise ValueError(f"{cls.enum_value!r} not in {ScalarKeyEnum.__name__}")
if cls.enum_value in cls._enum_to_key:
log.warning(
f"'{cls.enum_value.value}' is already registered to {ScalarKey.__name__}"
)
cls._enum_to_key[cls.enum_value] = cls
@classmethod
def resolve(cls, key: ScalarKeyEnum):
"""
Create a key instance from enum instance
"""
return cls._enum_to_key[key]()
def get_iterations_data(self, iter_buckets: dict) -> dict:
"""
Convert a list of bucket entries to `x`s array and `y`s array
"""
return extract_properties_to_lists(
("x", "y"),
iter_buckets[self.name]["buckets"],
self._get_iterations_data_single,
)
def _get_iterations_data_single(self, iter_data):
"""
Extract x value and y value from a single bucket item
"""
return int(iter_data[self.bucket_key_key]), iter_data["avg_val"]["value"]
class TimestampKey(ScalarKey):
"""
Aggregate by timestamp in milliseconds since epoch
"""
name = "timestamp"
field = "timestamp"
enum_value = ScalarKeyEnum.timestamp
def get_aggregation(self, interval: int) -> dict:
return {
self.name: {
"date_histogram": {
"field": "timestamp",
"interval": interval,
"min_doc_count": 1,
}
}
}
class IterKey(ScalarKey):
"""
Aggregate by iteration number
"""
name = "iters"
field = "iter"
enum_value = ScalarKeyEnum.iter
def get_aggregation(self, interval: int) -> dict:
return {
self.name: {
"histogram": {"field": "iter", "interval": interval, "min_doc_count": 1}
}
}
class ISOTimeKey(ScalarKey):
"""
Aggregate by time formatted as ISO strings
"""
name = "iso_time"
field = "timestamp"
enum_value = ScalarKeyEnum.iso_time
bucket_key_key = "key_as_string"
def get_aggregation(self, interval: int) -> dict:
return {
self.name: {
"date_histogram": {
"field": "timestamp",
"interval": interval,
"min_doc_count": 1,
"format": "strict_date_time",
}
}
}
def _get_iterations_data_single(self, iter_data):
return iter_data[self.bucket_key_key], iter_data["avg_val"]["value"]

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from .builder import Builder

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from typing import Optional, Sequence, Iterable, Union
from config import config
log = config.logger(__file__)
RANGE_IGNORE_VALUE = -1
class Builder:
@staticmethod
def dates_range(from_date: Union[int, float], to_date: Union[int, float]) -> dict:
return {
"range": {
"timestamp": {
"gte": int(from_date),
"lte": int(to_date),
"format": "epoch_second",
}
}
}
@staticmethod
def terms(field: str, values: Iterable[str]) -> dict:
return {"terms": {field: list(values)}}
@staticmethod
def normalize_range(
range_: Sequence[Union[int, float]],
ignore_value: Union[int, float] = RANGE_IGNORE_VALUE,
) -> Optional[Sequence[Union[int, float]]]:
if not range_ or set(range_) == {ignore_value}:
return None
if len(range_) < 2:
return [range_[0]] * 2
return range_

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from .queue_bll import QueueBLL

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from collections import defaultdict
from datetime import datetime
from typing import Callable, Sequence, Optional, Tuple
from elasticsearch import Elasticsearch
import database
import es_factory
from apierrors import errors
from bll.queue.queue_metrics import QueueMetrics
from bll.workers import WorkerBLL
from database.errors import translate_errors_context
from database.model.queue import Queue, Entry
class QueueBLL(object):
def __init__(self, worker_bll: WorkerBLL = None, es: Elasticsearch = None):
self.worker_bll = worker_bll or WorkerBLL()
self.es = es or es_factory.connect("workers")
self._metrics = QueueMetrics(self.es)
@property
def metrics(self) -> QueueMetrics:
return self._metrics
@staticmethod
def create(
company_id: str,
name: str,
tags: Optional[Sequence[str]] = None,
system_tags: Optional[Sequence[str]] = None,
) -> Queue:
"""Creates a queue"""
with translate_errors_context():
now = datetime.utcnow()
queue = Queue(
id=database.utils.id(),
company=company_id,
created=now,
name=name,
tags=tags or [],
system_tags=system_tags or [],
last_update=now,
)
queue.save()
return queue
def get_by_id(
self, company_id: str, queue_id: str, only: Optional[Sequence[str]] = None
) -> Queue:
"""
Get queue by id
:raise errors.bad_request.InvalidQueueId: if the queue is not found
"""
with translate_errors_context():
query = dict(id=queue_id, company=company_id)
qs = Queue.objects(**query)
if only:
qs = qs.only(*only)
queue = qs.first()
if not queue:
raise errors.bad_request.InvalidQueueId(**query)
return queue
@classmethod
def get_queue_with_task(cls, company_id: str, queue_id: str, task_id: str) -> Queue:
with translate_errors_context():
query = dict(id=queue_id, company=company_id)
queue = Queue.objects(entries__task=task_id, **query).first()
if not queue:
raise errors.bad_request.InvalidQueueOrTaskNotQueued(
task=task_id, **query
)
return queue
def get_default(self, company_id: str) -> Queue:
"""
Get the default queue
:raise errors.bad_request.NoDefaultQueue: if the default queue not found
:raise errors.bad_request.MultipleDefaultQueues: if more than one default queue is found
"""
with translate_errors_context():
res = Queue.objects(company=company_id, system_tags="default").only(
"id", "name"
)
if not res:
raise errors.bad_request.NoDefaultQueue()
if len(res) > 1:
raise errors.bad_request.MultipleDefaultQueues(
queues=tuple(r.id for r in res)
)
return res.first()
def update(
self, company_id: str, queue_id: str, **update_fields
) -> Tuple[int, dict]:
"""
Partial update of the queue from update_fields
:raise errors.bad_request.InvalidQueueId: if the queue is not found
:return: number of updated objects and updated fields dictionary
"""
with translate_errors_context():
# validate the queue exists
self.get_by_id(company_id=company_id, queue_id=queue_id, only=("id",))
return Queue.safe_update(company_id, queue_id, update_fields)
def delete(self, company_id: str, queue_id: str, force: bool) -> None:
"""
Delete the queue
:raise errors.bad_request.InvalidQueueId: if the queue is not found
:raise errors.bad_request.QueueNotEmpty: if the queue is not empty and 'force' not set
"""
with translate_errors_context():
queue = self.get_by_id(company_id=company_id, queue_id=queue_id)
if queue.entries and not force:
raise errors.bad_request.QueueNotEmpty(
"use force=true to delete", id=queue_id
)
queue.delete()
def get_all(self, company_id: str, query_dict: dict) -> Sequence[dict]:
"""Get all the queues according to the query"""
with translate_errors_context():
return Queue.get_many(
company=company_id, parameters=query_dict, query_dict=query_dict
)
def get_queue_infos(self, company_id: str, query_dict: dict) -> Sequence[dict]:
"""
Get infos on all the company queues, including queue tasks and workers
"""
projection = Queue.get_extra_projection("entries.task.name")
with translate_errors_context():
res = Queue.get_many_with_join(
company=company_id,
query_dict=query_dict,
override_projection=projection,
)
queue_workers = defaultdict(list)
for worker in self.worker_bll.get_all(company_id):
for queue in worker.queues:
queue_workers[queue].append(worker)
for item in res:
item["workers"] = [
{
"name": w.id,
"ip": w.ip,
"task": w.task.to_struct() if w.task else None,
}
for w in queue_workers.get(item["id"], [])
]
return res
def add_task(self, company_id: str, queue_id: str, task_id: str) -> dict:
"""
Add the task to the queue and return the queue update results
:raise errors.bad_request.TaskAlreadyQueued: if the task is already in the queue
:raise errors.bad_request.InvalidQueueOrTaskNotQueued: if the queue update operation failed
"""
with translate_errors_context():
queue = self.get_by_id(company_id=company_id, queue_id=queue_id)
if any(e.task == task_id for e in queue.entries):
raise errors.bad_request.TaskAlreadyQueued(task=task_id)
self.metrics.log_queue_metrics_to_es(company_id=company_id, queues=[queue])
entry = Entry(added=datetime.utcnow(), task=task_id)
query = dict(id=queue_id, company=company_id)
res = Queue.objects(entries__task__ne=task_id, **query).update_one(
push__entries=entry, last_update=datetime.utcnow(), upsert=False
)
if not res:
raise errors.bad_request.InvalidQueueOrTaskNotQueued(
task=task_id, **query
)
return res
def get_next_task(self, company_id: str, queue_id: str) -> Optional[Entry]:
"""
Atomically pop and return the first task from the queue (or None)
:raise errors.bad_request.InvalidQueueId: if the queue does not exist
"""
with translate_errors_context():
query = dict(id=queue_id, company=company_id)
queue = Queue.objects(**query).modify(
pop__entries=-1, last_update=datetime.utcnow(), upsert=False
)
if not queue:
raise errors.bad_request.InvalidQueueId(**query)
self.metrics.log_queue_metrics_to_es(company_id, queues=[queue])
if not queue.entries:
return
return queue.entries[0]
def remove_task(self, company_id: str, queue_id: str, task_id: str) -> int:
"""
Removes the task from the queue and returns the number of removed items
:raise errors.bad_request.InvalidQueueOrTaskNotQueued: if the task is not found in the queue
"""
with translate_errors_context():
queue = self.get_queue_with_task(
company_id=company_id, queue_id=queue_id, task_id=task_id
)
self.metrics.log_queue_metrics_to_es(company_id, queues=[queue])
entries_to_remove = [e for e in queue.entries if e.task == task_id]
query = dict(id=queue_id, company=company_id)
res = Queue.objects(entries__task=task_id, **query).update_one(
pull_all__entries=entries_to_remove, last_update=datetime.utcnow()
)
return len(entries_to_remove) if res else 0
def reposition_task(
self,
company_id: str,
queue_id: str,
task_id: str,
pos_func: Callable[[int], int],
) -> int:
"""
Moves the task in the queue to the position calculated by pos_func
Returns the updated task position in the queue
"""
with translate_errors_context():
queue = self.get_queue_with_task(
company_id=company_id, queue_id=queue_id, task_id=task_id
)
position = next(i for i, e in enumerate(queue.entries) if e.task == task_id)
new_position = pos_func(position)
if new_position != position:
entry = queue.entries[position]
query = dict(id=queue_id, company=company_id)
updated = Queue.objects(entries__task=task_id, **query).update_one(
pull__entries=entry, last_update=datetime.utcnow()
)
if not updated:
raise errors.bad_request.RemovedDuringReposition(
task=task_id, **query
)
inst = {"$push": {"entries": {"$each": [entry.to_proper_dict()]}}}
if new_position >= 0:
inst["$push"]["entries"]["$position"] = new_position
res = Queue.objects(entries__task__ne=task_id, **query).update_one(
__raw__=inst
)
if not res:
raise errors.bad_request.FailedAddingDuringReposition(
task=task_id, **query
)
return new_position

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from collections import defaultdict
from datetime import datetime
from typing import Sequence
import elasticsearch.helpers
from elasticsearch import Elasticsearch
import es_factory
from apierrors.errors import bad_request
from bll.query import Builder as QueryBuilder
from config import config
from database.errors import translate_errors_context
from database.model.queue import Queue, Entry
from timing_context import TimingContext
log = config.logger(__file__)
class QueueMetrics:
class EsKeys:
DOC_TYPE = "metrics"
WAITING_TIME_FIELD = "average_waiting_time"
QUEUE_LENGTH_FIELD = "queue_length"
TIMESTAMP_FIELD = "timestamp"
QUEUE_FIELD = "queue"
def __init__(self, es: Elasticsearch):
self.es = es
@staticmethod
def _queue_metrics_prefix_for_company(company_id: str) -> str:
"""Returns the es index prefix for the company"""
return f"queue_metrics_{company_id}_"
@staticmethod
def _get_es_index_suffix():
"""Get the index name suffix for storing current month data"""
return datetime.utcnow().strftime("%Y-%m")
@staticmethod
def _calc_avg_waiting_time(entries: Sequence[Entry]) -> float:
"""
Calculate avg waiting time for the given tasks.
Return 0 if the list is empty
"""
if not entries:
return 0
now = datetime.utcnow()
total_waiting_in_secs = sum((now - e.added).total_seconds() for e in entries)
return total_waiting_in_secs / len(entries)
def log_queue_metrics_to_es(self, company_id: str, queues: Sequence[Queue]) -> bool:
"""
Calculate and write queue statistics (avg waiting time and queue length) to Elastic
:return: True if the write to es was successful, false otherwise
"""
es_index = (
self._queue_metrics_prefix_for_company(company_id)
+ self._get_es_index_suffix()
)
timestamp = es_factory.get_timestamp_millis()
def make_doc(queue: Queue) -> dict:
entries = [e for e in queue.entries if e.added]
return dict(
_index=es_index,
_type=self.EsKeys.DOC_TYPE,
_source={
self.EsKeys.TIMESTAMP_FIELD: timestamp,
self.EsKeys.QUEUE_FIELD: queue.id,
self.EsKeys.WAITING_TIME_FIELD: self._calc_avg_waiting_time(
entries
),
self.EsKeys.QUEUE_LENGTH_FIELD: len(entries),
},
)
actions = list(map(make_doc, queues))
es_res = elasticsearch.helpers.bulk(self.es, actions)
added, errors = es_res[:2]
return (added == len(actions)) and not errors
def _log_current_metrics(self, company_id: str, queue_ids=Sequence[str]):
query = dict(company=company_id)
if queue_ids:
query["id__in"] = list(queue_ids)
queues = Queue.objects(**query)
self.log_queue_metrics_to_es(company_id, queues=list(queues))
def _search_company_metrics(self, company_id: str, es_req: dict) -> dict:
return self.es.search(
index=f"{self._queue_metrics_prefix_for_company(company_id)}*",
doc_type=self.EsKeys.DOC_TYPE,
body=es_req,
)
@classmethod
def _get_dates_agg(cls, interval) -> dict:
"""
Aggregation for building date histogram with internal grouping per queue.
We are grouping by queue inside date histogram and not vice versa so that
it will be easy to average between queue metrics inside each date bucket.
Ignore empty buckets.
"""
return {
"dates": {
"date_histogram": {
"field": cls.EsKeys.TIMESTAMP_FIELD,
"interval": f"{interval}s",
"min_doc_count": 1,
},
"aggs": {
"queues": {
"terms": {"field": cls.EsKeys.QUEUE_FIELD},
"aggs": cls._get_top_waiting_agg(),
}
},
}
}
@classmethod
def _get_top_waiting_agg(cls) -> dict:
"""
Aggregation for getting max waiting time and the corresponding queue length
inside each date->queue bucket
"""
return {
"top_avg_waiting": {
"top_hits": {
"sort": [
{cls.EsKeys.WAITING_TIME_FIELD: {"order": "desc"}},
{cls.EsKeys.QUEUE_LENGTH_FIELD: {"order": "desc"}},
],
"_source": {
"includes": [
cls.EsKeys.WAITING_TIME_FIELD,
cls.EsKeys.QUEUE_LENGTH_FIELD,
]
},
"size": 1,
}
}
}
def get_queue_metrics(
self,
company_id: str,
from_date: float,
to_date: float,
interval: int,
queue_ids: Sequence[str],
) -> dict:
"""
Get the company queue metrics in the specified time range.
Returned as date histograms of average values per queue and metric type.
The from_date is extended by 'metrics_before_from_date' seconds from
queues.conf due to possibly small amount of points. The default extension is 3600s
In case no queue ids are specified the avg across all the
company queues is calculated for each metric
"""
# self._log_current_metrics(company, queue_ids=queue_ids)
if from_date >= to_date:
raise bad_request.FieldsValueError("from_date must be less than to_date")
seconds_before = config.get("services.queues.metrics_before_from_date", 3600)
must_terms = [QueryBuilder.dates_range(from_date - seconds_before, to_date)]
if queue_ids:
must_terms.append(QueryBuilder.terms("queue", queue_ids))
es_req = {
"size": 0,
"query": {"bool": {"must": must_terms}},
"aggs": self._get_dates_agg(interval),
}
with translate_errors_context(), TimingContext("es", "get_queue_metrics"):
res = self._search_company_metrics(company_id, es_req)
if "aggregations" not in res:
return {}
date_metrics = [
dict(
timestamp=d["key"],
queue_metrics=self._extract_queue_metrics(d["queues"]["buckets"]),
)
for d in res["aggregations"]["dates"]["buckets"]
if d["doc_count"] > 0
]
if queue_ids:
return self._datetime_histogram_per_queue(date_metrics)
return self._average_datetime_histogram(date_metrics)
@classmethod
def _datetime_histogram_per_queue(cls, date_metrics: Sequence[dict]) -> dict:
"""
Build datetime histogram per queue from datetime histogram where every
bucket contains all the queues metrics
"""
queues_data = defaultdict(list)
for date_data in date_metrics:
timestamp = date_data["timestamp"]
for queue, metrics in date_data["queue_metrics"].items():
queues_data[queue].append({"date": timestamp, **metrics})
return queues_data
@classmethod
def _average_datetime_histogram(cls, date_metrics: Sequence[dict]) -> dict:
"""
Calculate weighted averages and total count for each bucket of date_metrics histogram.
If for any queue the data is missing then take it from the previous bucket
The result is returned as a dictionary with one key 'total'
"""
queues_total = []
last_values = {}
for date_data in date_metrics:
date_metrics = date_data["queue_metrics"]
queue_metrics = {
**date_metrics,
**{k: v for k, v in last_values.items() if k not in date_metrics},
}
total_length = sum(m["queue_length"] for m in queue_metrics.values())
if total_length:
total_average = sum(
m["avg_waiting_time"] * m["queue_length"] / total_length
for m in queue_metrics.values()
)
else:
total_average = 0
queues_total.append(
dict(
date=date_data["timestamp"],
avg_waiting_time=total_average,
queue_length=total_length,
)
)
for k, v in date_metrics.items():
last_values[k] = v
return dict(total=queues_total)
@classmethod
def _extract_queue_metrics(cls, queue_buckets: Sequence[dict]) -> dict:
"""
Extract ES data for single date and queue bucket
"""
queue_metrics = dict()
for queue_data in queue_buckets:
if not queue_data["doc_count"]:
continue
res = queue_data["top_avg_waiting"]["hits"]["hits"][0]["_source"]
queue_metrics[queue_data["key"]] = {
"queue_length": res[cls.EsKeys.QUEUE_LENGTH_FIELD],
"avg_waiting_time": res[cls.EsKeys.WAITING_TIME_FIELD],
}
return queue_metrics

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from datetime import datetime
import operator
from threading import Thread, Lock
from time import sleep
import attr
import psutil
class ResourceMonitor(Thread):
@attr.s(auto_attribs=True)
class Sample:
cpu_usage: float = 0.0
mem_used_gb: float = 0
mem_free_gb: float = 0
@classmethod
def _apply(cls, op, *samples):
return cls(
**{
field: op(*(getattr(sample, field) for sample in samples))
for field in attr.fields_dict(cls)
}
)
def min(self, sample):
return self._apply(min, self, sample)
def max(self, sample):
return self._apply(max, self, sample)
def avg(self, sample, count):
res = self._apply(lambda x: x * count, self)
res = self._apply(operator.add, res, sample)
res = self._apply(lambda x: x / (count + 1), res)
return res
def __init__(self, sample_interval_sec=5):
super(ResourceMonitor, self).__init__(daemon=True)
self.sample_interval_sec = sample_interval_sec
self._lock = Lock()
self._clear()
def _clear(self):
sample = self._get_sample()
self._avg = sample
self._min = sample
self._max = sample
self._clear_time = datetime.utcnow()
self._count = 1
@classmethod
def _get_sample(cls) -> Sample:
return cls.Sample(
cpu_usage=psutil.cpu_percent(),
mem_used_gb=psutil.virtual_memory().used / (1024 ** 3),
mem_free_gb=psutil.virtual_memory().free / (1024 ** 3),
)
def run(self):
while True:
sample = self._get_sample()
with self._lock:
self._min = self._min.min(sample)
self._max = self._max.max(sample)
self._avg = self._avg.avg(sample, self._count)
self._count += 1
sleep(self.sample_interval_sec)
def get_stats(self) -> dict:
""" Returns current resource statistics and clears internal resource statistics """
with self._lock:
min_ = attr.asdict(self._min)
max_ = attr.asdict(self._max)
avg = attr.asdict(self._avg)
res = {
"interval_sec": (datetime.utcnow() - self._clear_time).total_seconds(),
"num_cores": psutil.cpu_count(),
**{
k: {"min": v, "max": max_[k], "avg": avg[k]}
for k, v in min_.items()
}
}
self._clear()
return res

View File

@@ -0,0 +1,306 @@
import logging
import queue
import random
import time
from datetime import timedelta, datetime
from time import sleep
from typing import Sequence, Optional
import dpath
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from bll.query import Builder as QueryBuilder
from bll.util import get_server_uuid
from bll.workers import WorkerStats, WorkerBLL
from config import config
from config.info import get_deployment_type
from database.model import Company, User
from database.model.queue import Queue
from database.model.task.task import Task
from utilities import safe_get
from utilities.json import dumps
from utilities.threads_manager import ThreadsManager
from version import __version__ as current_version
from .resource_monitor import ResourceMonitor
log = config.logger(__file__)
worker_bll = WorkerBLL()
class StatisticsReporter:
threads = ThreadsManager("Statistics", resource_monitor=ResourceMonitor)
send_queue = queue.Queue()
supported = config.get("apiserver.statistics.supported", True)
@classmethod
def start(cls):
cls.start_sender()
cls.start_reporter()
@classmethod
@threads.register("reporter", daemon=True)
def start_reporter(cls):
"""
Periodically send statistics reports for companies who have opted in.
Note: in trains we usually have only a single company
"""
if not cls.supported:
return
report_interval = timedelta(
hours=config.get("apiserver.statistics.report_interval_hours", 24)
)
while True:
sleep(report_interval.total_seconds())
try:
for company in Company.objects(
defaults__stats_option__enabled=True
).only("id"):
stats = cls.get_statistics(company.id)
cls.send_queue.put(stats)
except Exception as ex:
log.exception(f"Failed collecting stats: {str(ex)}")
@classmethod
@threads.register("sender", daemon=True)
def start_sender(cls):
if not cls.supported:
return
url = config.get("apiserver.statistics.url")
retries = config.get("apiserver.statistics.max_retries", 5)
max_backoff = config.get("apiserver.statistics.max_backoff_sec", 5)
session = requests.Session()
adapter = HTTPAdapter(max_retries=Retry(retries))
session.mount("http://", adapter)
session.mount("https://", adapter)
session.headers["Content-type"] = "application/json"
WarningFilter.attach()
while True:
try:
report = cls.send_queue.get()
# Set a random backoff factor each time we send a report
adapter.max_retries.backoff_factor = random.random() * max_backoff
session.post(url, data=dumps(report))
except Exception as ex:
pass
@classmethod
def get_statistics(cls, company_id: str) -> dict:
"""
Returns a statistics report per company
"""
return {
"time": datetime.utcnow(),
"company_id": company_id,
"server": {
"version": current_version,
"deployment": get_deployment_type(),
"uuid": get_server_uuid(),
"queues": {"count": Queue.objects(company=company_id).count()},
"users": {"count": User.objects(company=company_id).count()},
"resources": cls.threads.resource_monitor.get_stats(),
"experiments": next(
iter(cls._get_experiments_stats(company_id).values()), {}
),
},
"agents": cls._get_agents_statistics(company_id),
}
@classmethod
def _get_agents_statistics(cls, company_id: str) -> Sequence[dict]:
result = cls._get_resource_stats_per_agent(company_id, key="resources")
dpath.merge(
result, cls._get_experiments_stats_per_agent(company_id, key="experiments")
)
return [{"uuid": agent_id, **data} for agent_id, data in result.items()]
@classmethod
def _get_resource_stats_per_agent(cls, company_id: str, key: str) -> dict:
agent_resource_threshold_sec = timedelta(
hours=config.get("apiserver.statistics.report_interval_hours", 24)
).total_seconds()
to_timestamp = int(time.time())
from_timestamp = to_timestamp - int(agent_resource_threshold_sec)
es_req = {
"size": 0,
"query": QueryBuilder.dates_range(from_timestamp, to_timestamp),
"aggs": {
"workers": {
"terms": {"field": "worker"},
"aggs": {
"categories": {
"terms": {"field": "category"},
"aggs": {"count": {"cardinality": {"field": "variant"}}},
},
"metrics": {
"terms": {"field": "metric"},
"aggs": {
"min": {"min": {"field": "value"}},
"max": {"max": {"field": "value"}},
"avg": {"avg": {"field": "value"}},
},
},
},
}
},
}
res = cls._run_worker_stats_query(company_id, es_req)
def _get_cardinality_fields(categories: Sequence[dict]) -> dict:
names = {"cpu": "num_cores"}
return {
names[c["key"]]: safe_get(c, "count/value")
for c in categories
if c["key"] in names
}
def _get_metric_fields(metrics: Sequence[dict]) -> dict:
names = {
"cpu_usage": "cpu_usage",
"memory_used": "mem_used_gb",
"memory_free": "mem_free_gb",
}
return {
names[m["key"]]: {
"min": safe_get(m, "min/value"),
"max": safe_get(m, "max/value"),
"avg": safe_get(m, "avg/value"),
}
for m in metrics
if m["key"] in names
}
buckets = safe_get(res, "aggregations/workers/buckets", default=[])
return {
b["key"]: {
key: {
"interval_sec": agent_resource_threshold_sec,
**_get_cardinality_fields(safe_get(b, "categories/buckets", [])),
**_get_metric_fields(safe_get(b, "metrics/buckets", [])),
}
}
for b in buckets
}
@classmethod
def _get_experiments_stats_per_agent(cls, company_id: str, key: str) -> dict:
agent_relevant_threshold = timedelta(
days=config.get("apiserver.statistics.agent_relevant_threshold_days", 30)
)
to_timestamp = int(time.time())
from_timestamp = to_timestamp - int(agent_relevant_threshold.total_seconds())
workers = cls._get_active_workers(company_id, from_timestamp, to_timestamp)
if not workers:
return {}
stats = cls._get_experiments_stats(company_id, list(workers.keys()))
return {
worker_id: {key: {**workers[worker_id], **stat}}
for worker_id, stat in stats.items()
}
@classmethod
def _get_active_workers(
cls, company_id, from_timestamp: int, to_timestamp: int
) -> dict:
es_req = {
"size": 0,
"query": QueryBuilder.dates_range(from_timestamp, to_timestamp),
"aggs": {
"workers": {
"terms": {"field": "worker"},
"aggs": {"last_activity_time": {"max": {"field": "timestamp"}}},
}
},
}
res = cls._run_worker_stats_query(company_id, es_req)
buckets = safe_get(res, "aggregations/workers/buckets", default=[])
return {
b["key"]: {"last_activity_time": b["last_activity_time"]["value"]}
for b in buckets
}
@classmethod
def _run_worker_stats_query(cls, company_id, es_req) -> dict:
return worker_bll.es_client.search(
index=f"{WorkerStats.worker_stats_prefix_for_company(company_id)}*",
doc_type="stat",
body=es_req,
)
@classmethod
def _get_experiments_stats(
cls, company_id, workers: Optional[Sequence] = None
) -> dict:
pipeline = [
{
"$match": {
"company": company_id,
"started": {"$exists": True, "$ne": None},
"last_update": {"$exists": True, "$ne": None},
"status": {"$nin": ["created", "queued"]},
**({"last_worker": {"$in": workers}} if workers else {}),
}
},
{
"$group": {
"_id": "$last_worker" if workers else None,
"count": {"$sum": 1},
"avg_run_time_sec": {
"$avg": {
"$divide": [
{"$subtract": ["$last_update", "$started"]},
1000,
]
}
},
"avg_iterations": {"$avg": "$last_iteration"},
}
},
{
"$project": {
"count": 1,
"avg_run_time_sec": {"$trunc": "$avg_run_time_sec"},
"avg_iterations": {"$trunc": "$avg_iterations"},
}
},
]
return {
group["_id"]: {k: v for k, v in group.items() if k != "_id"}
for group in Task.aggregate(*pipeline)
}
class WarningFilter(logging.Filter):
@classmethod
def attach(cls):
from urllib3.connectionpool import (
ConnectionPool,
) # required to make sure the logger is created
assert ConnectionPool # make sure import is not optimized out
logging.getLogger("urllib3.connectionpool").addFilter(cls())
def filter(self, record):
if (
record.levelno == logging.WARNING
and len(record.args) > 2
and record.args[2] == "/stats"
):
return False
return True

View File

@@ -2,8 +2,7 @@ import re
from collections import OrderedDict
from datetime import datetime, timedelta
from time import sleep
from typing import Mapping, Collection
from urllib.parse import urlparse
from typing import Collection, Sequence, Tuple, Any
import six
from mongoengine import Q
@@ -13,12 +12,15 @@ import es_factory
from apierrors import errors
from config import config
from database.errors import translate_errors_context
from database.fields import OutputDestinationField
from database.model.model import Model
from database.model.project import Project
from database.model.task.metrics import MetricEvent
from database.model.task.output import Output
from database.model.task.task import Task, TaskStatus, TaskStatusMessage, TaskTags
from database.model.task.task import (
Task,
TaskStatus,
TaskStatusMessage,
TaskSystemTags,
)
from database.utils import get_company_or_none_constraint, id as create_id
from service_repo import APICall
from timing_context import TimingContext
@@ -27,7 +29,7 @@ from .utils import ChangeStatusRequest, validate_status_change
class TaskBLL(object):
threads = ThreadsManager()
threads = ThreadsManager("TaskBLL")
def __init__(self, events_es=None):
self.events_es = (
@@ -143,7 +145,7 @@ class TaskBLL(object):
return model
@classmethod
def validate(cls, task: Task, force=False):
def validate(cls, task: Task):
assert isinstance(task, Task)
if task.parent and not Task.get(
@@ -154,24 +156,12 @@ class TaskBLL(object):
if task.project:
Project.get_for_writing(company=task.company, id=task.project)
model = cls.validate_execution_model(task)
if model and not force and not model.ready:
raise errors.bad_request.ModelNotReady(
"can't be used in a task", model=model.id
)
cls.validate_execution_model(task)
if task.execution:
if task.execution.parameters:
cls._validate_execution_parameters(task.execution.parameters)
if task.output and task.output.destination:
parsed_url = urlparse(task.output.destination)
if parsed_url.scheme not in OutputDestinationField.schemes:
raise errors.bad_request.FieldsValueError(
"unsupported scheme for output destination",
dest=task.output.destination,
)
@staticmethod
def _validate_execution_parameters(parameters):
invalid_keys = [k for k in parameters if re.search(r"\s", k)]
@@ -218,7 +208,7 @@ class TaskBLL(object):
]
with translate_errors_context():
result = Task.objects.aggregate(*pipeline)
result = Task.aggregate(*pipeline)
return [r["metrics"][0] for r in result]
@staticmethod
@@ -236,7 +226,7 @@ class TaskBLL(object):
last_update: datetime = None,
last_iteration: int = None,
last_iteration_max: int = None,
last_metrics: Mapping[str, Mapping[str, MetricEvent]] = None,
last_values: Sequence[Tuple[Tuple[str, ...], Any]] = None,
**extra_updates,
):
"""
@@ -248,7 +238,7 @@ class TaskBLL(object):
task's last iteration value.
:param last_iteration_max: Last reported iteration. Use this to conditionally set a value only
if the current task's last iteration value is smaller than the provided value.
:param last_metrics: Last reported metrics summary.
:param last_values: Last reported metrics summary (value, metric, variant).
:param extra_updates: Extra task updates to include in this update call.
:return:
"""
@@ -259,10 +249,18 @@ class TaskBLL(object):
elif last_iteration_max is not None:
extra_updates.update(max__last_iteration=last_iteration_max)
if last_metrics is not None:
extra_updates.update(last_metrics=last_metrics)
if last_values is not None:
return Task.objects(id=task_id, company=company_id).update(
def op_path(op, *path):
return "__".join((op, "last_metrics") + path)
for path, value in last_values:
extra_updates[op_path("set", *path)] = value
if path[-1] == "value":
extra_updates[op_path("min", *path[:-1], "min_value")] = value
extra_updates[op_path("max", *path[:-1], "max_value")] = value
Task.objects(id=task_id, company=company_id).update(
upsert=False, last_update=last_update, **extra_updates
)
@@ -378,11 +376,27 @@ class TaskBLL(object):
task = TaskBLL.get_task_with_access(
task_id,
company_id=company_id,
only=("status", "project", "tags", "last_update"),
only=(
"status",
"project",
"tags",
"system_tags",
"last_worker",
"last_update",
),
requires_write_access=True,
)
if TaskTags.development in task.tags:
def is_run_by_worker(t: Task) -> bool:
"""Checks if there is an active worker running the task"""
update_timeout = config.get("apiserver.workers.task_update_timeout", 600)
return (
t.last_worker
and t.last_update
and (datetime.utcnow() - t.last_update).total_seconds() < update_timeout
)
if TaskSystemTags.development in task.system_tags or not is_run_by_worker(task):
new_status = TaskStatus.stopped
status_message = f"Stopped by {user_name}"
else:
@@ -448,3 +462,58 @@ class TaskBLL(object):
except Exception as ex:
log.exception(f"Failed stopping tasks: {str(ex)}")
@staticmethod
def get_aggregated_project_execution_parameters(
company_id,
project_ids: Sequence[str] = None,
page: int = 0,
page_size: int = 500,
) -> Tuple[int, int, Sequence[str]]:
page = max(0, page)
page_size = max(1, page_size)
pipeline = [
{
"$match": {
"company": company_id,
"execution.parameters": {"$exists": True, "$gt": {}},
**({"project": {"$in": project_ids}} if project_ids else {}),
}
},
{"$project": {"parameters": {"$objectToArray": "$execution.parameters"}}},
{"$unwind": "$parameters"},
{"$group": {"_id": "$parameters.k"}},
{"$sort": {"_id": 1}},
{
"$group": {
"_id": 1,
"total": {"$sum": 1},
"results": {"$push": "$$ROOT"},
}
},
{
"$project": {
"total": 1,
"results": {"$slice": ["$results", page * page_size, page_size]},
}
},
]
with translate_errors_context():
result = next(
Task.aggregate(*pipeline),
None,
)
total = 0
remaining = 0
results = []
if result:
total = int(result.get("total", -1))
results = [r["_id"] for r in result.get("results", [])]
remaining = max(0, total - (len(results) + page * page_size))
return total, remaining, results

View File

@@ -7,7 +7,7 @@ import six
from apierrors import errors
from database.errors import translate_errors_context
from database.model.project import Project
from database.model.task.task import Task, TaskStatus
from database.model.task.task import Task, TaskStatus, TaskSystemTags
from database.utils import get_options
from timing_context import TimingContext
from utilities.attrs import typed_attrs
@@ -25,9 +25,10 @@ class ChangeStatusRequest(object):
status_message = attr.ib(type=six.string_types, default="")
force = attr.ib(type=bool, default=False)
allow_same_state_transition = attr.ib(type=bool, default=True)
current_status_override = attr.ib(default=None)
def execute(self, **kwargs):
current_status = self.task.status
current_status = self.current_status_override or self.task.status
project_id = self.task.project
# Verify new status is allowed from current status (will throw exception if not valid)
@@ -44,6 +45,9 @@ class ChangeStatusRequest(object):
last_update=now,
)
if self.new_status == TaskStatus.queued:
fields["pull__system_tags"] = TaskSystemTags.development
def safe_mongoengine_key(key):
return f"__{key}" if key in control else key
@@ -66,6 +70,10 @@ class ChangeStatusRequest(object):
)
update_project_time(project_id)
# make sure that _raw_ queries are not returned back to the client
fields.pop("__raw__", None)
return dict(updated=updated, fields=fields)
def validate_transition(self, current_status):
@@ -95,7 +103,8 @@ def validate_status_change(current_status, new_status):
state_machine = {
TaskStatus.created: {TaskStatus.in_progress},
TaskStatus.created: {TaskStatus.queued, TaskStatus.in_progress},
TaskStatus.queued: {TaskStatus.created, TaskStatus.in_progress},
TaskStatus.in_progress: {
TaskStatus.stopped,
TaskStatus.failed,
@@ -125,7 +134,7 @@ state_machine = {
TaskStatus.published,
TaskStatus.in_progress,
TaskStatus.created,
}
},
}
@@ -135,9 +144,11 @@ def get_possible_status_changes(current_status):
:return possible states from current state
"""
possible = state_machine.get(current_status)
assert (
possible is not None
), f"Current status {current_status} not supported by state machine"
if possible is None:
raise errors.server_error.InternalError(
f"Current status {current_status} not supported by state machine"
)
return possible

73
server/bll/util.py Normal file
View File

@@ -0,0 +1,73 @@
import functools
from operator import itemgetter
from typing import Sequence, Optional, Callable, Tuple, Dict, Any, Set
from database.model import AttributedDocument
from database.model.settings import Settings
def extract_properties_to_lists(
key_names: Sequence[str],
data: Sequence[dict],
extract_func: Optional[Callable[[dict], Tuple]] = None,
) -> dict:
"""
Given a list of dictionaries and names of dictionary keys
builds a dictionary with the requested keys and values lists
:param key_names: names of the keys in the resulting dictionary
:param data: sequence of dictionaries to extract values from
:param extract_func: the optional callable that extracts properties
from a dictionary and put them in a tuple in the order corresponding to
key_names. If not specified then properties are extracted according to key_names
"""
value_sequences = zip(*map(extract_func or itemgetter(*key_names), data))
return dict(zip(key_names, map(list, value_sequences)))
class SetFieldsResolver:
"""
The class receives set fields dictionary
and for the set fields that require 'min' or 'max'
operation replace them with a simple set in case the
DB document does not have these fields set
"""
SET_MODIFIERS = ("min", "max")
def __init__(self, set_fields: Dict[str, Any]):
self.orig_fields = set_fields
self.fields = {
f: fname
for f, modifier, dunder, fname in (
(f,) + f.partition("__") for f in set_fields.keys()
)
if dunder and modifier in self.SET_MODIFIERS
}
def _get_updated_name(self, doc: AttributedDocument, name: str) -> str:
if name in self.fields and doc.get_field_value(self.fields[name]) is None:
return self.fields[name]
return name
def get_fields(self, doc: AttributedDocument):
"""
For the given document return the set fields instructions
with min/max operations replaced with a single set in case
the document does not have the field set
"""
return {
self._get_updated_name(doc, name): value
for name, value in self.orig_fields.items()
}
def get_names(self) -> Set[str]:
"""
Returns the names of the fields that had min/max modifiers
in the format suitable for projection (dot separated)
"""
return set(name.replace("__", ".") for name in self.fields.values())
@functools.lru_cache()
def get_server_uuid() -> Optional[str]:
return Settings.get_by_key("server.uuid")

View File

@@ -0,0 +1,422 @@
import itertools
from datetime import datetime, timedelta
from typing import Sequence, Set, Optional
import attr
import elasticsearch.helpers
import es_factory
from apierrors import APIError
from apierrors.errors import bad_request, server_error
from apimodels.workers import (
DEFAULT_TIMEOUT,
IdNameEntry,
WorkerEntry,
StatusReportRequest,
WorkerResponseEntry,
QueueEntry,
MachineStats,
)
from config import config
from database.errors import translate_errors_context
from database.model.auth import User
from database.model.company import Company
from database.model.queue import Queue
from database.model.task.task import Task
from redis_manager import redman
from timing_context import TimingContext
from tools import safe_get
from .stats import WorkerStats
log = config.logger(__file__)
class WorkerBLL:
def __init__(self, es=None, redis=None):
self.es_client = es if es is not None else es_factory.connect("workers")
self.redis = redis if redis is not None else redman.connection("workers")
self._stats = WorkerStats(self.es_client)
@property
def stats(self) -> WorkerStats:
return self._stats
def register_worker(
self,
company_id: str,
user_id: str,
worker: str,
ip: str = "",
queues: Sequence[str] = None,
timeout: int = 0,
) -> WorkerEntry:
"""
Register a worker
:param company_id: worker's company ID
:param user_id: user ID under which this worker is running
:param worker: worker ID
:param ip: the real ip of the worker
:param queues: queues reported as being monitored by the worker
:param timeout: registration expiration timeout in seconds
:raise bad_request.InvalidUserId: in case the calling user or company does not exist
:return: worker entry instance
"""
key = WorkerBLL._get_worker_key(company_id, user_id, worker)
timeout = timeout or DEFAULT_TIMEOUT
queues = queues or []
with translate_errors_context():
query = dict(id=user_id, company=company_id)
user = User.objects(**query).only("id", "name").first()
if not user:
raise bad_request.InvalidUserId(**query)
company = Company.objects(id=company_id).only("id", "name").first()
if not company:
raise server_error.InternalError("invalid company", company=company_id)
queue_objs = Queue.objects(company=company_id, id__in=queues).only("id")
if len(queue_objs) < len(queues):
invalid = set(queues).difference(q.id for q in queue_objs)
raise bad_request.InvalidQueueId(ids=invalid)
now = datetime.utcnow()
entry = WorkerEntry(
key=key,
id=worker,
user=user.to_proper_dict(),
company=company.to_proper_dict(),
ip=ip,
queues=queues,
register_time=now,
register_timeout=timeout,
last_activity_time=now,
)
self.redis.setex(key, timedelta(seconds=timeout), entry.to_json())
return entry
def unregister_worker(self, company_id: str, user_id: str, worker: str) -> None:
"""
Unregister a worker
:param company_id: worker's company ID
:param user_id: user ID under which this worker is running
:param worker: worker ID
:raise bad_request.WorkerNotRegistered: the worker was not previously registered
"""
with TimingContext("redis", "workers_unregister"):
res = self.redis.delete(
company_id, self._get_worker_key(company_id, user_id, worker)
)
if not res:
raise bad_request.WorkerNotRegistered(worker=worker)
def status_report(
self, company_id: str, user_id: str, ip: str, report: StatusReportRequest
) -> None:
"""
Write worker status report
:param company_id: worker's company ID
:param user_id: user_id ID under which this worker is running
:raise bad_request.InvalidTaskId: the reported task was not found
:return: worker entry instance
"""
entry = self._get_worker(company_id, user_id, report.worker)
try:
entry.ip = ip
now = datetime.utcnow()
entry.last_activity_time = now
if report.machine_stats:
self._log_stats_to_es(
company_id=company_id,
company_name=entry.company.name,
worker=report.worker,
timestamp=report.timestamp,
task=report.task,
machine_stats=report.machine_stats,
)
entry.queue = report.queue
if report.queues:
entry.queues = report.queues
if not report.task:
entry.task = None
else:
with translate_errors_context():
query = dict(id=report.task, company=company_id)
update = dict(
last_worker=report.worker,
last_worker_report=now,
last_update=now,
)
# modify(new=True, ...) returns the modified object
task = Task.objects(**query).modify(new=True, **update)
if not task:
raise bad_request.InvalidTaskId(**query)
entry.task = IdNameEntry(id=task.id, name=task.name)
entry.last_report_time = now
except APIError:
raise
except Exception as e:
msg = "Failed processing worker status report"
log.exception(msg)
raise server_error.DataError(msg, err=e.args[0])
finally:
self._save_worker(entry)
def get_all(
self, company_id: str, last_seen: Optional[int] = None
) -> Sequence[WorkerEntry]:
"""
Get all the company workers that were active during the last_seen period
:param company_id: worker's company id
:param last_seen: period in seconds to check. Min value is 1 second
:return:
"""
try:
workers = self._get(company_id)
except Exception as e:
raise server_error.DataError("failed loading worker entries", err=e.args[0])
if last_seen:
ref_time = datetime.utcnow() - timedelta(seconds=max(1, last_seen))
workers = [
w
for w in workers
if w.last_activity_time.replace(tzinfo=None) >= ref_time
]
return workers
def get_all_with_projection(
self, company_id: str, last_seen: int
) -> Sequence[WorkerResponseEntry]:
helpers = list(
map(
WorkerConversionHelper.from_worker_entry,
self.get_all(company_id=company_id, last_seen=last_seen),
)
)
task_ids = set(filter(None, (helper.task_id for helper in helpers)))
all_queues = set(
itertools.chain.from_iterable(helper.queue_ids for helper in helpers)
)
queues_info = {}
if all_queues:
projection = [
{"$match": {"_id": {"$in": list(all_queues)}}},
{
"$project": {
"name": 1,
"next_entry": {"$arrayElemAt": ["$entries", 0]},
"num_entries": {"$size": "$entries"},
}
},
]
queues_info = {
res["_id"]: res for res in Queue.objects.aggregate(*projection)
}
task_ids = task_ids.union(
filter(
None,
(
safe_get(info, "next_entry/task")
for info in queues_info.values()
),
)
)
tasks_info = {}
if task_ids:
tasks_info = {
task.id: task
for task in Task.objects(id__in=task_ids).only(
"name", "started", "last_iteration"
)
}
def update_queue_entries(*entries):
for entry in entries:
if not entry:
continue
info = queues_info.get(entry.id, None)
if not info:
continue
entry.name = info.get("name", None)
entry.num_tasks = info.get("num_entries", 0)
task_id = safe_get(info, "next_entry/task")
if task_id:
task = tasks_info.get(task_id, None)
entry.next_task = IdNameEntry(
id=task_id, name=task.name if task else None
)
for helper in helpers:
worker = helper.worker
if helper.task_id:
task = tasks_info.get(helper.task_id, None)
if task:
worker.task.running_time = (
int((datetime.utcnow() - task.started).total_seconds() * 1000)
if task.started
else 0
)
worker.task.last_iteration = task.last_iteration
update_queue_entries(worker.queue)
if worker.queues:
update_queue_entries(*worker.queues)
return [helper.worker for helper in helpers]
@staticmethod
def _get_worker_key(company: str, user: str, worker_id: str) -> str:
"""Build redis key from company, user and worker_id"""
return f"worker_{company}_{user}_{worker_id}"
def _get_worker(self, company_id: str, user_id: str, worker: str) -> WorkerEntry:
"""
Get a worker entry for the provided worker ID. The entry is loaded from Redis
if it exists (i.e. worker has already been registered), otherwise the worker
is registered and its entry stored into Redis).
:param company_id: worker's company ID
:param user_id: user ID under which this worker is running
:param worker: worker ID
:raise bad_request.InvalidWorkerId: in case the worker id was not found
:return: worker entry instance
"""
key = self._get_worker_key(company_id, user_id, worker)
with TimingContext("redis", "get_worker"):
data = self.redis.get(key)
if data:
try:
entry = WorkerEntry.from_json(data)
if not entry.key:
entry.key = key
self._save_worker(entry)
return entry
except Exception as e:
msg = "Failed parsing worker entry"
log.exception(msg)
raise server_error.DataError(msg, err=e.args[0])
# Failed loading worker from Redis
if config.get("apiserver.workers.auto_register", False):
try:
return self.register_worker(company_id, user_id, worker)
except Exception:
log.error(
"Failed auto registration of {} for company {}".format(
worker, company_id
)
)
raise bad_request.InvalidWorkerId(worker=worker)
def _save_worker(self, entry: WorkerEntry) -> None:
"""Save worker entry in Redis"""
try:
self.redis.setex(
entry.key, timedelta(seconds=entry.register_timeout), entry.to_json()
)
except Exception:
msg = "Failed saving worker entry"
log.exception(msg)
def _get(
self, company: str, user: str = "*", worker_id: str = "*"
) -> Sequence[WorkerEntry]:
"""Get worker entries matching the company and user, worker patterns"""
match = self._get_worker_key(company, user, worker_id)
with TimingContext("redis", "workers_get_all"):
res = self.redis.scan_iter(match)
return [WorkerEntry.from_json(self.redis.get(r)) for r in res]
@staticmethod
def _get_es_index_suffix():
"""Get the index name suffix for storing current month data"""
return datetime.utcnow().strftime("%Y-%m")
def _log_stats_to_es(
self,
company_id: str,
company_name: str,
worker: str,
timestamp: int,
task: str,
machine_stats: MachineStats,
) -> bool:
"""
Actually writing the worker statistics to Elastic
:return: True if successful, False otherwise
"""
es_index = (
f"{self._stats.worker_stats_prefix_for_company(company_id)}"
f"{self._get_es_index_suffix()}"
)
def make_doc(category, metric, variant, value) -> dict:
return dict(
_index=es_index,
_type="stat",
_source=dict(
timestamp=timestamp,
worker=worker,
company=company_name,
task=task,
category=category,
metric=metric,
variant=variant,
value=float(value),
),
)
actions = []
for field, value in machine_stats.to_struct().items():
if not value:
continue
category = field.partition("_")[0]
metric = field
if not isinstance(value, (list, tuple)):
actions.append(make_doc(category, metric, "total", value))
else:
actions.extend(
make_doc(category, metric, str(i), val)
for i, val in enumerate(value)
)
es_res = elasticsearch.helpers.bulk(self.es_client, actions)
added, errors = es_res[:2]
return (added == len(actions)) and not errors
@attr.s(auto_attribs=True)
class WorkerConversionHelper:
worker: WorkerResponseEntry
task_id: str
queue_ids: Set[str]
@classmethod
def from_worker_entry(cls, worker: WorkerEntry):
data = worker.to_struct()
queue = data.pop("queue", None) or None
queue_ids = set(data.pop("queues", []))
queues = [QueueEntry(id=id) for id in queue_ids]
if queue:
queue = next((q for q in queues if q.id == queue), None)
return cls(
worker=WorkerResponseEntry(queues=queues, queue=queue, **data),
task_id=worker.task.id if worker.task else None,
queue_ids=queue_ids,
)

244
server/bll/workers/stats.py Normal file
View File

@@ -0,0 +1,244 @@
from operator import attrgetter
from typing import Optional, Sequence
from boltons.iterutils import bucketize
from apierrors.errors import bad_request
from apimodels.workers import AggregationType, GetStatsRequest, StatItem
from bll.query import Builder as QueryBuilder
from config import config
from database.errors import translate_errors_context
from timing_context import TimingContext
log = config.logger(__file__)
class WorkerStats:
def __init__(self, es):
self.es = es
@staticmethod
def worker_stats_prefix_for_company(company_id: str) -> str:
"""Returns the es index prefix for the company"""
return f"worker_stats_{company_id}_"
def _search_company_stats(self, company_id: str, es_req: dict) -> dict:
return self.es.search(
index=f"{self.worker_stats_prefix_for_company(company_id)}*",
doc_type="stat",
body=es_req,
)
def get_worker_stats_keys(
self, company_id: str, worker_ids: Optional[Sequence[str]]
) -> dict:
"""
Get dictionary of metric types grouped by categories
:param company_id: company id
:param worker_ids: optional list of workers to get metric types from.
If not specified them metrics for all the company workers returned
:return:
"""
es_req = {
"size": 0,
"aggs": {
"categories": {
"terms": {"field": "category"},
"aggs": {"metrics": {"terms": {"field": "metric"}}},
}
},
}
if worker_ids:
es_req["query"] = QueryBuilder.terms("worker", worker_ids)
res = self._search_company_stats(company_id, es_req)
if not res["hits"]["total"]:
raise bad_request.WorkerStatsNotFound(
f"No statistic metrics found for the company {company_id} and workers {worker_ids}"
)
return {
category["key"]: [
metric["key"] for metric in category["metrics"]["buckets"]
]
for category in res["aggregations"]["categories"]["buckets"]
}
def get_worker_stats(self, company_id: str, request: GetStatsRequest) -> dict:
"""
Get statistics for company workers metrics in the specified time range
Returned as date histograms for different aggregation types
grouped by worker, metric type (and optionally metric variant)
Buckets with no metrics are not returned
Note: all the statistics are retrieved as one ES query
"""
if request.from_date >= request.to_date:
raise bad_request.FieldsValueError("from_date must be less than to_date")
def get_dates_agg() -> dict:
es_to_agg_types = (
("avg", AggregationType.avg.value),
("min", AggregationType.min.value),
("max", AggregationType.max.value),
)
return {
"dates": {
"date_histogram": {
"field": "timestamp",
"interval": f"{request.interval}s",
"min_doc_count": 1,
},
"aggs": {
agg_type: {es_agg: {"field": "value"}}
for es_agg, agg_type in es_to_agg_types
},
}
}
def get_variants_agg() -> dict:
return {
"variants": {"terms": {"field": "variant"}, "aggs": get_dates_agg()}
}
es_req = {
"size": 0,
"aggs": {
"workers": {
"terms": {"field": "worker"},
"aggs": {
"metrics": {
"terms": {"field": "metric"},
"aggs": get_variants_agg()
if request.split_by_variant
else get_dates_agg(),
}
},
}
},
}
query_terms = [
QueryBuilder.dates_range(request.from_date, request.to_date),
QueryBuilder.terms("metric", {item.key for item in request.items}),
]
if request.worker_ids:
query_terms.append(QueryBuilder.terms("worker", request.worker_ids))
es_req["query"] = {"bool": {"must": query_terms}}
with translate_errors_context(), TimingContext("es", "get_worker_stats"):
data = self._search_company_stats(company_id, es_req)
return self._extract_results(data, request.items, request.split_by_variant)
@staticmethod
def _extract_results(
data: dict, request_items: Sequence[StatItem], split_by_variant: bool
) -> dict:
"""
Clean results returned from elastic search (remove "aggregations", "buckets" etc.),
leave only aggregation types requested by the user and return a clean dictionary
and return a "clean" dictionary of
:param data: aggregation data retrieved from ES
:param request_items: aggs types requested by the user
:param split_by_variant: if False then aggregate by metric type, otherwise metric type + variant
"""
if "aggregations" not in data:
return {}
items_by_key = bucketize(request_items, key=attrgetter("key"))
aggs_per_metric = {
key: [item.aggregation for item in items]
for key, items in items_by_key.items()
}
def extract_date_stats(date: dict, metric_key) -> dict:
return {
"date": date["key"],
"count": date["doc_count"],
**{agg: date[agg]["value"] for agg in aggs_per_metric[metric_key]},
}
def extract_metric_results(
metric_or_variant: dict, metric_key: str
) -> Sequence[dict]:
return [
extract_date_stats(date, metric_key)
for date in metric_or_variant["dates"]["buckets"]
if date["doc_count"]
]
def extract_variant_results(metric: dict) -> dict:
metric_key = metric["key"]
return {
variant["key"]: extract_metric_results(variant, metric_key)
for variant in metric["variants"]["buckets"]
}
def extract_worker_results(worker: dict) -> dict:
return {
metric["key"]: extract_variant_results(metric)
if split_by_variant
else extract_metric_results(metric, metric["key"])
for metric in worker["metrics"]["buckets"]
}
return {
worker["key"]: extract_worker_results(worker)
for worker in data["aggregations"]["workers"]["buckets"]
}
def get_activity_report(
self,
company_id: str,
from_date: float,
to_date: float,
interval: int,
active_only: bool,
) -> Sequence[dict]:
"""
Get statistics for company workers metrics in the specified time range
Returned as date histograms for different aggregation types
grouped by worker, metric type (and optionally metric variant)
Note: all the statistics are retrieved using one ES query
"""
if from_date >= to_date:
raise bad_request.FieldsValueError("from_date must be less than to_date")
must = [QueryBuilder.dates_range(from_date, to_date)]
if active_only:
must.append({"exists": {"field": "task"}})
es_req = {
"size": 0,
"aggs": {
"dates": {
"date_histogram": {
"field": "timestamp",
"interval": f"{interval}s",
},
"aggs": {"workers_count": {"cardinality": {"field": "worker"}}},
}
},
"query": {"bool": {"must": must}},
}
with translate_errors_context(), TimingContext(
"es", "get_worker_activity_report"
):
data = self._search_company_stats(company_id, es_req)
if "aggregations" not in data:
return {}
ret = [
dict(date=date["key"], count=date["workers_count"]["value"])
for date in data["aggregations"]["dates"]["buckets"]
]
if ret and ret[-1]["date"] > (to_date - 0.9 * interval):
# remove last interval if it's incomplete. Allow 10% tolerance
ret.pop()
return ret

View File

@@ -1,4 +1,5 @@
import logging
import os
from functools import reduce
from os import getenv
from os.path import expandvars
@@ -16,6 +17,9 @@ DEFAULT_EXTRA_CONFIG_PATH = "/opt/trains/config"
EXTRA_CONFIG_PATH_ENV_KEY = "TRAINS_CONFIG_DIR"
EXTRA_CONFIG_PATH_SEP = ":"
EXTRA_CONFIG_VALUES_ENV_KEY_SEP = "__"
EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX = f"TRAINS{EXTRA_CONFIG_VALUES_ENV_KEY_SEP}"
class BasicConfig:
NotSet = object()
@@ -46,6 +50,20 @@ class BasicConfig:
path = ".".join((self.prefix, Path(name).stem))
return logging.getLogger(path)
def _read_extra_env_config_values(self):
""" Loads extra configuration from environment-injected values """
result = ConfigTree()
prefix = EXTRA_CONFIG_VALUES_ENV_KEY_PREFIX
keys = sorted(k for k in os.environ if k.startswith(prefix))
for key in keys:
path = key[len(prefix) :].replace(EXTRA_CONFIG_VALUES_ENV_KEY_SEP, ".")
result = ConfigTree.merge_configs(
result, ConfigFactory.parse_string(f"{path}: {os.environ[key]}")
)
return result
def _read_env_paths(self, key):
value = getenv(EXTRA_CONFIG_PATH_ENV_KEY, DEFAULT_EXTRA_CONFIG_PATH)
if value is None:
@@ -64,12 +82,17 @@ class BasicConfig:
def _load(self, verbose=True):
extra_config_paths = self._read_env_paths(EXTRA_CONFIG_PATH_ENV_KEY) or []
extra_config_values = self._read_extra_env_config_values()
configs = [
self._read_recursive(path, verbose=verbose)
for path in [self.folder] + extra_config_paths
]
self._config = reduce(
lambda config, path: ConfigTree.merge_configs(
config, self._read_recursive(path, verbose=verbose), copy_trees=True
lambda last, config: ConfigTree.merge_configs(
last, config, copy_trees=True
),
[self.folder] + extra_config_paths,
configs + [extra_config_values],
ConfigTree(),
)

View File

@@ -21,12 +21,19 @@
version {
required: false
default: 1.0
# if set then calls to endpoints with the version
# greater that the current max version will be rejected
check_max_version: false
}
mongo {
# controls whether FieldDoesNotExist exception will be raised for any extra attribute existing in stored data
# but not declared in a data model
strict: false
aggregate {
allow_disk_use: true
}
}
auth {
@@ -66,7 +73,46 @@
cors {
origins: "*"
# Not supported when origins is "*"
supports_credentials: true
}
default_company: "d1bd92a3b039400cbafc60a7a5b1e52b"
workers {
# Auto-register unknown workers on status reports and other calls
auto_register: true
# Timeout in seconds on task status update. If exceeded
# then task can be stopped without communicating to the worker
task_update_timeout: 600
}
check_for_updates {
enabled: true
# Check for updates every 24 hours
check_interval_sec: 86400
url: "https://updates.trains.allegro.ai/updates"
component_name: "trains-server"
# GET request timeout
request_timeout_sec: 3.0
}
statistics {
# Note: statistics are sent ONLY if the user has actively opted-in
supported: true
url: "https://updates.trains.allegro.ai/stats"
report_interval_hours: 24
agent_relevant_threshold_days: 30
max_retries: 5
max_backoff_sec: 5
}
}

View File

@@ -9,6 +9,17 @@ elastic {
}
index_version: "1"
}
workers {
hosts: [{host:"127.0.0.1", port:9200}]
args {
timeout: 60
dead_timeout: 10
max_retries: 5
retry_on_timeout: true
}
index_version: "1"
}
}
mongo {
@@ -19,3 +30,11 @@ mongo {
host: "mongodb://127.0.0.1:27017/auth"
}
}
redis {
workers {
host: "127.0.0.1"
port: 6379
db: 4
}
}

View File

@@ -1,5 +1,6 @@
from functools import lru_cache
from pathlib import Path
from os import getenv
root = Path(__file__).parent.parent
@@ -18,3 +19,25 @@ def get_version():
return (root / "VERSION").read_text().strip()
except FileNotFoundError:
return ""
@lru_cache()
def get_commit_number():
try:
return (root / "COMMIT").read_text().strip()
except FileNotFoundError:
return ""
@lru_cache()
def get_deployment_type() -> str:
value = getenv("TRAINS_SERVER_DEPLOYMENT_TYPE")
if value:
return value
try:
value = (root / "DEPLOY").read_text().strip()
except FileNotFoundError:
pass
return value or "manual"

View File

@@ -1,3 +1,7 @@
from os import getenv
from boltons.iterutils import first
from furl import furl
from jsonmodels import models
from jsonmodels.errors import ValidationError
from jsonmodels.fields import StringField
@@ -8,9 +12,16 @@ from config import config
from .defs import Database
from .utils import get_items
log = config.logger(__file__)
log = config.logger("database")
strict = config.get('apiserver.mongo.strict', True)
strict = config.get("apiserver.mongo.strict", True)
OVERRIDE_HOST_ENV_KEY = (
"TRAINS_MONGODB_SERVICE_HOST",
"MONGODB_SERVICE_HOST",
"MONGODB_SERVICE_SERVICE_HOST",
)
OVERRIDE_PORT_ENV_KEY = ("TRAINS_MONGODB_SERVICE_PORT", "MONGODB_SERVICE_PORT")
_entries = []
@@ -21,28 +32,47 @@ class DatabaseEntry(models.Base):
@property
def health_alias(self):
return '__health__' + self.alias
return "__health__" + self.alias
def initialize():
db_entries = config.get('hosts.mongo', {})
db_entries = config.get("hosts.mongo", {})
missing = []
log.info('Initializing database connections')
log.info("Initializing database connections")
override_hostname = first(map(getenv, OVERRIDE_HOST_ENV_KEY), None)
if override_hostname:
log.info(f"Using override mongodb host {override_hostname}")
override_port = first(map(getenv, OVERRIDE_PORT_ENV_KEY), None)
if override_port:
log.info(f"Using override mongodb port {override_port}")
for key, alias in get_items(Database).items():
if key not in db_entries:
missing.append(key)
continue
entry = DatabaseEntry(alias=alias, **db_entries.get(key))
if override_hostname:
entry.host = furl(entry.host).set(host=override_hostname).url
if override_port:
entry.host = furl(entry.host).set(port=override_port).url
try:
entry.validate()
log.info('Registering connection to %(alias)s (%(host)s)' % entry.to_struct())
log.info(
"Registering connection to %(alias)s (%(host)s)" % entry.to_struct()
)
register_connection(alias=alias, host=entry.host)
_entries.append(entry)
except ValidationError as ex:
raise Exception('Invalid database entry `%s`: %s' % (key, ex.args[0]))
raise Exception("Invalid database entry `%s`: %s" % (key, ex.args[0]))
if missing:
raise ValueError('Missing database configuration for %s' % ', '.join(missing))
raise ValueError("Missing database configuration for %s" % ", ".join(missing))
def get_entries():

View File

@@ -1,5 +1,6 @@
import re
from operator import itemgetter
from sys import maxsize
from typing import Type, Tuple
import six
from mongoengine import (
@@ -11,6 +12,7 @@ from mongoengine import (
SortedListField,
MapField,
DictField,
DynamicField,
)
@@ -88,104 +90,6 @@ class CustomFloatField(FloatField):
self.error("Float value must be greater than %s" % str(self.greater_than))
# TODO: bucket name should be at most 63 characters....
aws_s3_bucket_only_regex = (
r"^s3://"
r"(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w)" # bucket name
)
aws_s3_url_with_bucket_regex = (
r"^s3://"
r"(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w)" # bucket name
r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?))" # domain...
)
non_aws_s3_regex = (
r"^s3://"
r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?)|" # domain...
r"localhost|" # localhost...
r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}|" # ...or ipv4
r"\[?[A-F0-9]*:[A-F0-9:]+\]?)" # ...or ipv6
r"(?::\d+)?" # optional port
r"(?:/(?:(?:\w[A-Z0-9\-]+\w)\.)*(?:\w[A-Z0-9\-]+\w))" # bucket name
)
google_gs_bucket_only_regex = (
r"^gs://"
r"(?:(?:\w[A-Z0-9\-_]+\w)\.)*(?:\w[A-Z0-9\-_]+\w)" # bucket name
)
file_regex = r"^file://"
generic_url_regex = (
r"^%s://" # scheme placeholder
r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}(?<!-)\.?)|" # domain...
r"localhost|" # localhost...
r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}|" # ...or ipv4
r"\[?[A-F0-9]*:[A-F0-9:]+\]?)" # ...or ipv6
r"(?::\d+)?" # optional port
)
path_suffix = r"(?:/?|[/?]\S+)$"
file_path_suffix = r"(?:/\S*[^/]+)$"
class _RegexURLField(StringField):
_regex = []
def __init__(self, regex, **kwargs):
super(_RegexURLField, self).__init__(**kwargs)
regex = regex if isinstance(regex, (tuple, list)) else [regex]
self._regex = [
re.compile(e, re.IGNORECASE) if isinstance(e, six.string_types) else e
for e in regex
]
def validate(self, value):
# Check first if the scheme is valid
if not any(regex for regex in self._regex if regex.match(value)):
self.error("Invalid URL: {}".format(value))
return
class OutputDestinationField(_RegexURLField):
""" A field representing task output URL """
schemes = ["s3", "gs", "file"]
_expressions = (
aws_s3_bucket_only_regex + path_suffix,
aws_s3_url_with_bucket_regex + path_suffix,
non_aws_s3_regex + path_suffix,
google_gs_bucket_only_regex + path_suffix,
file_regex + path_suffix,
)
def __init__(self, **kwargs):
super(OutputDestinationField, self).__init__(self._expressions, **kwargs)
class SupportedURLField(_RegexURLField):
""" A field representing a model URL """
schemes = ["s3", "gs", "file", "http", "https"]
_expressions = tuple(
pattern + file_path_suffix
for pattern in (
aws_s3_bucket_only_regex,
aws_s3_url_with_bucket_regex,
non_aws_s3_regex,
google_gs_bucket_only_regex,
file_regex,
(generic_url_regex % "http"),
(generic_url_regex % "https"),
)
)
def __init__(self, **kwargs):
super(SupportedURLField, self).__init__(self._expressions, **kwargs)
class StrippedStringField(StringField):
def __init__(
self, regex=None, max_length=None, min_length=None, strip_chars=None, **kwargs
@@ -235,3 +139,42 @@ class SafeDictField(DictField):
if contains_empty_key(value):
self.error("Empty keys are not allowed in a DictField")
class SafeSortedListField(SortedListField):
"""
SortedListField that does not raise an error in case items are not comparable
(in which case they will be sorted by their string representation)
"""
def to_mongo(self, *args, **kwargs):
try:
return super(SafeSortedListField, self).to_mongo(*args, **kwargs)
except TypeError:
return self._safe_to_mongo(*args, **kwargs)
def _safe_to_mongo(self, value, use_db_field=True, fields=None):
value = super(SortedListField, self).to_mongo(value, use_db_field, fields)
if self._ordering is not None:
def key(v): return str(itemgetter(self._ordering)(v))
else:
key = str
return sorted(value, key=key, reverse=self._order_reverse)
class UnionField(DynamicField):
def __init__(self, types, *args, **kwargs):
super(UnionField, self).__init__(*args, **kwargs)
self.types: Tuple[Type] = tuple(types)
def validate(self, value, clean=True):
if not isinstance(value, self.types):
type_names = [t.__name__ for t in self.types]
expected = " or ".join(
filter(
None,
(", ".join(type_names[:-1]), type_names[-1]))
)
self.error(
f"Expected {expected}, got {type(value).__name__}: {value}"
)
super(UnionField, self).validate(value, clean)

View File

@@ -1,3 +1,5 @@
from enum import Enum
from mongoengine import Document, StringField
from apierrors import errors
@@ -54,3 +56,7 @@ def validate_id(cls, company, **kwargs):
**{name: obj_id for obj_id in missing for name in id_to_name[obj_id]}
)
class EntityVisibility(Enum):
active = "active"
archived = "archived"

View File

@@ -45,6 +45,7 @@ class Role(object):
class Credentials(EmbeddedDocument):
key = StringField(required=True)
secret = StringField(required=True)
last_used = DateTimeField()
class User(DbModelMixin, AuthDocument):

View File

@@ -1,11 +1,12 @@
import re
from collections import namedtuple
from functools import reduce
from typing import Collection
from typing import Collection, Sequence, Union
from boltons.iterutils import first
from dateutil.parser import parse as parse_datetime
from mongoengine import Q, Document
from six import string_types
from mongoengine import Q, Document, ListField, StringField
from pymongo.command_cursor import CommandCursor
from apierrors import errors
from config import config
@@ -13,7 +14,12 @@ from database.errors import MakeGetAllQueryError
from database.projection import project_dict, ProjectionHelper
from database.props import PropsMixin
from database.query import RegexQ, RegexWrapper
from database.utils import get_company_or_none_constraint, get_fields_with_attr
from database.utils import (
get_company_or_none_constraint,
get_fields_choices,
field_does_not_exist,
field_exists,
)
log = config.logger("dbmodel")
@@ -56,6 +62,7 @@ class GetMixin(PropsMixin):
_text_score = "$text_score"
_ordering_key = "order_by"
_search_text_key = "search_text"
_multi_field_param_sep = "__"
_multi_field_param_prefix = {
@@ -68,7 +75,7 @@ class GetMixin(PropsMixin):
def __init__(
self,
pattern_fields=("name",),
list_fields=("tags", "id"),
list_fields=("tags", "system_tags", "id"),
datetime_fields=None,
fields=None,
):
@@ -215,6 +222,24 @@ class GetMixin(PropsMixin):
return get_company_or_none_constraint(company)
return Q(company=company)
@classmethod
def validate_order_by(cls, parameters, search_text) -> Sequence:
"""
Validate and extract order_by params as a list
"""
order_by = parameters.get(cls._ordering_key)
if not order_by:
return []
order_by = order_by if isinstance(order_by, list) else [order_by]
order_by = [cls._text_score if x == "@text_score" else x for x in order_by]
if not search_text and cls._text_score in order_by:
raise errors.bad_request.FieldsValueError(
"text score cannot be used in order_by when search text is not used"
)
return order_by
@classmethod
def validate_paging(
cls, parameters=None, default_page=None, default_page_size=None
@@ -332,8 +357,9 @@ class GetMixin(PropsMixin):
`@text_score` keyword. A text index must be defined on the document type, otherwise an error will
be raised.
:param return_dicts: Return a list of dictionaries. If True, a list of dicts is returned (if projection was
requested, each contains only the requested projection).
If False, a QuerySet object is returned (lazy evaluated)
requested, each contains only the requested projection). If False, a QuerySet object is returned
(lazy evaluated). If return_dicts is requested then the entities with the None value in order_by field
are returned last in the ordering.
:param company: Company ID (required)
:param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
:param query_dict: If provided, passed to prepare_query() along with all of the relevant arguments to produce
@@ -356,17 +382,19 @@ class GetMixin(PropsMixin):
q = cls._prepare_perm_query(company, allow_public=allow_public)
_query = (q & query) if query else q
if return_dicts:
return cls._get_many_override_none_ordering(
query=_query,
parameters=parameters,
override_projection=override_projection,
)
return cls._get_many_no_company(
query=_query,
parameters=parameters,
override_projection=override_projection,
return_dicts=return_dicts,
query=_query, parameters=parameters, override_projection=override_projection
)
@classmethod
def _get_many_no_company(
cls, query, parameters=None, override_projection=None, return_dicts=True
):
def _get_many_no_company(cls, query, parameters=None, override_projection=None):
"""
Fetch all documents matching a provided query.
This is a company-less version for internal uses. We assume the caller has either added any necessary
@@ -375,44 +403,25 @@ class GetMixin(PropsMixin):
NOTE: BE VERY CAREFUL WITH THIS CALL, as it allows returning data across companies.
:param query: Query object (mongoengine.Q)
:param return_dicts: Return a list of dictionaries. If True, a list of dicts is returned (if projection was
requested, each contains only the requested projection).
If False, a QuerySet object is returned (lazy evaluated)
:param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
:param override_projection: A list of projection fields overriding any projection specified in the `param_dict`
argument
"""
parameters = parameters or {}
if not query:
raise ValueError("query or call_data must be provided")
parameters = parameters or {}
search_text = parameters.get(cls._search_text_key)
order_by = cls.validate_order_by(parameters=parameters, search_text=search_text)
page, page_size = cls.validate_paging(parameters=parameters)
order_by = parameters.get(cls._ordering_key)
if order_by:
order_by = order_by if isinstance(order_by, list) else [order_by]
order_by = [cls._text_score if x == "@text_score" else x for x in order_by]
search_text = parameters.get("search_text")
only = cls.get_projection(parameters, override_projection)
if not search_text and order_by and cls._text_score in order_by:
raise errors.bad_request.FieldsValueError(
"text score cannot be used in order_by when search text is not used"
)
qs = cls.objects(query)
if search_text:
qs = qs.search_text(search_text)
if order_by:
# add ordering
qs = (
qs.order_by(order_by)
if isinstance(order_by, string_types)
else qs.order_by(*order_by)
)
qs = qs.order_by(*order_by)
if only:
# add projection
qs = qs.only(*only)
@@ -424,10 +433,91 @@ class GetMixin(PropsMixin):
# add paging
qs = qs.skip(page * page_size).limit(page_size)
if return_dicts:
return [obj.to_proper_dict(only=only) for obj in qs]
return qs
@classmethod
def _get_many_override_none_ordering(
cls: Union[Document, "GetMixin"],
query: Q = None,
parameters: dict = None,
override_projection: Collection[str] = None,
) -> Sequence[dict]:
"""
Fetch all documents matching a provided query. For the first order by field
the None values are sorted in the end regardless of the sorting order.
This is a company-less version for internal uses. We assume the caller has either added any necessary
constraints to the query or that no constraints are required.
NOTE: BE VERY CAREFUL WITH THIS CALL, as it allows returning data across companies.
:param query: Query object (mongoengine.Q)
:param parameters: Parameters dict from which paging ordering and searching parameters are extracted.
:param override_projection: A list of projection fields overriding any projection specified in the `param_dict`
argument
"""
if not query:
raise ValueError("query or call_data must be provided")
parameters = parameters or {}
search_text = parameters.get(cls._search_text_key)
order_by = cls.validate_order_by(parameters=parameters, search_text=search_text)
page, page_size = cls.validate_paging(parameters=parameters)
only = cls.get_projection(parameters, override_projection)
query_sets = [cls.objects(query)]
if order_by:
order_field = first(
field for field in order_by if not field.startswith("$")
)
if (
order_field
and not order_field.startswith("-")
and "[" not in order_field
):
params = {}
mongo_field = order_field.replace(".", "__")
if mongo_field in cls.get_field_names_for_type(of_type=ListField):
params["is_list"] = True
elif mongo_field in cls.get_field_names_for_type(of_type=StringField):
params["empty_value"] = ""
non_empty = query & field_exists(mongo_field, **params)
empty = query & field_does_not_exist(mongo_field, **params)
query_sets = [cls.objects(non_empty), cls.objects(empty)]
query_sets = [qs.order_by(*order_by) for qs in query_sets]
if search_text:
query_sets = [qs.search_text(search_text) for qs in query_sets]
if only:
# add projection
query_sets = [qs.only(*only) for qs in query_sets]
else:
exclude = set(cls.get_exclude_fields())
if exclude:
query_sets = [qs.exclude(*exclude) for qs in query_sets]
if page is None or not page_size:
return [obj.to_proper_dict(only=only) for qs in query_sets for obj in qs]
# add paging
ret = []
start = page * page_size
for qs in query_sets:
qs_size = qs.count()
if qs_size < start:
start -= qs_size
continue
ret.extend(
obj.to_proper_dict(only=only) for obj in qs.skip(start).limit(page_size)
)
if len(ret) >= page_size:
break
start = 0
page_size -= len(ret)
return ret
@classmethod
def get_for_writing(
cls, *args, _only: Collection[str] = None, **kwargs
@@ -464,8 +554,8 @@ class UpdateMixin(object):
def user_set_allowed(cls):
res = getattr(cls, "__user_set_allowed_fields", None)
if res is None:
res = cls.__user_set_allowed_fields = dict(
get_fields_with_attr(cls, "user_set_allowed")
res = cls.__user_set_allowed_fields = get_fields_choices(
cls, "user_set_allowed"
)
return res
@@ -503,7 +593,24 @@ class UpdateMixin(object):
class DbModelMixin(GetMixin, ProperDictMixin, UpdateMixin):
""" Provide convenience methods for a subclass of mongoengine.Document """
pass
@classmethod
def aggregate(
cls: Document, *pipeline: dict, allow_disk_use=None, **kwargs
) -> CommandCursor:
"""
Aggregate objects of this document class according to the provided pipeline.
:param pipeline: a list of dictionaries describing the pipeline stages
:param allow_disk_use: if True, allow the server to use disk space if aggregation query cannot fit in memory.
If None, default behavior will be used (see apiserver.conf/mongo/aggregate/allow_disk_use)
:param kwargs: additional keyword arguments passed to mongoengine
:return:
"""
kwargs.update(
allowDiskUse=allow_disk_use
if allow_disk_use is not None
else config.get("apiserver.mongo.aggregate.allow_disk_use", True)
)
return cls.objects.aggregate(*pipeline, **kwargs)
def validate_id(cls, company, **kwargs):

View File

@@ -1,23 +1,36 @@
from mongoengine import Document, EmbeddedDocument, EmbeddedDocumentField, StringField, Q
from mongoengine import (
Document,
EmbeddedDocument,
EmbeddedDocumentField,
StringField,
Q,
BooleanField,
DateTimeField,
)
from database import Database, strict
from database.fields import StrippedStringField
from database.model import DbModelMixin
class ReportStatsOption(EmbeddedDocument):
enabled = BooleanField(default=False) # opt-in for statistics reporting
enabled_version = StringField() # server version when enabled
enabled_time = DateTimeField() # time when enabled
enabled_user = StringField() # ID of user who enabled
class CompanyDefaults(EmbeddedDocument):
cluster = StringField()
stats_option = EmbeddedDocumentField(ReportStatsOption, default=ReportStatsOption)
class Company(DbModelMixin, Document):
meta = {
'db_alias': Database.backend,
'strict': strict,
}
meta = {"db_alias": Database.backend, "strict": strict}
id = StringField(primary_key=True)
name = StrippedStringField(unique=True, min_length=3)
defaults = EmbeddedDocumentField(CompanyDefaults)
defaults = EmbeddedDocumentField(CompanyDefaults, default=CompanyDefaults)
@classmethod
def _prepare_perm_query(cls, company, allow_public=False):

View File

@@ -1,7 +1,7 @@
from mongoengine import Document, StringField, DateTimeField, ListField, BooleanField
from database import Database, strict
from database.fields import SupportedURLField, StrippedStringField, SafeDictField
from database.fields import StrippedStringField, SafeDictField
from database.model import DbModelMixin
from database.model.model_labels import ModelLabels
from database.model.company import Company
@@ -48,7 +48,8 @@ class Model(DbModelMixin, Document):
task = StringField(reference_field=Task)
comment = StringField(user_set_allowed=True)
tags = ListField(StringField(required=True), user_set_allowed=True)
uri = SupportedURLField(default='', user_set_allowed=True)
system_tags = ListField(StringField(required=True), user_set_allowed=True)
uri = StrippedStringField(default='', user_set_allowed=True)
framework = StringField()
design = SafeDictField()
labels = ModelLabels()

View File

@@ -1,7 +1,7 @@
from mongoengine import StringField, DateTimeField, ListField
from database import Database, strict
from database.fields import OutputDestinationField, StrippedStringField
from database.fields import StrippedStringField
from database.model import AttributedDocument
from database.model.base import GetMixin
@@ -9,7 +9,8 @@ from database.model.base import GetMixin
class Project(AttributedDocument):
get_all_query_options = GetMixin.QueryParameterOptions(
pattern_fields=("name", "description"), list_fields=("tags", "id")
pattern_fields=("name", "description"),
list_fields=("tags", "system_tags", "id"),
)
meta = {
@@ -34,6 +35,7 @@ class Project(AttributedDocument):
)
description = StringField(required=True)
created = DateTimeField(required=True)
tags = ListField(StringField(required=True), default=list)
default_output_destination = OutputDestinationField()
tags = ListField(StringField(required=True))
system_tags = ListField(StringField(required=True))
default_output_destination = StrippedStringField()
last_update = DateTimeField()

View File

@@ -0,0 +1,47 @@
from mongoengine import (
Document,
EmbeddedDocument,
StringField,
DateTimeField,
EmbeddedDocumentListField,
ListField,
)
from database import Database, strict
from database.fields import StrippedStringField
from database.model import DbModelMixin
from database.model.base import ProperDictMixin, GetMixin
from database.model.company import Company
from database.model.task.task import Task
class Entry(EmbeddedDocument, ProperDictMixin):
""" Entry representing a task waiting in the queue """
task = StringField(required=True, reference_field=Task)
''' Task ID '''
added = DateTimeField(required=True)
''' Added to the queue '''
class Queue(DbModelMixin, Document):
get_all_query_options = GetMixin.QueryParameterOptions(
pattern_fields=("name",),
list_fields=("tags", "system_tags", "id"),
)
meta = {
'db_alias': Database.backend,
'strict': strict,
}
id = StringField(primary_key=True)
name = StrippedStringField(
required=True, unique_with="company", min_length=3, user_set_allowed=True
)
company = StringField(required=True, reference_field=Company)
created = DateTimeField(required=True)
tags = ListField(StringField(required=True), default=list, user_set_allowed=True)
system_tags = ListField(StringField(required=True), user_set_allowed=True)
entries = EmbeddedDocumentListField(Entry, default=list)
last_update = DateTimeField()

View File

@@ -0,0 +1,57 @@
from typing import Any, Optional, Sequence, Tuple
from mongoengine import Document, StringField, DynamicField, Q
from mongoengine.errors import NotUniqueError
from database import Database, strict
from database.model import DbModelMixin
class Settings(DbModelMixin, Document):
meta = {
"db_alias": Database.backend,
"strict": strict,
}
key = StringField(primary_key=True)
value = DynamicField()
@classmethod
def get_by_key(cls, key: str, default: Optional[Any] = None, sep: str = ".") -> Any:
key = key.strip(sep)
res = Settings.objects(key=key).first()
if not res:
return default
return res.value
@classmethod
def get_by_prefix(
cls, key_prefix: str, default: Optional[Any] = None, sep: str = "."
) -> Sequence[Tuple[str, Any]]:
key_prefix = key_prefix.strip(sep)
query = Q(key=key_prefix) | Q(key__startswith=key_prefix + sep)
res = Settings.objects(query)
if not res:
return default
return [(x.key, x.value) for x in res]
@classmethod
def set_or_add_value(cls, key: str, value: Any, sep: str = ".") -> bool:
""" Sets a new value or adds a new key/value setting (if key does not exist) """
key = key.strip(sep)
res = Settings.objects(key=key).update(key=key, value=value, upsert=True)
# if Settings.objects(key=key).only("key"):
#
# else:
# res = Settings(key=key, value=value).save()
return bool(res)
@classmethod
def add_value(cls, key: str, value: Any, sep: str = ".") -> bool:
""" Adds a new key/value settings. Fails if key already exists. """
key = key.strip(sep)
try:
res = Settings(key=key, value=value).save(force_insert=True)
return bool(res)
except NotUniqueError:
return False

View File

@@ -1,14 +1,14 @@
from mongoengine import EmbeddedDocument, StringField, DateTimeField, LongField, DynamicField
from mongoengine import EmbeddedDocument, StringField, DynamicField
class MetricEvent(EmbeddedDocument):
metric = StringField(required=True, )
variant = StringField(required=True)
type = StringField(required=True)
timestamp = DateTimeField(default=0, required=True)
iter = LongField()
value = DynamicField(required=True)
meta = {
# For backwards compatibility reasons
'strict': False,
}
@classmethod
def from_dict(cls, **kwargs):
return cls(**{k: v for k, v in kwargs.items() if k in cls._fields})
metric = StringField(required=True)
variant = StringField(required=True)
value = DynamicField(required=True)
min_value = DynamicField() # for backwards compatibility reasons
max_value = DynamicField() # for backwards compatibility reasons

View File

@@ -1,7 +1,7 @@
from mongoengine import EmbeddedDocument, StringField
from database.utils import get_options
from database.fields import OutputDestinationField
from database.fields import StrippedStringField
from database.utils import get_options
class Result(object):
@@ -10,7 +10,7 @@ class Result(object):
class Output(EmbeddedDocument):
destination = OutputDestinationField()
destination = StrippedStringField()
model = StringField(reference_field='Model')
error = StringField(user_set_allowed=True)
result = StringField(choices=get_options(Result))

View File

@@ -1,5 +1,3 @@
from enum import Enum
from mongoengine import (
StringField,
EmbeddedDocumentField,
@@ -7,10 +5,18 @@ from mongoengine import (
DateTimeField,
IntField,
ListField,
LongField,
)
from database import Database, strict
from database.fields import StrippedStringField, SafeMapField, SafeDictField
from database.fields import (
StrippedStringField,
SafeMapField,
SafeDictField,
UnionField,
EmbeddedDocumentSortedListField,
SafeSortedListField,
)
from database.model import AttributedDocument
from database.model.model_labels import ModelLabels
from database.model.project import Project
@@ -22,27 +28,28 @@ DEFAULT_LAST_ITERATION = 0
class TaskStatus(object):
created = 'created'
in_progress = 'in_progress'
stopped = 'stopped'
publishing = 'publishing'
published = 'published'
closed = 'closed'
failed = 'failed'
completed = 'completed'
unknown = 'unknown'
created = "created"
queued = "queued"
in_progress = "in_progress"
stopped = "stopped"
publishing = "publishing"
published = "published"
closed = "closed"
failed = "failed"
completed = "completed"
unknown = "unknown"
class TaskStatusMessage(object):
stopping = 'stopping'
stopping = "stopping"
class TaskTags(object):
development = 'development'
class TaskSystemTags(object):
development = "development"
class Script(EmbeddedDocument):
binary = StringField(default='python')
binary = StringField(default="python")
repository = StringField(required=True)
tag = StringField()
branch = StringField()
@@ -53,51 +60,70 @@ class Script(EmbeddedDocument):
diff = StringField()
class ArtifactTypeData(EmbeddedDocument):
preview = StringField()
content_type = StringField()
data_hash = StringField()
class Artifact(EmbeddedDocument):
key = StringField(required=True)
type = StringField(required=True)
mode = StringField(choices=("input", "output"), default="output")
uri = StringField()
hash = StringField()
content_size = LongField()
timestamp = LongField()
type_data = EmbeddedDocumentField(ArtifactTypeData)
display_data = SafeSortedListField(ListField(UnionField((int, float, str))))
class Execution(EmbeddedDocument):
test_split = IntField(default=0)
parameters = SafeDictField(default=dict)
model = StringField(reference_field='Model')
model_desc = SafeMapField(StringField(default=''))
model = StringField(reference_field="Model")
model_desc = SafeMapField(StringField(default=""))
model_labels = ModelLabels()
framework = StringField()
artifacts = EmbeddedDocumentSortedListField(Artifact)
docker_cmd = StringField()
queue = StringField()
''' Queue ID where task was queued '''
""" Queue ID where task was queued """
class TaskType(object):
training = 'training'
testing = 'testing'
training = "training"
testing = "testing"
class Task(AttributedDocument):
meta = {
'db_alias': Database.backend,
'strict': strict,
'indexes': [
'created',
'started',
'completed',
"db_alias": Database.backend,
"strict": strict,
"indexes": [
"created",
"started",
"completed",
{
'name': '%s.task.main_text_index' % Database.backend,
'fields': [
'$name',
'$id',
'$comment',
'$execution.model',
'$output.model',
'$script.repository',
'$script.entry_point',
"name": "%s.task.main_text_index" % Database.backend,
"fields": [
"$name",
"$id",
"$comment",
"$execution.model",
"$output.model",
"$script.repository",
"$script.entry_point",
],
'default_language': 'english',
'weights': {
'name': 10,
'id': 10,
'comment': 10,
'execution.model': 2,
'output.model': 2,
'script.repository': 1,
'script.entry_point': 1,
"default_language": "english",
"weights": {
"name": 10,
"id": 10,
"comment": 10,
"execution.model": 2,
"output.model": 2,
"script.repository": 1,
"script.entry_point": 1,
},
},
],
@@ -123,12 +149,10 @@ class Task(AttributedDocument):
output = EmbeddedDocumentField(Output, default=Output)
execution: Execution = EmbeddedDocumentField(Execution, default=Execution)
tags = ListField(StringField(required=True), user_set_allowed=True)
system_tags = ListField(StringField(required=True), user_set_allowed=True)
script = EmbeddedDocumentField(Script)
last_worker = StringField()
last_worker_report = DateTimeField()
last_update = DateTimeField()
last_iteration = IntField(default=DEFAULT_LAST_ITERATION)
last_metrics = SafeMapField(field=SafeMapField(EmbeddedDocumentField(MetricEvent)))
class TaskVisibility(Enum):
active = 'active'
archived = 'archived'

View File

@@ -0,0 +1,18 @@
from mongoengine import Document, DateTimeField, StringField
from database import Database, strict
from database.model import DbModelMixin
class Version(DbModelMixin, Document):
meta = {
"collection": "versions", # custom collection name ('version' is not a proper collection name...)
"db_alias": Database.backend, # although we'll use this model for all databases, a default must be defined
"strict": strict,
"indexes": [("-created", "-num")],
}
id = StringField(primary_key=True)
num = StringField(required=True)
created = DateTimeField(required=True)
desc = StringField()

View File

@@ -1,13 +1,17 @@
import threading
from concurrent.futures import ThreadPoolExecutor
from itertools import groupby, chain
from typing import Sequence, Dict, Callable, Tuple, Any, Type
import dpath
import dpath.path
from apierrors import errors
from database.props import PropsMixin
SEP = "."
def project_dict(data, projection, separator='.'):
def project_dict(data, projection, separator=SEP):
"""
Project partial data from a dictionary into a new dictionary
:param data: Input dictionary
@@ -30,19 +34,27 @@ def project_dict(data, projection, separator='.'):
if path_part not in dst:
dst[path_part] = [{} for _ in range(len(src_part))]
elif not isinstance(dst[path_part], (list, tuple)):
raise TypeError('Incompatible destination type %s for %s (list expected)'
% (type(dst), separator.join(path_parts[:depth + 1])))
raise TypeError(
"Incompatible destination type %s for %s (list expected)"
% (type(dst), separator.join(path_parts[: depth + 1]))
)
elif not len(dst[path_part]) == len(src_part):
raise ValueError('Destination list length differs from source length for %s'
% separator.join(path_parts[:depth + 1]))
raise ValueError(
"Destination list length differs from source length for %s"
% separator.join(path_parts[: depth + 1])
)
dst[path_part] = [copy_path(path_parts[depth + 1:], s, d)
for s, d in zip(src_part, dst[path_part])]
dst[path_part] = [
copy_path(path_parts[depth + 1:], s, d)
for s, d in zip(src_part, dst[path_part])
]
return destination
else:
raise TypeError('Unsupported projection type %s for %s'
% (type(src), separator.join(path_parts[:depth + 1])))
raise TypeError(
"Unsupported projection type %s for %s"
% (type(src), separator.join(path_parts[: depth + 1]))
)
last_part = path_parts[-1]
dst[last_part] = src[last_part]
@@ -53,12 +65,35 @@ def project_dict(data, projection, separator='.'):
for projection_path in sorted(projection):
copy_path(
path_parts=projection_path.split(separator),
source=data,
destination=result)
path_parts=projection_path.split(separator), source=data, destination=result
)
return result
class _ReferenceProxy(dict):
def __init__(self, id):
super(_ReferenceProxy, self).__init__(**({"id": id} if id else {}))
class _ProxyManager:
lock = threading.Lock()
def __init__(self):
self._proxies: Dict[str, _ReferenceProxy] = {}
def add(self, id):
with self.lock:
proxy = self._proxies.get(id)
if proxy is None:
proxy = self._proxies[id] = _ReferenceProxy(id)
return proxy
def update(self, result):
proxy = self._proxies.get(result.get("id"))
if proxy is not None:
proxy.update(result)
class ProjectionHelper(object):
pool = ThreadPoolExecutor()
@@ -72,6 +107,11 @@ class ProjectionHelper(object):
self._doc_cls = doc_cls
self._doc_projection = None
self._ref_projection = None
self._proxy_manager = _ProxyManager()
# Cached dpath paths for each of the result documents
self._cached_results_paths: Dict[int, Sequence[Tuple[Any, Type]]] = {}
self._parse_projection(projection)
def _collect_projection_fields(self, doc_cls, projection):
@@ -81,8 +121,12 @@ class ProjectionHelper(object):
:param projection: List of projection fields
:return: A tuple of document projection and reference fields information
"""
doc_projection = set() # Projection fields for this class (used in the main query)
ref_projection_info = [] # Projection information for reference fields (used in join queries)
doc_projection = (
set()
) # Projection fields for this class (used in the main query)
ref_projection_info = (
[]
) # Projection information for reference fields (used in join queries)
for field in projection:
for ref_field, ref_field_cls in doc_cls.get_reference_fields().items():
if not field.startswith(ref_field):
@@ -93,7 +137,7 @@ class ProjectionHelper(object):
# use '<reference field name>.*')
continue
subfield = field[len(ref_field):]
if not subfield.startswith('.'):
if not subfield.startswith(SEP):
# Starts with something that looks like a reference field, but isn't
continue
@@ -103,10 +147,12 @@ class ProjectionHelper(object):
# Not a reference field, just add to the top-level projection
# We strip any trailing '*' since it means nothing for simple fields and for embedded documents
orig_field = field
if field.endswith('.*'):
if field.endswith(".*"):
field = field[:-2]
if not field:
raise errors.bad_request.InvalidFields(field=orig_field, object=doc_cls.__name__)
raise errors.bad_request.InvalidFields(
field=orig_field, object=doc_cls.__name__
)
doc_projection.add(field)
return doc_projection, ref_projection_info
@@ -124,12 +170,14 @@ class ProjectionHelper(object):
if not projection:
return [], {}
doc_projection, ref_projection_info = self._collect_projection_fields(doc_cls, projection)
doc_projection, ref_projection_info = self._collect_projection_fields(
doc_cls, projection
)
def normalize_cls_projection(cls_, fields):
""" Normalize projection for this class and group (expand *, for once) """
if '*' in fields:
return list(fields.difference('*').union(cls_.get_fields()))
if "*" in fields:
return list(fields.difference("*").union(cls_.get_fields()))
return list(fields)
def compute_ref_cls_projection(cls_, group):
@@ -143,12 +191,16 @@ class ProjectionHelper(object):
# Aggregate by reference field. We'll leave out '*' from the projected items since
ref_projection = {
ref_field: dict(cls=ref_cls, only=compute_ref_cls_projection(ref_cls, g))
for (ref_field, ref_cls), g in groupby(sorted(ref_projection_info, key=sort_key), sort_key)
for (ref_field, ref_cls), g in groupby(
sorted(ref_projection_info, key=sort_key), sort_key
)
}
# Make sure this doesn't contain any reference field we'll join anyway
# (i.e. in case only_fields=[project, project.name])
doc_projection = normalize_cls_projection(doc_cls, doc_projection.difference(ref_projection).union({'id'}))
doc_projection = normalize_cls_projection(
doc_cls, doc_projection.difference(ref_projection).union({"id"})
)
# Make sure that in case one or more field is a subfield of another field, we only use the the top-level field.
# This is done since in such a case, MongoDB will only use the most restrictive field (most nested field) and
@@ -158,13 +210,20 @@ class ProjectionHelper(object):
doc_projection = [
field
for field in doc_projection
if not any(field.startswith(f"{other_field}.") for other_field in projection_set - {field})
if not any(
field.startswith(f"{other_field}.")
for other_field in projection_set - {field}
)
]
# Make sure we didn't get any invalid projection fields for this class
invalid_fields = [f for f in doc_projection if f.split('.')[0] not in doc_cls.get_fields()]
invalid_fields = [
f for f in doc_projection if f.split(SEP)[0] not in doc_cls.get_fields()
]
if invalid_fields:
raise errors.bad_request.InvalidFields(fields=invalid_fields, object=doc_cls.__name__)
raise errors.bad_request.InvalidFields(
fields=invalid_fields, object=doc_cls.__name__
)
if ref_projection:
# Join mode - use both normal projection fields and top-level reference fields
@@ -178,11 +237,44 @@ class ProjectionHelper(object):
self._doc_projection = doc_projection
self._ref_projection = ref_projection
@staticmethod
def _search(doc_cls, obj, path, only_values=True):
""" Call dpath.search with yielded=True, collect result values """
def _search(
self,
doc_cls: PropsMixin,
obj: dict,
path: str,
factory: Callable[[str], dict] = None,
) -> Sequence[str]:
"""
Search for a path in the given object, return the list of values found for the
given path (multiple values may exist if the path is a glob expression)
:param doc_cls: The document class represented by the object
:param obj: Data object
:param path: Path to a leaf in the data object ("." separated, may contain "*")
(in case the path contains "*", there may be multiple values)
:param factory: If provided, replace each value found with an instance provided by the factory.
"""
norm_path = doc_cls.get_dpath_translated_path(path)
return [v if only_values else (k, v) for k, v in dpath.search(obj, norm_path, separator='.', yielded=True)]
globlist = norm_path.strip(SEP).split(SEP)
obj_paths = self._cached_results_paths.get(id(obj))
if obj_paths is None:
obj_paths = self._cached_results_paths[id(obj)] = list(
dpath.path.paths(obj, dirs=True, skip=True)
)
paths = [p for p in obj_paths if dpath.path.match(p, globlist)]
def search_and_replace(p: Sequence[Tuple[str, Type]]) -> Any:
parent = None
target = obj
for part in p:
parent = target
target = target[part[0]]
if parent and factory:
parent[p[-1][0]] = factory(target)
return target
return [search_and_replace(p) for p in paths]
def project(self, results, projection_func):
"""
@@ -197,28 +289,50 @@ class ProjectionHelper(object):
if ref_projection:
# Join mode - get results for each reference fields projection required (this is the join step)
# Note: this is a recursive step, so we support nested reference fields
# Note: this is a recursive step, so nested reference fields are supported
def do_projection(item):
ref_field_name, data = item
res = {}
ids = list(filter(None, set(chain.from_iterable(self._search(cls, res, ref_field_name)
for res in results))))
if ids:
doc_type = data['cls']
doc_only = list(filter(None, data['only']))
doc_only = list({'id'} | set(doc_only)) if doc_only else None
res = {r['id']: r for r in projection_func(doc_type=doc_type, projection=doc_only, ids=ids)}
data['res'] = res
def collect_ids(ref_field_name):
"""
Collect unique IDs for the given reference path from all result documents.
All collected IDs are replaced in the result dictionaries with a reference proxy generated by the
proxies manager to allow rapid update later on when projection results are obtained.
"""
all_ids = (
self._search(
cls, res, ref_field_name, factory=self._proxy_manager.add
)
for res in results
)
return list(filter(None, set(chain.from_iterable(all_ids))))
items = list(ref_projection.items())
if len(ref_projection) == 1:
do_projection(items[0])
else:
for _ in self.pool.map(do_projection, items):
# From ThreadPoolExecutor.map() documentation: If a call raises an exception then that exception
# will be raised when its value is retrieved from the map() iterator
pass
items = [
tup
for tup in (
(*item, collect_ids(item[0])) for item in ref_projection.items()
)
if tup[2]
]
if items:
def do_projection(item):
ref_field_name, data, ids = item
doc_type = data["cls"]
doc_only = list(filter(None, data["only"]))
doc_only = list({"id"} | set(doc_only)) if doc_only else None
for res in projection_func(
doc_type=doc_type, projection=doc_only, ids=ids
):
self._proxy_manager.update(res)
if len(ref_projection) == 1:
do_projection(items[0])
else:
for _ in self.pool.map(do_projection, items):
# From ThreadPoolExecutor.map() documentation: If a call raises an exception then that exception
# will be raised when its value is retrieved from the map() iterator
pass
def do_expand_reference_ids(result, skip_fields=None):
ref_fields = cls.get_reference_fields()
@@ -226,44 +340,18 @@ class ProjectionHelper(object):
ref_fields = set(ref_fields) - set(skip_fields)
self._expand_reference_fields(cls, result, ref_fields)
def merge_projection_result(result):
for ref_field_name, data in ref_projection.items():
res = data.get('res')
if not res:
self._expand_reference_fields(cls, result, [ref_field_name])
continue
ref_ids = self._search(cls, result, ref_field_name, only_values=False)
if not ref_ids:
continue
for path, value in ref_ids:
obj = res.get(value) or {'id': value}
dpath.new(result, path, obj, separator='.')
# any reference field not projected should be expanded
do_expand_reference_ids(result, skip_fields=list(ref_projection))
update_func = merge_projection_result if ref_projection else \
do_expand_reference_ids if self._should_expand_reference_ids else None
if update_func:
# any reference field not projected should be expanded
if self._should_expand_reference_ids:
for result in results:
update_func(result)
do_expand_reference_ids(
result, skip_fields=list(ref_projection) if ref_projection else None
)
return results
@classmethod
def _expand_reference_fields(cls, doc_cls, result, fields):
def _expand_reference_fields(self, doc_cls, result, fields):
for ref_field_name in fields:
ref_ids = cls._search(doc_cls, result, ref_field_name, only_values=False)
if not ref_ids:
continue
for path, value in ref_ids:
dpath.set(
result,
path,
{'id': value} if value else {},
separator='.')
self._search(doc_cls, result, ref_field_name, factory=_ReferenceProxy)
@classmethod
def expand_reference_ids(cls, doc_cls, result):
cls._expand_reference_fields(doc_cls, result, doc_cls.get_reference_fields())
def expand_reference_ids(self, doc_cls, result):
self._expand_reference_fields(doc_cls, result, doc_cls.get_reference_fields())

View File

@@ -1,17 +1,19 @@
from collections import OrderedDict
from collections import OrderedDict, defaultdict
from itertools import chain
from operator import attrgetter
from threading import Lock
from typing import Sequence
import six
from mongoengine import EmbeddedDocumentField, EmbeddedDocumentListField
from mongoengine.base import get_document
from mongoengine.base import get_document, BaseField
from database.fields import (
LengthRangeEmbeddedDocumentListField,
UniqueEmbeddedDocumentListField,
EmbeddedDocumentSortedListField,
)
from database.utils import get_fields, get_fields_and_attr
from database.utils import get_fields, get_fields_attr
class PropsMixin(object):
@@ -19,6 +21,7 @@ class PropsMixin(object):
__cached_reference_fields = None
__cached_exclude_fields = None
__cached_fields_with_instance = None
__cached_field_names_per_type = None
__cached_dpath_computed_fields_lock = Lock()
__cached_dpath_computed_fields = None
@@ -29,6 +32,39 @@ class PropsMixin(object):
cls.__cached_fields = get_fields(cls)
return cls.__cached_fields
@classmethod
def get_field_names_for_type(cls, of_type=BaseField):
"""
Return field names per type including subfields
The fields of derived types are also returned
"""
assert issubclass(of_type, BaseField)
if cls.__cached_field_names_per_type is None:
fields = defaultdict(list)
for name, field in get_fields(cls, return_instance=True, subfields=True):
fields[type(field)].append(name)
for type_ in fields:
fields[type_].extend(
chain.from_iterable(
fields[other_type]
for other_type in fields
if other_type != type_ and issubclass(other_type, type_)
)
)
cls.__cached_field_names_per_type = fields
if of_type not in cls.__cached_field_names_per_type:
names = list(
chain.from_iterable(
field_names
for type_, field_names in cls.__cached_field_names_per_type.items()
if issubclass(type_, of_type)
)
)
cls.__cached_field_names_per_type[of_type] = names
return cls.__cached_field_names_per_type[of_type]
@classmethod
def get_fields_with_instance(cls, doc_cls):
if cls.__cached_fields_with_instance is None:
@@ -42,7 +78,7 @@ class PropsMixin(object):
@staticmethod
def _get_fields_with_attr(cls_, attr):
""" Get all fields with the specified attribute (supports nested fields) """
res = get_fields_and_attr(cls_, attr=attr)
res = get_fields_attr(cls_, attr=attr)
def resolve_doc(v):
if not isinstance(v, six.string_types):
@@ -122,6 +158,14 @@ class PropsMixin(object):
cls.__cached_reference_fields = OrderedDict(sorted(fields.items()))
return cls.__cached_reference_fields
@classmethod
def get_extra_projection(cls, fields: Sequence) -> tuple:
if isinstance(fields, str):
fields = [fields]
return tuple(
set(fields).union(cls.get_fields()).difference(cls.get_exclude_fields())
)
@classmethod
def get_exclude_fields(cls):
if cls.__cached_exclude_fields is None:
@@ -140,3 +184,18 @@ class PropsMixin(object):
result = separator.join(translated)
cls.__cached_dpath_computed_fields[path] = result
return cls.__cached_dpath_computed_fields[path]
def get_field_value(self, field_path: str, default=None):
"""
Return the document field_path value by the field_path name.
The path may contain '.'. If on any level the path is
not found then the default value is returned
"""
path_elements = field_path.split(".")
current = self
for name in path_elements:
current = getattr(current, name, default)
if current == default:
break
return current

View File

@@ -1,5 +1,6 @@
import hashlib
from inspect import ismethod, getmembers
from typing import Sequence, Tuple, Set, Optional, Callable, Any
from uuid import uuid4
from mongoengine import EmbeddedDocumentField, ListField, Document, Q
@@ -8,61 +9,65 @@ from mongoengine.base import BaseField
from .errors import translate_errors_context, ParseCallError
def get_fields(cls, of_type=BaseField, return_instance=False):
def get_fields(cls, of_type=BaseField, return_instance=False, subfields=False):
return _get_fields(
cls,
of_type=of_type,
subfields=subfields,
selector=lambda k, v: (k, v) if return_instance else k,
)
def get_fields_attr(cls, attr):
""" get field names from a class containing mongoengine fields """
res = []
for cls_ in reversed(cls.mro()):
res.extend([k if not return_instance else (k, v)
for k, v in vars(cls_).items()
if isinstance(v, of_type)])
return res
return dict(
_get_fields(cls, with_attr=attr, selector=lambda k, v: (k, getattr(v, attr)))
)
def get_fields_and_attr(cls, attr):
""" get field names from a class containing mongoengine fields """
res = {}
for cls_ in reversed(cls.mro()):
res.update({k: getattr(v, attr)
for k, v in vars(cls_).items()
if isinstance(v, BaseField) and hasattr(v, attr)})
return res
def get_fields_choices(cls, attr):
def get_choices(field_name: str, field: BaseField) -> Tuple:
if isinstance(field, ListField):
return field_name, field.field.choices
return field_name, field.choices
return dict(_get_fields(cls, with_attr=attr, subfields=True, selector=get_choices))
def _get_field_choices(name, field):
field_t = type(field)
if issubclass(field_t, EmbeddedDocumentField):
obj = field.document_type_obj
n, choices = _get_field_choices(field.name, obj.field)
return '%s__%s' % (name, n), choices
elif issubclass(type(field), ListField):
return name, field.field.choices
return name, field.choices
def get_fields_with_attr(cls, attr, default=False):
def _get_fields(
cls,
with_attr=None,
of_type=BaseField,
subfields=False,
selector: Optional[Callable[[str, BaseField], Any]] = None,
path: Tuple[str, ...] = (),
):
fields = []
for field_name, field in cls._fields.items():
if not getattr(field, attr, default):
continue
field_t = type(field)
if issubclass(field_t, EmbeddedDocumentField):
fields.extend((('%s__%s' % (field_name, name), choices)
for name, choices in get_fields_with_attr(field.document_type, attr, default)))
elif issubclass(type(field), ListField):
fields.append((field_name, field.field.choices))
else:
fields.append((field_name, field.choices))
field_path = path + (field_name,)
if isinstance(field, of_type) and (not with_attr or hasattr(field, with_attr)):
full_name = "__".join(field_path)
fields.append(selector(full_name, field) if selector else full_name)
if subfields and isinstance(field, EmbeddedDocumentField):
fields.extend(
_get_fields(
field.document_type,
with_attr=with_attr,
of_type=of_type,
subfields=subfields,
selector=selector,
path=field_path,
)
)
return fields
def get_items(cls):
""" get key/value items from an enum-like class (members represent enumeration key/value) """
res = {
k: v
for k, v in getmembers(cls)
if not (k.startswith("_") or ismethod(v))
}
res = {k: v for k, v in getmembers(cls) if not (k.startswith("_") or ismethod(v))}
return res
@@ -81,7 +86,7 @@ def parse_from_call(call_data, fields, cls_fields, discard_none_values=True):
fields = {k: None for k in fields}
fields = {k: v for k, v in fields.items() if k in cls_fields}
res = {}
with translate_errors_context('parsing call data'):
with translate_errors_context("parsing call data"):
for field, desc in fields.items():
value = call_data.get(field)
if value is None:
@@ -93,20 +98,34 @@ def parse_from_call(call_data, fields, cls_fields, discard_none_values=True):
if callable(desc):
desc(value)
else:
if issubclass(desc, (list, tuple, dict)) and not isinstance(value, desc):
raise ParseCallError('expecting %s' % desc.__name__, field=field)
if issubclass(desc, Document) and not desc.objects(id=value).only('id'):
raise ParseCallError('expecting %s id' % desc.__name__, id=value, field=field)
if issubclass(desc, (list, tuple, dict)) and not isinstance(
value, desc
):
raise ParseCallError(
"expecting %s" % desc.__name__, field=field
)
if issubclass(desc, Document) and not desc.objects(id=value).only(
"id"
):
raise ParseCallError(
"expecting %s id" % desc.__name__, id=value, field=field
)
res[field] = value
return res
def init_cls_from_base(cls, instance):
return cls(**{k: v for k, v in instance.to_mongo(use_db_field=False).to_dict().items() if k[0] != '_'})
return cls(
**{
k: v
for k, v in instance.to_mongo(use_db_field=False).to_dict().items()
if k[0] != "_"
}
)
def get_company_or_none_constraint(company=None):
return Q(company__in=(company, None, '')) | Q(company__exists=False)
return Q(company__in=(company, None, "")) | Q(company__exists=False)
def field_does_not_exist(field: str, empty_value=None, is_list=False) -> Q:
@@ -118,23 +137,43 @@ def field_does_not_exist(field: str, empty_value=None, is_list=False) -> Q:
the length of the array will be used (len==0 means empty)
:return:
"""
query = (Q(**{f"{field}__exists": False}) |
Q(**{f"{field}__in": {empty_value, None}}))
query = Q(**{f"{field}__exists": False}) | Q(
**{f"{field}__in": {empty_value, None}}
)
if is_list:
query |= Q(**{f"{field}__size": 0})
return query
def field_exists(field: str, empty_value=None, is_list=False) -> Q:
"""
Creates a query object used for finding a field that exists and is not None or empty.
:param field: Field name
:param empty_value: The empty value to test for (None means no specific empty value will be used)
:param is_list: Is this a list (array) field. In this case, instead of testing for an empty value,
the length of the array will be used (len==0 means empty)
:return:
"""
query = Q(**{f"{field}__exists": True}) & Q(
**{f"{field}__nin": {empty_value, None}}
)
if is_list:
query &= Q(**{f"{field}__not__size": 0})
return query
def get_subkey(d, key_path, default=None):
""" Get a key from a nested dictionary. kay_path is a '.' separated string of keys used to traverse
the nested dictionary.
"""
keys = key_path.split('.')
keys = key_path.split(".")
for i, key in enumerate(keys):
if not isinstance(d, dict):
raise KeyError('Expecting a dict (%s)' % ('.'.join(keys[:i]) if i else 'bad input'))
raise KeyError(
"Expecting a dict (%s)" % (".".join(keys[:i]) if i else "bad input")
)
d = d.get(key)
if key is None:
if d is None:
return default
return d
@@ -158,3 +197,42 @@ def merge_dicts(*dicts):
def filter_fields(cls, fields):
"""From the fields dictionary return only the fields that match cls fields"""
return {key: fields[key] for key in fields if key in get_fields(cls)}
def _names_set(*names: str) -> Set[str]:
"""
Given a list of names return set with names and '-names'
"""
return set(names) | set(f"-{name}" for name in names)
system_tag_names = {
"model": _names_set("active", "archived"),
"project": _names_set("archived", "public", "default"),
"task": _names_set("active", "archived", "development"),
"queue": _names_set("default"),
}
system_tag_prefixes = {"task": _names_set("annotat")}
def partition_tags(
entity: str, tags: Sequence[str], system_tags: Optional[Sequence[str]] = ()
) -> Tuple[Sequence[str], Sequence[str]]:
"""
Partition the given tags sequence into system and user-defined tags
:param entity: The name of the entity that defines the list of the system tags
:param tags: The tags to partition
:param system_tags: Optional. If passed then these tags are considered system together
with those defined for the entity.
:return: a tuple where the first element is the sequence of user-defined tags and
the second element is the sequence of system tags
"""
tags = set(tags)
system_tags = set(system_tags)
system_tags |= tags & system_tag_names[entity]
prefixes = system_tag_prefixes.get(entity, [])
system_tags |= {t for t in tags for p in prefixes if t.lower().startswith(p)}
return list(tags - system_tags), list(system_tags)

View File

@@ -0,0 +1,27 @@
{
"template": "queue_metrics_*",
"settings": {
"number_of_shards": 1
},
"mappings": {
"metrics": {
"_source": {
"enabled": true
},
"properties": {
"timestamp": {
"type": "date"
},
"queue": {
"type": "keyword"
},
"average_waiting_time": {
"type": "float"
},
"queue_length": {
"type": "integer"
}
}
}
}
}

View File

@@ -0,0 +1,23 @@
{
"template": "worker_stats_*",
"settings": {
"number_of_shards": 1
},
"mappings": {
"stat": {
"_source": {
"enabled": true
},
"properties": {
"timestamp": { "type": "date" },
"worker": { "type": "keyword" },
"category": { "type": "keyword" },
"metric": { "type": "keyword" },
"variant": { "type": "keyword" },
"value": { "type": "float" },
"unit": { "type": "keyword" },
"task": { "type": "keyword" }
}
}
}
}

View File

@@ -1,11 +1,28 @@
from datetime import datetime
from os import getenv
from boltons.iterutils import first
from elasticsearch import Elasticsearch, Transport
from config import config
log = config.logger(__file__)
OVERRIDE_HOST_ENV_KEY = (
"TRAINS_ELASTIC_SERVICE_HOST",
"ELASTIC_SERVICE_HOST",
"ELASTIC_SERVICE_SERVICE_HOST",
)
OVERRIDE_PORT_ENV_KEY = ("TRAINS_ELASTIC_SERVICE_PORT", "ELASTIC_SERVICE_PORT")
OVERRIDE_HOST = first(filter(None, map(getenv, OVERRIDE_HOST_ENV_KEY)))
if OVERRIDE_HOST:
log.info(f"Using override elastic host {OVERRIDE_HOST}")
OVERRIDE_PORT = first(filter(None, map(getenv, OVERRIDE_PORT_ENV_KEY)))
if OVERRIDE_PORT:
log.info(f"Using override elastic port {OVERRIDE_PORT}")
_instances = {}
@@ -13,6 +30,7 @@ class MissingClusterConfiguration(Exception):
"""
Exception when cluster configuration is not found in config files
"""
pass
@@ -20,6 +38,7 @@ class InvalidClusterConfiguration(Exception):
"""
Exception when cluster configuration does not contain required properties
"""
pass
@@ -33,28 +52,41 @@ def connect(cluster_name):
:raises InvalidClusterConfiguration: in case cluster config section misses needed properties
"""
if cluster_name not in _instances:
cluster_config = _get_cluster_config(cluster_name)
hosts = cluster_config.get('hosts', None)
cluster_config = get_cluster_config(cluster_name)
hosts = cluster_config.get("hosts", None)
if not hosts:
raise InvalidClusterConfiguration(cluster_name)
args = cluster_config.get('args', {})
_instances[cluster_name] = Elasticsearch(hosts=hosts, transport_class=Transport, **args)
args = cluster_config.get("args", {})
_instances[cluster_name] = Elasticsearch(
hosts=hosts, transport_class=Transport, **args
)
return _instances[cluster_name]
def _get_cluster_config(cluster_name):
def get_cluster_config(cluster_name):
"""
Returns cluster config for the specified cluster path
:param cluster_name: Dot separated cluster path in the configuration file
:return: config section for the cluster
:raises MissingClusterConfiguration: in case no config section is found for the cluster
"""
cluster_key = '.'.join(('hosts.elastic', cluster_name))
cluster_key = ".".join(("hosts.elastic", cluster_name))
cluster_config = config.get(cluster_key, None)
if not cluster_config:
raise MissingClusterConfiguration(cluster_name)
def set_host_prop(key, value):
for host in cluster_config.get("hosts", []):
host[key] = value
if OVERRIDE_HOST:
set_host_prop("host", OVERRIDE_HOST)
if OVERRIDE_PORT:
set_host_prop("port", OVERRIDE_PORT)
return cluster_config

View File

@@ -1,17 +1,32 @@
import importlib.util
from datetime import datetime
from pathlib import Path
from uuid import uuid4
import attr
from furl import furl
from mongoengine.connection import get_db
from semantic_version import Version
from database.model.user import User
from database.model.auth import User as AuthUser, Credentials
import database.utils
from bll.queue import QueueBLL
from config import config
from database import Database
from database.model.auth import Role
from database.model.auth import User as AuthUser, Credentials
from database.model.company import Company
from database.model.queue import Queue
from database.model.settings import Settings
from database.model.user import User
from database.model.version import Version as DatabaseVersion
from elastic.apply_mappings import apply_mappings_to_host
from es_factory import get_cluster_config
from service_repo.auth.fixed_user import FixedUser
log = config.logger(__file__)
migration_dir = (Path(__file__) / "../../migration/mongodb").resolve()
class MissingElasticConfiguration(Exception):
"""
@@ -22,10 +37,9 @@ class MissingElasticConfiguration(Exception):
def init_es_data():
hosts_key = "hosts.elastic.events.hosts"
hosts_config = config.get(hosts_key, None)
hosts_config = get_cluster_config("events").get("hosts")
if not hosts_config:
raise MissingElasticConfiguration(hosts_key)
raise MissingElasticConfiguration("for cluster 'events'")
for conf in hosts_config:
host = furl(scheme="http", host=conf["host"], port=conf["port"]).url
@@ -47,6 +61,18 @@ def _ensure_company():
return company_id
def _ensure_default_queue(company):
"""
If no queue is present for the company then
create a new one and mark it as a default
"""
queue = Queue.objects(company=company).only("id").first()
if queue:
return
QueueBLL.create(company, name="default", system_tags=["default"])
def _ensure_auth_user(user_data, company_id):
ensure_credentials = {"key", "secret"}.issubset(user_data.keys())
if ensure_credentials:
@@ -85,10 +111,7 @@ def _ensure_user(user: FixedUser, company_id: str):
data["email"] = f"{user.user_id}@example.com"
data["role"] = Role.user
_ensure_auth_user(
user_data=data,
company_id=company_id,
)
_ensure_auth_user(user_data=data, company_id=company_id)
given_name, _, family_name = user.name.partition(" ")
@@ -101,12 +124,82 @@ def _ensure_user(user: FixedUser, company_id: str):
).save()
def _apply_migrations():
if not migration_dir.is_dir():
raise ValueError(f"Invalid migration dir {migration_dir}")
try:
previous_versions = sorted(
(Version(ver.num) for ver in DatabaseVersion.objects().only("num")),
reverse=True,
)
except ValueError as ex:
raise ValueError(f"Invalid database version number encountered: {ex}")
last_version = previous_versions[0] if previous_versions else Version("0.0.0")
try:
new_scripts = {
ver: path
for ver, path in ((Version(f.stem), f) for f in migration_dir.glob("*.py"))
if ver > last_version
}
except ValueError as ex:
raise ValueError(f"Failed parsing migration version from file: {ex}")
dbs = {Database.auth: "migrate_auth", Database.backend: "migrate_backend"}
migration_log = log.getChild("mongodb_migration")
for script_version in sorted(new_scripts.keys()):
script = new_scripts[script_version]
spec = importlib.util.spec_from_file_location(script.stem, str(script))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
for alias, func_name in dbs.items():
func = getattr(module, func_name, None)
if not func:
continue
try:
migration_log.info(f"Applying {script.stem}/{func_name}()")
func(get_db(alias))
except Exception:
migration_log.exception(f"Failed applying {script}:{func_name}()")
raise ValueError("Migration failed, aborting. Please restore backup.")
DatabaseVersion(
id=database.utils.id(),
num=script.stem,
created=datetime.utcnow(),
desc="Applied on server startup",
).save()
def _ensure_uuid():
Settings.add_value("server.uuid", str(uuid4()))
def init_mongo_data():
try:
_apply_migrations()
_ensure_uuid()
company_id = _ensure_company()
_ensure_default_queue(company_id)
users = [
{"name": "apiserver", "role": Role.system, "email": "apiserver@example.com"},
{"name": "webserver", "role": Role.system, "email": "webserver@example.com"},
{
"name": "apiserver",
"role": Role.system,
"email": "apiserver@example.com",
},
{
"name": "webserver",
"role": Role.system,
"email": "webserver@example.com",
},
{"name": "tests", "role": Role.user, "email": "tests@example.com"},
]
@@ -117,7 +210,11 @@ def init_mongo_data():
_ensure_auth_user(user, company_id)
if FixedUser.enabled():
log.info("Fixed users mode is enabled")
for user in FixedUser.from_config():
_ensure_user(user, company_id)
try:
_ensure_user(user, company_id)
except Exception as ex:
log.error(f"Failed creating fixed user {user['name']}: {ex}")
except Exception as ex:
pass
log.exception("Failed initializing mongodb")

195
server/redis_manager.py Normal file
View File

@@ -0,0 +1,195 @@
import threading
from os import getenv
from time import sleep
from boltons.iterutils import first
from redis import StrictRedis
from redis.sentinel import Sentinel, SentinelConnectionPool
from apierrors.errors.server_error import ConfigError, GeneralError
from config import config
log = config.logger(__file__)
OVERRIDE_HOST_ENV_KEY = ("TRAINS_REDIS_SERVICE_HOST", "REDIS_SERVICE_HOST")
OVERRIDE_PORT_ENV_KEY = ("TRAINS_REDIS_SERVICE_PORT", "REDIS_SERVICE_PORT")
OVERRIDE_HOST = first(filter(None, map(getenv, OVERRIDE_HOST_ENV_KEY)))
if OVERRIDE_HOST:
log.info(f"Using override redis host {OVERRIDE_HOST}")
OVERRIDE_PORT = first(filter(None, map(getenv, OVERRIDE_PORT_ENV_KEY)))
if OVERRIDE_PORT:
log.info(f"Using override redis port {OVERRIDE_PORT}")
class MyPubSubWorkerThread(threading.Thread):
def __init__(self, sentinel, on_new_master, msg_sleep_time, daemon=True):
super(MyPubSubWorkerThread, self).__init__()
self.daemon = daemon
self.sentinel = sentinel
self.on_new_master = on_new_master
self.sentinel_host = sentinel.connection_pool.connection_kwargs["host"]
self.msg_sleep_time = msg_sleep_time
self._running = False
self.pubsub = None
def subscribe(self):
if self.pubsub:
try:
self.pubsub.unsubscribe()
self.pubsub.punsubscribe()
except Exception:
pass
finally:
self.pubsub = None
subscriptions = {"+switch-master": self.on_new_master}
while not self.pubsub or not self.pubsub.subscribed:
try:
self.pubsub = self.sentinel.pubsub()
self.pubsub.subscribe(**subscriptions)
except Exception as ex:
log.warn(
f"Error while subscribing to sentinel at {self.sentinel_host} ({ex.args[0]}) Sleeping and retrying"
)
sleep(3)
log.info(f"Subscribed to sentinel {self.sentinel_host}")
def run(self):
if self._running:
return
self._running = True
self.subscribe()
while self.pubsub.subscribed:
try:
self.pubsub.get_message(
ignore_subscribe_messages=True, timeout=self.msg_sleep_time
)
except Exception as ex:
log.warn(
f"Error while getting message from sentinel {self.sentinel_host} ({ex.args[0]}) Resubscribing"
)
self.subscribe()
self.pubsub.close()
self._running = False
def stop(self):
# stopping simply unsubscribes from all channels and patterns.
# the unsubscribe responses that are generated will short circuit
# the loop in run(), calling pubsub.close() to clean up the connection
self.pubsub.unsubscribe()
self.pubsub.punsubscribe()
# todo,future - multi master clusters?
class RedisCluster(object):
def __init__(self, sentinel_hosts, service_name, **connection_kwargs):
self.service_name = service_name
self.sentinel = Sentinel(sentinel_hosts, **connection_kwargs)
self.master = None
self.master_host_port = None
self.reconfigure()
self.sentinel_threads = {}
self.listen()
def reconfigure(self):
try:
self.master_host_port = self.sentinel.discover_master(self.service_name)
self.master = self.sentinel.master_for(self.service_name)
log.info(f"Reconfigured master to {self.master_host_port}")
except Exception as ex:
log.error(f"Error while reconfiguring. {ex.args[0]}")
def listen(self):
def on_new_master(workerThread):
self.reconfigure()
for sentinel in self.sentinel.sentinels:
sentinel_host = sentinel.connection_pool.connection_kwargs["host"]
self.sentinel_threads[sentinel_host] = MyPubSubWorkerThread(
sentinel, on_new_master, msg_sleep_time=0.001, daemon=True
)
self.sentinel_threads[sentinel_host].start()
class RedisManager(object):
def __init__(self, redis_config_dict):
self.aliases = {}
for alias, alias_config in redis_config_dict.items():
alias_config = alias_config.as_plain_ordered_dict()
is_cluster = alias_config.get("cluster", False)
host = OVERRIDE_HOST or alias_config.get("host", None)
if host:
alias_config["host"] = host
port = OVERRIDE_PORT or alias_config.get("port", None)
if port:
alias_config["port"] = port
db = alias_config.get("db", 0)
sentinels = alias_config.get("sentinels", None)
service_name = alias_config.get("service_name", None)
if not is_cluster and sentinels:
raise ConfigError(
"Redis configuration is invalid. mixed regular and cluster mode",
alias=alias,
)
if is_cluster and (not sentinels or not service_name):
raise ConfigError(
"Redis configuration is invalid. missing sentinels or service_name",
alias=alias,
)
if not is_cluster and (not port or not host):
raise ConfigError(
"Redis configuration is invalid. missing port or host", alias=alias
)
if is_cluster:
# todo support all redis connection args via sentinel's connection_kwargs
del alias_config["sentinels"]
del alias_config["cluster"]
del alias_config["service_name"]
self.aliases[alias] = RedisCluster(
sentinels, service_name, **alias_config
)
else:
self.aliases[alias] = StrictRedis(**alias_config)
def connection(self, alias):
obj = self.aliases.get(alias)
if not obj:
raise GeneralError(f"Invalid Redis alias {alias}")
if isinstance(obj, RedisCluster):
obj.master.get("health")
return obj.master
else:
obj.get("health")
return obj
def host(self, alias):
r = self.connection(alias)
pool = r.connection_pool
if isinstance(pool, SentinelConnectionPool):
connections = pool.connection_kwargs[
"connection_pool"
]._available_connections
else:
connections = pool._available_connections
if len(connections) > 0:
return connections[0].host
else:
return None
redman = RedisManager(config.get("hosts.redis"))

View File

@@ -26,3 +26,6 @@ fastjsonschema>=2.8
boltons>=19.1.0
semantic_version>=2.6.0,<3
furl>=2.0.0
redis>=2.10.5
humanfriendly==4.18
psutil>=5.6.5

View File

@@ -15,6 +15,11 @@ _definitions {
type: string
description: ""
}
last_used {
type: string
description: ""
format: "date-time"
}
}
}
role {
@@ -52,6 +57,22 @@ login {
}
}
logout {
internal: false
allow_roles = [ "*" ]
"2.2" {
description: """Removes the authentication cookie from the current session"""
request {
type: object
additionalProperties: false
}
response {
type: object
additionalProperties: false
}
}
}
get_token_for_user {
"2.1" {
description: """Get a token for the specified user. Intended for internal use."""
@@ -255,33 +276,6 @@ revoke_credentials {
}
}
delete_user {
allow_roles = [ "system", "root", "admin" ]
internal: false
"2.1" {
description: """Delete a new user manually. Only supported in on-premises deployments. This only removes the user's auth entry so that any references to the deleted user's ID will still have valid user information"""
request {
type: object
required: [ user ]
properties {
user {
type: string
description: User ID
}
}
}
response {
type: object
properties {
deleted {
description: "True if user was successfully deleted, False otherwise"
type: boolean
}
}
}
}
}
edit_user {
internal: false
allow_roles: ["system", "root", "admin"]

View File

@@ -149,6 +149,14 @@
}
}
}
scalar_key_enum {
type: string
enum: [
iter
timestamp
iso_time
]
}
log_level_enum {
type: string
enum: [
@@ -216,7 +224,7 @@
}
add_batch {
"2.1" {
description: "Adds a batch of events in a single call."
description: "Adds a batch of events in a single call (json-lines format, stream-friendly)"
batch_request: {
action: add
version: 1.5
@@ -243,6 +251,11 @@
type: string
description: "Task ID"
}
allow_locked {
type: boolean
description: "Allow deleting events even if the task is locked"
default: false
}
}
}
response {
@@ -444,6 +457,10 @@
type: integer
description: "Number of events to return each time"
}
event_type {
type: string
description: "Return only events of this type"
}
}
}
response {
@@ -682,6 +699,19 @@
type: string
description: "Task ID"
}
samples {
description: "The amount of histogram points to return (0 to return all the points). Optional, the default value is 10000."
type: integer
}
key {
description: """
Histogram x axis to use:
iter - iteration number
iso_time - event time as ISO formatted string
timestamp - event timestamp as milliseconds since epoch
"""
"$ref": "#/definitions/scalar_key_enum"
}
}
}
response {
@@ -715,7 +745,19 @@
description: "List of task Task IDs"
}
}
samples {
description: "The amount of histogram points to return (0 to return all the points). Optional, the default value is 10000."
type: integer
}
key {
description: """
Histogram x axis to use:
iter - iteration number
iso_time - event time as ISO formatted string
timestamp - event timestamp as milliseconds since epoch
"""
"$ref": "#/definitions/scalar_key_enum"
}
}
}
response {

View File

@@ -57,9 +57,14 @@
}
tags {
type: array
description: "Tags"
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
framework {
description: "Framework on which the model is based. Should be identical to the framework of the task which created the model"
type: string
@@ -159,7 +164,12 @@
type: boolean
}
tags {
description: "Tags list used to filter results. Prepend '-' to tag name to indicate exclusion"
description: "User-defined tags list used to filter results. Prepend '-' to tag name to indicate exclusion"
type: array
items { type: string }
}
system_tags {
description: "System tags list used to filter results. Prepend '-' to system tag name to indicate exclusion"
type: array
items { type: string }
}
@@ -263,10 +273,15 @@
type: string
}
tags {
description: "Tags list"
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
override_model_id {
description: "Override model ID. If provided, this model is updated in the task."
type: string
@@ -325,10 +340,15 @@
type: string
}
tags {
description: "Tags list"
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
framework {
description: "Framework on which the model is based. Case insensitive. Should be identical to the framework of the task which created the model."
type: string
@@ -344,7 +364,7 @@
additionalProperties { type: integer }
}
ready {
description: "Indication if the model is final and can be used by other tasks Default is false."
description: "Indication if the model is final and can be used by other tasks. Default is false."
type: boolean
default: false
}
@@ -408,10 +428,15 @@
type: string
}
tags {
description: "Tags list"
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
framework {
description: "Framework on which the model is based. Case insensitive. Should be identical to the framework of the task which created the model."
type: string
@@ -485,10 +510,15 @@
type: string
}
tags {
description: "Tags list"
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
ready {
description: "Indication if the model is final and can be used by other tasks Default is false."
type: boolean

View File

@@ -1,462 +1,523 @@
{
_description: "Provides support for defining Projects containing Tasks, Models and Dataset Versions."
_definitions {
multi_field_pattern_data {
_description: "Provides support for defining Projects containing Tasks, Models and Dataset Versions."
_definitions {
multi_field_pattern_data {
type: object
properties {
pattern {
description: "Pattern string (regex)"
type: string
}
fields {
description: "List of field names"
type: array
items { type: string }
}
}
}
project {
type: object
properties {
id {
description: "Project id"
type: string
}
name {
description: "Project name"
type: string
}
description {
description: "Project description"
type: string
}
user {
description: "Associated user id"
type: string
}
company {
description: "Company id"
type: string
}
created {
description: "Creation time"
type: string
format: "date-time"
}
tags {
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
last_update {
description: """Last project update time. Reflects the last time the project metadata was changed or a task in this project has changed status"""
type: string
format: "date-time"
}
}
}
stats_status_count {
type: object
properties {
total_runtime {
description: "Total run time of all tasks in project (in seconds)"
type: integer
}
status_count {
description: "Status counts"
type: object
properties {
created {
description: "Number of 'created' tasks in project"
type: integer
}
queued {
description: "Number of 'queued' tasks in project"
type: integer
}
in_progress {
description: "Number of 'in_progress' tasks in project"
type: integer
}
stopped {
description: "Number of 'stopped' tasks in project"
type: integer
}
published {
description: "Number of 'published' tasks in project"
type: integer
}
closed {
description: "Number of 'closed' tasks in project"
type: integer
}
failed {
description: "Number of 'failed' tasks in project"
type: integer
}
unknown {
description: "Number of 'unknown' tasks in project"
type: integer
}
}
}
}
}
stats {
type: object
properties {
active {
description: "Stats for active tasks"
"$ref": "#/definitions/stats_status_count"
}
archived {
description: "Stats for archived tasks"
"$ref": "#/definitions/stats_status_count"
}
}
}
projects_get_all_response_single {
// copy-paste from project definition
type: object
properties {
id {
description: "Project id"
type: string
}
name {
description: "Project name"
type: string
}
description {
description: "Project description"
type: string
}
user {
description: "Associated user id"
type: string
}
company {
description: "Company id"
type: string
}
created {
description: "Creation time"
type: string
format: "date-time"
}
tags {
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
// extra properties
stats: {
description: "Additional project stats"
"$ref": "#/definitions/stats"
}
}
}
metric_variant_result {
type: object
properties {
metric {
description: "Metric name"
type: string
}
metric_hash {
description: """Metric name hash. Used instead of the metric name when categorizing
last metrics events in task objects."""
type: string
}
variant {
description: "Variant name"
type: string
}
variant_hash {
description: """Variant name hash. Used instead of the variant name when categorizing
last metrics events in task objects."""
type: string
}
}
}
}
create {
"2.1" {
description: "Create a new project"
request {
type: object
required :[
name
description
]
properties {
pattern {
description: "Pattern string (regex)"
name {
description: "Project name Unique within the company."
type: string
}
fields {
description: "List of field names"
description {
description: "Project description. "
type: string
}
tags {
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
}
}
project_tags_enum {
type: string
enum: [ archived, public, default ]
}
project {
response {
type: object
properties {
id {
description: "Project id"
type: string
}
name {
description: "Project name"
type: string
}
description {
description: "Project description"
type: string
}
user {
description: "Associated user id"
type: string
}
company {
description: "Company id"
type: string
}
created {
description: "Creation time"
type: string
format: "date-time"
}
tags {
description: "Tags"
type: array
items { "$ref": "#/definitions/project_tags_enum" }
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
last_update {
description: """Last project update time. Reflects the last time the project metadata was changed or a task in this project has changed status"""
type: string
format: "date-time"
}
}
}
stats_status_count {
type: object
properties {
total_runtime {
description: "Total run time of all tasks in project (in seconds)"
type: integer
}
status_count {
description: "Status counts"
type: object
properties {
created {
description: "Number of 'created' tasks in project"
type: integer
}
queued {
description: "Number of 'queued' tasks in project"
type: integer
}
in_progress {
description: "Number of 'in_progress' tasks in project"
type: integer
}
stopped {
description: "Number of 'stopped' tasks in project"
type: integer
}
published {
description: "Number of 'published' tasks in project"
type: integer
}
closed {
description: "Number of 'closed' tasks in project"
type: integer
}
failed {
description: "Number of 'failed' tasks in project"
type: integer
}
unknown {
description: "Number of 'unknown' tasks in project"
type: integer
}
}
}
}
}
stats {
type: object
properties {
active {
description: "Stats for active tasks"
"$ref": "#/definitions/stats_status_count"
}
archived {
description: "Stats for archived tasks"
"$ref": "#/definitions/stats_status_count"
}
}
}
projects_get_all_response_single {
// copy-paste from project definition
type: object
properties {
id {
description: "Project id"
type: string
}
name {
description: "Project name"
type: string
}
description {
description: "Project description"
type: string
}
user {
description: "Associated user id"
type: string
}
company {
description: "Company id"
type: string
}
created {
description: "Creation time"
type: string
format: "date-time"
}
tags {
description: "Tags"
type: array
items { "$ref": "#/definitions/project_tags_enum" }
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
// extra properties
stats: {
description: "Additional project stats"
"$ref": "#/definitions/stats"
}
}
}
metric_variant_result {
type: object
properties {
metric {
description: "Metric name"
type: string
}
metric_hash {
description: """Metric name hash. Used instead of the metric name when categorizing
last metrics events in task objects."""
type: string
}
variant {
description: "Variant name"
type: string
}
variant_hash {
description: """Variant name hash. Used instead of the variant name when categorizing
last metrics events in task objects."""
type: string
}
}
}
}
create {
"2.1" {
description: "Create a new project"
request {
type: object
required :[
name
description
]
properties {
name {
description: "Project name Unique within the company."
type: string
}
description {
description: "Project description. "
type: string
}
tags {
description: "Tags"
type: array
items { "$ref": "#/definitions/project_tags_enum" }
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
}
}
response {
type: object
properties {
id {
description: "Project id"
type: string
}
}
}
}
}
get_by_id {
"2.1" {
description: ""
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
}
}
response {
type: object
properties {
project {
description: "Project info"
"$ref": "#/definitions/project"
}
}
}
}
}
get_all {
"2.1" {
description: "Get all the company's projects and all public projects"
request {
type: object
properties {
id {
description: "List of IDs to filter by"
type: array
items { type: string }
}
name {
description: "Get only projects whose name matches this pattern (python regular expression syntax)"
type: string
}
description {
description: "Get only projects whose description matches this pattern (python regular expression syntax)"
type: string
}
tags {
description: "Tags list used to filter results. Prepend '-' to tag name to indicate exclusion"
type: array
items { type: string }
}
order_by {
description: "List of field names to order by. When search_text is used, '@text_score' can be used as a field representing the text score of returned documents. Use '-' prefix to specify descending order. Optional, recommended when using page"
type: array
items { type: string }
}
page {
description: "Page number, returns a specific page out of the resulting list of dataviews"
type: integer
minimum: 0
}
page_size {
description: "Page size, specifies the number of results returned in each page (last page may contain fewer results)"
type: integer
minimum: 1
}
search_text {
description: "Free text search query"
type: string
}
only_fields {
description: "List of document's field names (nesting is supported using '.', e.g. execution.model_labels). If provided, this list defines the query's projection (only these fields will be returned for each result entry)"
type: array
items { type: string }
}
_all_ {
description: "Multi-field pattern condition (all fields match pattern)"
"$ref": "#/definitions/multi_field_pattern_data"
}
_any_ {
description: "Multi-field pattern condition (any field matches pattern)"
"$ref": "#/definitions/multi_field_pattern_data"
}
}
}
response {
type: object
properties {
projects {
description: "Projects list"
type: array
items { "$ref": "#/definitions/projects_get_all_response_single" }
}
}
}
}
}
get_all_ex {
internal: true
"2.1": ${get_all."2.1"} {
request {
properties {
include_stats {
description: "If true, include project statistic in response."
type: boolean
default: false
}
stats_for_state {
description: "Report stats include only statistics for tasks in the specified state. If Null is provided, stats for all task states will be returned."
type: string
enum: [ active, archived ]
default: active
}
}
}
}
}
update {
"2.1" {
description: "Update project information"
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
name {
description: "Project name. Unique within the company."
type: string
}
description {
description: "Project description. "
type: string
}
description {
description: "Project description"
type: string
}
tags {
description: "Tags list"
type: array
items { type: string }
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
}
}
response {
type: object
properties {
updated {
description: "Number of projects updated (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
fields {
description: "Updated fields names and values"
type: object
additionalProperties: true
}
}
}
}
}
delete {
"2.1" {
description: "Deletes a project"
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
force {
description: """If not true, fails if project has tasks.
If true, and project has tasks, they will be unassigned"""
type: boolean
default: false
}
}
}
response {
type: object
properties {
deleted {
description: "Number of projects deleted (0 or 1)"
type: integer
}
disassociated_tasks {
description: "Number of tasks disassociated from the deleted project"
type: integer
}
}
}
}
}
get_unique_metric_variants {
"2.1" {
description: """Get all metric/variant pairs reported for tasks in a specific project.
If no project is specified, metrics/variant paris reported for all tasks will be returned.
If the project does not exist, an empty list will be returned."""
request {
type: object
properties {
project {
description: "Project ID"
type: string
}
}
}
response {
type: object
properties {
metrics {
description: "A list of metric variants reported for tasks in this project"
type: array
items { "$ref": "#/definitions/metric_variant_result" }
}
}
}
}
}
}
get_by_id {
"2.1" {
description: ""
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
}
}
response {
type: object
properties {
project {
description: "Project info"
"$ref": "#/definitions/project"
}
}
}
}
}
get_all {
"2.1" {
description: "Get all the company's projects and all public projects"
request {
type: object
properties {
id {
description: "List of IDs to filter by"
type: array
items { type: string }
}
name {
description: "Get only projects whose name matches this pattern (python regular expression syntax)"
type: string
}
description {
description: "Get only projects whose description matches this pattern (python regular expression syntax)"
type: string
}
tags {
description: "User-defined tags list used to filter results. Prepend '-' to tag name to indicate exclusion"
type: array
items { type: string }
}
system_tags {
description: "System tags list used to filter results. Prepend '-' to system tag name to indicate exclusion"
type: array
items { type: string }
}
order_by {
description: "List of field names to order by. When search_text is used, '@text_score' can be used as a field representing the text score of returned documents. Use '-' prefix to specify descending order. Optional, recommended when using page"
type: array
items { type: string }
}
page {
description: "Page number, returns a specific page out of the resulting list of dataviews"
type: integer
minimum: 0
}
page_size {
description: "Page size, specifies the number of results returned in each page (last page may contain fewer results)"
type: integer
minimum: 1
}
search_text {
description: "Free text search query"
type: string
}
only_fields {
description: "List of document's field names (nesting is supported using '.', e.g. execution.model_labels). If provided, this list defines the query's projection (only these fields will be returned for each result entry)"
type: array
items { type: string }
}
_all_ {
description: "Multi-field pattern condition (all fields match pattern)"
"$ref": "#/definitions/multi_field_pattern_data"
}
_any_ {
description: "Multi-field pattern condition (any field matches pattern)"
"$ref": "#/definitions/multi_field_pattern_data"
}
}
}
response {
type: object
properties {
projects {
description: "Projects list"
type: array
items { "$ref": "#/definitions/projects_get_all_response_single" }
}
}
}
}
}
get_all_ex {
internal: true
"2.1": ${get_all."2.1"} {
request {
properties {
include_stats {
description: "If true, include project statistic in response."
type: boolean
default: false
}
stats_for_state {
description: "Report stats include only statistics for tasks in the specified state. If Null is provided, stats for all task states will be returned."
type: string
enum: [ active, archived ]
default: active
}
}
}
}
}
update {
"2.1" {
description: "Update project information"
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
name {
description: "Project name. Unique within the company."
type: string
}
description {
description: "Project description. "
type: string
}
description {
description: "Project description"
type: string
}
tags {
type: array
description: "User-defined tags"
items { type: string }
}
system_tags {
type: array
description: "System tags. This field is reserved for system use, please don't use it."
items {type: string}
}
default_output_destination {
description: "The default output destination URL for new tasks under this project"
type: string
}
}
}
response {
type: object
properties {
updated {
description: "Number of projects updated (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
fields {
description: "Updated fields names and values"
type: object
additionalProperties: true
}
}
}
}
}
delete {
"2.1" {
description: "Deletes a project"
request {
type: object
required: [ project ]
properties {
project {
description: "Project id"
type: string
}
force {
description: """If not true, fails if project has tasks.
If true, and project has tasks, they will be unassigned"""
type: boolean
default: false
}
}
}
response {
type: object
properties {
deleted {
description: "Number of projects deleted (0 or 1)"
type: integer
}
disassociated_tasks {
description: "Number of tasks disassociated from the deleted project"
type: integer
}
}
}
}
}
get_unique_metric_variants {
"2.1" {
description: """Get all metric/variant pairs reported for tasks in a specific project.
If no project is specified, metrics/variant paris reported for all tasks will be returned.
If the project does not exist, an empty list will be returned."""
request {
type: object
properties {
project {
description: "Project ID"
type: string
}
}
}
response {
type: object
properties {
metrics {
description: "A list of metric variants reported for tasks in this project"
type: array
items { "$ref": "#/definitions/metric_variant_result" }
}
}
}
}
}
get_hyper_parameters {
"2.2" {
description: """Get a list of all hyper parameter names used in tasks within the given project."""
request {
type: object
properties {
project {
description: "Project ID"
type: string
}
page {
description: "Page number"
default: 0
type: integer
}
page_size {
description: "Page size"
default: 500
type: integer
}
}
}
response {
type: object
properties {
parameters {
description: "A list of hyper parameter names"
type: array
items { type: string }
}
remaining {
description: "Remaining results"
type: integer
}
total {
description: "Total number of results"
type: integer
}
}
}
}

View File

@@ -0,0 +1,568 @@
{
_description: "Provides a management API for queues of tasks waiting to be executed by workers deployed anywhere (see Workers Service)."
_definitions {
queue_metrics {
type: object
properties: {
queue: {
type: string
description: "ID of the queue"
}
dates {
type: array
description: "List of timestamps (in seconds from epoch) in the acceding order. The timestamps are separated by the requested interval. Timestamps where no queue status change was recorded are omitted."
items { type: integer }
}
avg_waiting_times {
type: array
description: "List of average waiting times for tasks in the queue. The points correspond to the timestamps in the dates list. If more than one value exists for the given interval then the maximum value is taken."
items { type: number }
}
queue_lengths {
type: array
description: "List of tasks counts in the queue. The points correspond to the timestamps in the dates list. If more than one value exists for the given interval then the count that corresponds to the maximum average value is taken."
items { type: integer }
}
}
}
entry {
type: object
properties: {
task {
description: "Queued task ID"
type: string
}
added {
description: "Time this entry was added to the queue"
type: string
format: "date-time"
}
}
}
queue {
type: object
properties: {
id {
description: "Queue id"
type: string
}
name {
description: "Queue name"
type: string
}
user {
description: "Associated user id"
type: string
}
company {
description: "Company id"
type: string
}
created {
description: "Queue creation time"
type: string
format: "date-time"
}
tags {
description: "User-defined tags"
type: array
items { type: string }
}
system_tags {
description: "System tags. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
entries {
description: "List of ordered queue entries"
type: array
items { "$ref": "#/definitions/entry" }
}
}
}
}
get_by_id {
"2.4" {
description: "Gets queue information"
request {
type: object
required: [ queue ]
properties {
queue {
description: "Queue ID"
type: string
}
}
}
response {
type: object
properties {
queue {
description: "Queue info"
"$ref": "#/definitions/queue"
}
}
}
}
}
// typescript generation hack
get_all_ex {
internal: true
"2.4": ${get_all."2.4"}
}
get_all {
"2.4" {
description: "Get all queues"
request {
type: object
properties {
name {
description: "Get only queues whose name matches this pattern (python regular expression syntax)"
type: string
}
id {
description: "List of Queue IDs used to filter results"
type: array
items { type: string }
}
tags {
description: "User-defined tags list used to filter results. Prepend '-' to tag name to indicate exclusion"
type: array
items { type: string }
}
system_tags {
description: "System tags list used to filter results. Prepend '-' to system tag name to indicate exclusion"
type: array
items { type: string }
}
page {
description: "Page number, returns a specific page out of the result list of results."
type: integer
minimum: 0
}
page_size {
description: "Page size, specifies the number of results returned in each page (last page may contain fewer results)"
type: integer
minimum: 1
}
order_by {
description: "List of field names to order by. When search_text is used, '@text_score' can be used as a field representing the text score of returned documents. Use '-' prefix to specify descending order. Optional, recommended when using page"
type: array
items { type: string }
}
search_text {
description: "Free text search query"
type: string
}
only_fields {
description: "List of document field names (nesting is supported using '.', e.g. execution.model_labels). If provided, this list defines the query's projection (only these fields will be returned for each result entry)"
type: array
items { type: string }
}
}
}
response {
type: object
properties {
queues {
description: "Queues list"
type: array
items { "$ref": "#/definitions/queue"}
}
}
}
}
}
get_default {
"2.4" {
description: ""
request {
type: object
properties {}
additionalProperties: false
}
response {
type: object
properties {
id {
description: "Queue id"
type: string
}
name {
description: "Queue name"
type: string
}
}
}
}
}
create {
"2.4" {
description: "Create a new queue"
request {
type: object
required: [ name ]
properties {
name {
description: "Queue name Unique within the company."
type: string
}
tags {
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
}
}
response {
type: object
properties {
id {
description: "New queue ID"
type: string
}
}
}
}
}
update {
"2.4" {
description: "Update queue information"
request {
type: object
required: [ queue ]
properties {
queue {
description: "Queue id"
type: string
}
name {
description: "Queue name Unique within the company."
type: string
}
tags {
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
}
}
response {
type: object
properties {
updated {
description: "Number of queues updated (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
fields {
description: "Updated fields names and values"
type: object
additionalProperties: true
}
}
}
}
}
delete {
"2.4" {
description: "Deletes a queue. If the queue is not empty and force is not set to true, queue will not be deleted."
request {
type: object
required: [ queue ]
properties {
queue {
description: "Queue id"
type: string
}
force {
description: "Force delete of non-empty queue. Defaults to false"
type: boolean
default: false
}
}
}
response {
type: object
properties {
deleted {
description: "Number of queues deleted (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
}
}
}
}
add_task {
"2.4" {
description: "Adds a task entry to the queue."
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
}
}
response {
type: object
properties {
added {
description: "Number of tasks added (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
}
}
}
}
get_next_task {
"2.4" {
description: "Gets the next task from the top of the queue (FIFO). The task entry is removed from the queue."
request {
type: object
required: [ queue ]
properties {
queue {
description: "Queue id"
type: string
}
}
}
response {
type: object
properties {
entry {
description: "Entry information"
"$ref": "#/definitions/entry"
}
}
}
}
}
remove_task {
"2.4" {
description: "Removes a task entry from the queue."
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
}
}
response {
type: object
properties {
removed {
description: "Number of tasks removed (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
}
}
}
}
move_task_forward: {
"2.4" {
description: "Moves a task entry one step forward towards the top of the queue."
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
count {
description: "Number of positions in the queue to move the task forward relative to the current position. Optional, the default value is 1."
type: integer
}
}
}
response {
type: object
properties {
position {
description: "The new position of the task entry in the queue (index, -1 represents bottom of queue)"
type: integer
}
}
}
}
}
move_task_backward: {
"2.4" {
description: ""
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
count {
description: "Number of positions in the queue to move the task forward relative to the current position. Optional, the default value is 1."
type: integer
}
}
}
response {
type: object
properties {
position {
description: "The new position of the task entry in the queue (index, -1 represents bottom of queue)"
type: integer
}
}
}
}
}
move_task_to_front: {
"2.4" {
description: ""
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
}
}
response {
type: object
properties {
position {
description: "The new position of the task entry in the queue (index, -1 represents bottom of queue)"
type: integer
}
}
}
}
}
move_task_to_back: {
"2.4" {
description: ""
request {
type: object
required: [
queue
task
]
properties {
queue {
description: "Queue id"
type: string
}
task {
description: "Task id"
type: string
}
}
}
response {
type: object
properties {
position {
description: "The new position of the task entry in the queue (index, -1 represents bottom of queue)"
type: integer
}
}
}
}
}
get_queue_metrics : {
"2.4" {
description: "Returns metrics of the company queues. The metrics are avaraged in the specified interval."
request {
type: object
required: [from_date, to_date, interval]
properties: {
from_date {
description: "Starting time (in seconds from epoch) for collecting metrics"
type: number
}
to_date {
description: "Ending time (in seconds from epoch) for collecting metrics"
type: number
}
interval {
description: "Time interval in seconds for a single metrics point. The minimal value is 1"
type: integer
}
queue_ids {
description: "List of queue ids to collect metrics for. If not provided or empty then all then average metrics across all the company queues will be returned."
type: array
items { type: string }
}
}
}
response {
type: object
properties: {
queues {
type: array
description: "List of the requested queues with their metrics. If no queue ids were requested then 'all' queue is returned with the metrics averaged accross all the company queues."
items { "$ref": "#/definitions/queue_metrics" }
}
}
}
}
}
}

View File

@@ -0,0 +1,123 @@
_description: "server utilities"
_default {
internal: true
allow_roles: ["root", "system"]
}
get_stats {
"2.1" {
description: "Get the server collected statistics."
request {
type: object
properties {
interval {
description: "The period for statistics collection in seconds."
type: long
}
}
}
response {
type: object
properties: {
}
}
}
}
config {
"2.1" {
description: "Get server configuration. Secure section is not returned."
request {
type: object
properties {
path {
description: "Path of config value. Defaults to root"
type: string
}
}
}
response {
type: object
properties {
}
}
}
}
info {
authorize = false
allow_roles = [ "*" ]
"2.1" {
description: "Get server information, including version and build number"
request {
type: object
properties {
}
}
response {
type: object
properties {
version {
description: "Version string"
type: string
}
build {
description: "Build number"
type: string
}
commit {
description: "VCS commit number"
type: string
}
}
}
}
}
endpoints {
"2.1" {
description: "Show available endpoints"
request {
type: object
properties {
}
}
response {
type: object
properties {
}
}
}
}
report_stats_option {
"2.4" {
description: "Get or set the report statistics option per-company"
request {
type: object
properties {
enabled {
description: "If provided, sets the report statistics option (true/false)"
type: boolean
}
}
}
response {
type: object
properties {
enabled {
description: "Returns the current report stats option value"
type: boolean
}
enabled_time {
description: "If enabled, returns the time at which option was enabled"
type: string
format: date-time
}
enabled_version {
description: "If enabled, returns the server version at the time option was enabled"
type: string
}
enabled_user {
description: "If enabled, returns Id of the user who enabled the option"
type: string
}
}
}
}
}

View File

@@ -120,9 +120,83 @@ _definitions {
frame_per_roi
]
}
artifact_type_data {
type: object
properties {
preview {
description: "Description or textual data"
type: string
}
content_type {
description: "System defined raw data content type"
type: string
}
data_hash {
description: "Hash of raw data, without any headers or descriptive parts"
type: string
}
}
}
artifact {
type: object
required: [key, type]
properties {
key {
description: "Entry key"
type: string
}
type {
description: "System defined type"
type: string
}
mode {
description: "System defined input/output indication"
type: string
enum: [
input
output
]
default: output
}
uri {
description: "Raw data location"
type: string
}
content_size {
description: "Raw data length in bytes"
type: integer
}
hash {
description: "Hash of entire raw data"
type: string
}
timestamp {
description: "Epoch time when artifact was created"
type: integer
}
type_data {
description: "Additional fields defined by the system"
"$ref": "#/definitions/artifact_type_data"
}
display_data {
description: "User-defined list of key/value pairs, sorted"
type: array
items {
type: array
items {
type: string # can also be a number... TODO: upgrade the generator
}
}
}
}
}
execution {
type: object
properties {
queue {
description: "Queue ID where task was queued."
type: string
}
parameters {
description: "Json object containing the Task parameters"
type: object
@@ -149,12 +223,22 @@ _definitions {
description: """Framework related to the task. Case insensitive. Mandatory for Training tasks. """
type: string
}
docker_cmd {
description: "Command for running docker script for the execution of the task"
type: string
}
artifacts {
description: "Task artifacts"
type: array
items { "$ref": "#/definitions/artifact" }
}
}
}
task_status_enum {
type: string
enum: [
created
queued
in_progress
stopped
published
@@ -183,21 +267,16 @@ _definitions {
description: "Variant name"
type: string
}
type {
description: "Event type"
type: string
}
timestamp {
description: "Event report time (UTC)"
type: string
format: "date-time"
}
iter {
description: "Iteration number"
type: integer
}
value {
description: "Value"
description: "Last value reported"
type: number
}
min_value {
description: "Minimum value reported"
type: number
}
max_value {
description: "Maximum value reported"
type: number
}
}
@@ -278,7 +357,12 @@ _definitions {
"$ref": "#/definitions/script"
}
tags {
description: "Tags list"
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
@@ -300,6 +384,15 @@ _definitions {
type: string
format: "date-time"
}
last_worker {
description: "ID of last worker that handled the task"
type: string
}
last_worker_report {
description: "Last time a worker reported while working on this task"
type: string
format: "date-time"
}
last_update {
description: "Last time this task was created, updated, changed or events for this task were reported"
type: string
@@ -392,7 +485,12 @@ get_all {
items { type: string }
}
tags {
description: "List of task tags. Use '-' prefix to exclude tags"
description: "List of task user-defined tags. Use '-' prefix to exclude tags"
type: array
items { type: string }
}
system_tags {
description: "List of task system tags. Use '-' prefix to exclude system tags"
type: array
items { type: string }
}
@@ -467,7 +565,12 @@ create {
type: string
}
tags {
description: "Tags list"
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
@@ -527,7 +630,12 @@ validate {
type: string
}
tags {
description: "Tags list"
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
@@ -585,7 +693,12 @@ update {
type: string
}
tags {
description: "Tags list"
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
@@ -667,7 +780,12 @@ edit {
type: string
}
tags {
description: "Tags list"
description: "User-defined tags list"
type: array
items { type: string }
}
system_tags {
description: "System tags list. This field is reserved for system use, please don't use it."
type: array
items { type: string }
}
@@ -738,6 +856,11 @@ reset {
type: array
items { type: string }
}
dequeued {
description: "Response from queues.remove_task"
type: object
additionalProperties: true
}
frames {
description: "Response from frames.rollback"
type: object
@@ -1014,7 +1137,85 @@ publish {
}
}
}
enqueue {
"1.5" {
description: """Adds a task into a queue.
Fails if task state is not 'created'.
Fails if the following parameters in the task were not filled:
* execution.script.repository
* execution.script.entrypoint
"""
request = {
type: object
required: [
task
]
properties {
queue {
description: "Queue id. If not provided, task is added to the default queue."
type: string
}
}
} ${_references.status_change_request}
response {
type: object
properties {
queued {
description: "Number of tasks queued (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
updated {
description: "Number of tasks updated (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
fields {
description: "Updated fields names and values"
type: object
additionalProperties: true
}
}
}
}
}
dequeue {
"1.5" {
description: """Remove a task from its queue.
Fails if task status is not queued."""
request = {
type: object
required: [
task
]
} ${_references.status_change_request}
response {
type: object
properties {
dequeued {
description: "Number of tasks dequeued (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
updated {
description: "Number of tasks updated (0 or 1)"
type: integer
enum: [ 0, 1 ]
}
fields {
description: "Updated fields names and values"
type: object
additionalProperties: true
}
}
}
}
}
set_requirements {
"2.1" {
description: """Set the script requirements for a task"""
@@ -1062,7 +1263,7 @@ completed {
task
]
properties.force = ${_references.force_arg} {
description: "If not true, call fails if the task status is not created/in_progress/published"
description: "If not true, call fails if the task status is not in_progress/stopped"
}
} ${_references.status_change_request}
response {

View File

@@ -0,0 +1,483 @@
{
_description: "Provides an API for worker machines, allowing workers to report status and get tasks for execution"
_definitions {
metrics_category {
type: object
properties {
name {
type: string
description: "Name of the metrics category."
}
metric_keys {
type: array
items { type: string }
description: "The names of the metrics in the category."
}
}
}
aggregation_type {
type: string
enum: [ avg, min, max ]
description: "Metric aggregation type"
}
stat_item {
type: object
properties {
key {
type: string
description: "Name of a metric"
}
category {
"$ref": "#/definitions/aggregation_type"
}
}
}
aggregation_stats {
type: object
properties {
aggregation {
"$ref": "#/definitions/aggregation_type"
}
values {
type: array
description: "List of values corresponding to the dates in metric statistics"
items { type: number }
}
}
}
metric_stats {
type: object
properties {
metric {
type: string
description: "Name of the metric ("cpu_usage", "memory_used" etc.)"
}
variant {
type: string
description: "Name of the metric component. Set only if 'split_by_variant' was set in the request"
}
dates {
type: array
description: "List of timestamps (in seconds from epoch) in the acceding order. The timestamps are separated by the requested interval. Timestamps where no workers activity was recorded are omitted."
items { type: integer }
}
stats {
type: array
description: "Statistics data by type"
items { "$ref": "#/definitions/aggregation_stats" }
}
}
}
worker_stats {
type: object
properties {
worker {
type: string
description: "ID of the worker"
}
metrics {
type: array
description: "List of the metrics statistics for the worker"
items { "$ref": "#/definitions/metric_stats" }
}
}
}
activity_series {
type: object
properties {
dates {
type: array
description: "List of timestamps (in seconds from epoch) in the acceding order. The timestamps are separated by the requested interval."
items {type: integer}
}
counts {
type: array
description: "List of worker counts corresponding to the timestamps in the dates list. None values are returned for the dates with no workers."
items {type: integer}
}
}
}
worker {
type: object
properties {
id {
description: "Worker ID"
type: string
}
user {
description: "Associated user (under whose credentials are used by the worker daemon)"
"$ref": "#/definitions/id_name_entry"
}
company {
description: "Associated company"
"$ref": "#/definitions/id_name_entry"
}
ip {
description: "IP of the worker"
type: string
}
register_time {
description: "Registration time"
type: string
format: "date-time"
}
last_activity_time {
description: "Last activity time (even if an error occurred)"
type: string
format: "date-time"
}
last_report_time {
description: "Last successful report time"
type: string
format: "date-time"
}
task {
description: "Task currently being run by the worker"
"$ref": "#/definitions/current_task_entry"
}
queue {
description: "Queue from which running task was taken"
"$ref": "#/definitions/queue_entry"
}
queues {
description: "List of queues on which the worker is listening"
type: array
items { "$ref": "#/definitions/queue_entry" }
}
}
}
id_name_entry {
type: object
properties {
id {
description: "Worker ID"
type: string
}
name {
description: "Worker name"
type: string
}
}
}
current_task_entry = ${_definitions.id_name_entry} {
properties {
running_time {
description: "Task running time"
type: integer
}
last_iteration {
description: "Last task iteration"
type: integer
}
}
}
queue_entry = ${_definitions.id_name_entry} {
properties {
next_task {
description: "Next task in the queue"
"$ref": "#/definitions/id_name_entry"
}
num_tasks {
description: "Number of task entries in the queue"
type: integer
}
}
}
machine_stats {
type: object
properties {
cpu_usage {
description: "Average CPU usage per core"
type: array
items { type: number }
}
gpu_usage {
description: "Average GPU usage per GPU card"
type: array
items { type: number }
}
memory_used {
description: "Used memory MBs"
type: integer
}
memory_free {
description: "Free memory MBs"
type: integer
}
gpu_memory_free {
description: "GPU free memory MBs"
type: array
items { type: integer }
}
gpu_memory_used {
description: "GPU used memory MBs"
type: array
items { type: integer }
}
network_tx {
description: "Mbytes per second"
type: integer
}
network_rx {
description: "Mbytes per second"
type: integer
}
disk_free_home {
description: "Mbytes free space of /home drive"
type: integer
}
disk_free_temp {
description: "Mbytes free space of /tmp drive"
type: integer
}
disk_read {
description: "Mbytes read per second"
type: integer
}
disk_write {
description: "Mbytes write per second"
type: integer
}
cpu_temperature {
description: "CPU temperature"
type: array
items { type: number }
}
gpu_temperature {
description: "GPU temperature"
type: array
items { type: number }
}
}
}
}
get_all {
"2.4" {
description: "Returns information on all registered workers."
request {
type: object
properties {
last_seen {
description: """Filter out workers not active for more than last_seen seconds.
A value or 0 or 'none' will disable the filter."""
type: integer
default: 3600
}
}
}
response {
type: object
properties {
workers {
type: array
items { "$ref": "#/definitions/worker" }
}
}
}
}
}
register {
"2.4" {
description: "Register a worker in the system. Called by the Worker Daemon."
request {
required: [ worker ]
type: object
properties {
worker {
description: "Worker id. Must be unique in company."
type: string
}
timeout {
description: "Registration timeout in seconds. If timeout seconds have passed since the worker's last call to register or status_report, the worker is automatically removed from the list of registered workers."
type: integer
default: 600
}
queues {
description: "List of queue IDs on which the worker is listening."
type: array
items { type: string }
}
}
}
response {
type: object
properties {}
}
}
}
unregister {
"2.4" {
description: "Unregister a worker in the system. Called by the Worker Daemon."
request {
required: [ worker ]
type: object
properties {
worker {
description: "Worker id. Must be unique in company."
type: string
}
}
}
response {
type: object
properties {}
}
}
}
status_report {
"2.4" {
description: "Called periodically by the worker daemon to report machine status"
request {
required: [
worker
timestamp
]
type: object
properties {
worker {
description: "Worker id."
type: string
}
task {
description: "ID of a task currently being run by the worker. If no task is sent, the worker's task field will be cleared."
type: string
}
queue {
description: "ID of the queue from which task was received. If no queue is sent, the worker's queue field will be cleared."
type: string
}
queues {
description: "List of queue IDs on which the worker is listening. If null, the worker's queues list will not be updated."
type: array
items { type: string }
}
timestamp {
description: "UNIX time in seconds since epoch."
type: integer
}
machine_stats {
description: "The machine statistics."
"$ref": "#/definitions/machine_stats"
}
}
}
response {
type: object
properties {}
}
}
}
get_metric_keys {
"2.4" {
description: "Returns worker statistics metric keys grouped by categories."
request {
type: object
properties {
worker_ids {
description: "List of worker ids to collect metrics for. If not provided or empty then all the company workers metrics are analyzed."
type: array
items { type: string }
}
}
}
response {
type: object
properties {
categories {
type: array
description: "List of unique metric categories found in the statistics of the requested workers."
items { "$ref": "#/definitions/metrics_category" }
}
}
}
}
}
get_stats {
"2.4" {
description: "Returns statistics for the selected workers and time range aggregated by date intervals."
request {
type: object
required: [ from_date, to_date, interval, items ]
properties {
worker_ids {
description: "List of worker ids to collect metrics for. If not provided or empty then all the company workers metrics are analyzed."
type: array
items { type: string }
}
from_date {
description: "Starting time (in seconds from epoch) for collecting statistics"
type: number
}
to_date {
description: "Ending time (in seconds from epoch) for collecting statistics"
type: number
}
interval {
description: "Time interval in seconds for a single statistics point. The minimal value is 1"
type: integer
}
items {
description: "List of metric keys and requested statistics"
type: array
items { "$ref": "#/definitions/stat_item" }
}
split_by_variant {
description: "If true then break statistics by hardware sub types"
type: boolean
default: false
}
}
}
response {
type: object
properties {
workers {
type: array
description: "List of the requested workers with their statistics"
items { "$ref": "#/definitions/worker_stats" }
}
}
}
}
}
get_activity_report {
"2.4" {
description: "Returns count of active company workers in the selected time range."
request {
type: object
required: [ from_date, to_date, interval ]
properties {
from_date {
description: "Starting time (in seconds from epoch) for collecting statistics"
type: number
}
to_date {
description: "Ending time (in seconds from epoch) for collecting statistics"
type: number
}
interval {
description: "Time interval in seconds for a single statistics point. The minimal value is 1"
type: integer
}
}
}
response {
type: object
properties {
total {
description: "Activity series that include all the workers that sent reports in the given time interval."
"$ref": "#/definitions/activity_series"
}
active {
description: "Activity series that include only workers that worked on a task in the given time interval."
"$ref": "#/definitions/activity_series"
}
}
}
}
}
}

View File

@@ -7,16 +7,18 @@ from werkzeug.exceptions import BadRequest
import database
from apierrors.base import BaseError
from bll.statistics.stats_reporter import StatisticsReporter
from config import config
from init_data import init_es_data, init_mongo_data
from service_repo import ServiceRepo, APICall
from service_repo.auth import AuthType
from service_repo.errors import PathParsingError
from timing_context import TimingContext
from updates import check_updates_thread
from utilities import json
from init_data import init_es_data, init_mongo_data
app = Flask(__name__, static_url_path="/static")
CORS(app, supports_credentials=True, **config.get("apiserver.cors"))
CORS(app, **config.get("apiserver.cors"))
Compress(app)
log = config.logger(__file__)
@@ -35,6 +37,10 @@ ServiceRepo.load("services")
log.info(f"Exposed Services: {' '.join(ServiceRepo.endpoint_names())}")
check_updates_thread.start()
StatisticsReporter.start()
@app.before_first_request
def before_app_first_request():
pass
@@ -52,7 +58,9 @@ def before_request():
content, content_type = ServiceRepo.handle_call(call)
headers = {}
if call.result.filename:
headers["Content-Disposition"] = f"attachment; filename={call.result.filename}"
headers[
"Content-Disposition"
] = f"attachment; filename={call.result.filename}"
if call.result.headers:
headers.update(call.result.headers)
@@ -63,7 +71,12 @@ def before_request():
if call.result.cookies:
for key, value in call.result.cookies.items():
response.set_cookie(key, value, **config.get("apiserver.auth.cookies"))
if value is None:
response.set_cookie(key, "", expires=0)
else:
response.set_cookie(
key, value, **config.get("apiserver.auth.cookies")
)
return response
except Exception as ex:
@@ -104,7 +117,6 @@ def update_call_data(call, req):
form[key] = True
elif form[key].lower() == "false":
form[key] = False
# NOTE: dict() form data to make sure we won't pass along a MultiDict or some other nasty crap
call.data = json_body or form or {}

View File

@@ -7,8 +7,7 @@ from jsonmodels import models
from six import string_types
import database
import timing_context
from timing_context import TimingContext
from timing_context import TimingContext, TimingStats
from utilities import json
from .auth import Identity
from .auth import Payload as AuthPayload
@@ -104,7 +103,7 @@ class DataContainer(object):
if self._batched_data:
try:
data_model = [cls(**item) for item in self._batched_data]
except TypeError as ex:
except (ValueError, TypeError) as ex:
raise CallParsingError(str(ex))
for m in data_model:
@@ -112,7 +111,7 @@ class DataContainer(object):
else:
try:
data_model = cls(**self.data)
except TypeError as ex:
except (ValueError, TypeError) as ex:
raise CallParsingError(str(ex))
if not self.schema_validator.enabled:
@@ -182,8 +181,6 @@ class APICallResult(DataContainer):
traceback=self._traceback,
extra=self._extra,
)
if self.log_data:
res["data"] = self.data
return res
def copy_from(self, result):
@@ -258,6 +255,7 @@ class MissingIdentity(Exception):
class APICall(DataContainer):
HEADER_AUTHORIZATION = "Authorization"
HEADER_REAL_IP = "X-Real-IP"
HEADER_FORWARDED_FOR = "X-Forwarded-For"
""" Standard headers """
_transaction_headers = ("X-Trains-Trx",)
@@ -308,8 +306,6 @@ class APICall(DataContainer):
):
super(APICall, self).__init__(data=data, batched_data=batched_data)
timing_context.clear()
self._id = database.utils.id()
self._files = files # currently dic of key to flask's FileStorage)
self._start_ts = time.time()
@@ -387,8 +383,13 @@ class APICall(DataContainer):
@property
def real_ip(self):
real_ip = self.get_header(self.HEADER_REAL_IP)
return real_ip or self._remote_addr or "untrackable"
""" Obtain visitor's IP address """
return (
self.get_header(self.HEADER_FORWARDED_FOR)
or self.get_header(self.HEADER_REAL_IP)
or self._remote_addr
or "untrackable"
)
@property
def failed(self):
@@ -510,7 +511,7 @@ class APICall(DataContainer):
def mark_end(self):
self._end_ts = time.time()
self._duration = int((self._end_ts - self._start_ts) * 1000)
self.stats = timing_context.stats()
self.stats = TimingStats.aggregate()
def get_response(self):
def make_version_number(version):

View File

@@ -1,19 +1,19 @@
import base64
from datetime import datetime
import jwt
from mongoengine import Q
from database.errors import translate_errors_context
from database.model.company import Company
from database.utils import get_options
from database.model.auth import User, Entities, Credentials
from apierrors import errors
from config import config
from database.errors import translate_errors_context
from database.model.auth import User, Entities, Credentials
from database.model.company import Company
from database.utils import get_options
from timing_context import TimingContext
from .payload import Payload, Token, Basic, AuthType
from .identity import Identity
from .fixed_user import FixedUser
from .identity import Identity
from .payload import Payload, Token, Basic, AuthType
log = config.logger(__file__)
@@ -38,12 +38,16 @@ def authorize_token(jwt_token, *_, **__):
return Token.from_encoded_token(jwt_token)
except jwt.exceptions.InvalidKeyError as ex:
raise errors.unauthorized.InvalidToken('jwt invalid key error', reason=ex.args[0])
raise errors.unauthorized.InvalidToken(
"jwt invalid key error", reason=ex.args[0]
)
except jwt.InvalidTokenError as ex:
raise errors.unauthorized.InvalidToken('invalid jwt token', reason=ex.args[0])
raise errors.unauthorized.InvalidToken("invalid jwt token", reason=ex.args[0])
except ValueError as ex:
log.exception('Failed while processing token: %s' % ex.args[0])
raise errors.unauthorized.InvalidToken('failed processing token', reason=ex.args[0])
log.exception("Failed while processing token: %s" % ex.args[0])
raise errors.unauthorized.InvalidToken(
"failed processing token", reason=ex.args[0]
)
def authorize_credentials(auth_data, service, action, call_data_items):
@@ -58,6 +62,8 @@ def authorize_credentials(auth_data, service, action, call_data_items):
query = Q(credentials__match=Credentials(key=access_key, secret=secret_key))
fixed_user = None
if FixedUser.enabled():
fixed_user = FixedUser.get_by_username(access_key)
if fixed_user:
@@ -67,9 +73,14 @@ def authorize_credentials(auth_data, service, action, call_data_items):
with TimingContext("mongo", "user_by_cred"), translate_errors_context('authorizing request'):
user = User.objects(query).first()
if not user:
raise errors.unauthorized.InvalidCredentials('failed to locate provided credentials')
if not user:
raise errors.unauthorized.InvalidCredentials('failed to locate provided credentials')
if not fixed_user:
# In case these are proper credentials, update last used time
User.objects(id=user.id, credentials__key=access_key).update(
**{"set__credentials__$__last_used": datetime.utcnow()}
)
with TimingContext("mongo", "company_by_id"):
company = Company.objects(id=user.company).only('id', 'name').first()
@@ -85,13 +96,13 @@ def authorize_credentials(auth_data, service, action, call_data_items):
return basic
def authorize_impersonation(user, identity, service, action, call_data_items):
def authorize_impersonation(user, identity, service, action, call):
""" Returns a new basic object (auth payload)"""
if not user:
raise ValueError('missing user')
raise ValueError("missing user")
company = Company.objects(id=user.company).only('id', 'name').first()
company = Company.objects(id=user.company).only("id", "name").first()
if not company:
raise errors.unauthorized.InvalidCredentials('invalid user company')
raise errors.unauthorized.InvalidCredentials("invalid user company")
return Payload(auth_type=None, identity=identity)

View File

@@ -19,7 +19,7 @@ class FixedUser:
self.user_id = hashlib.md5(f"{self.username}:{self.password}".encode()).hexdigest()
@classmethod
def enabled(self):
def enabled(cls):
return config.get("apiserver.auth.fixed_users.enabled", False)
@classmethod

View File

@@ -30,6 +30,8 @@ def get_secret_key(length=50):
Create a random secret key.
Taken from the Django project.
NOTE: asterisk is not supported due to issues with environment variables containing
asterisks (in case the secret key is stored in an environment variable)
"""
chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&*(-_=+)'
chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&(-_=+)'
return get_random_string(length, chars)

View File

@@ -76,7 +76,7 @@ class Endpoint(object):
Provided endpoints and their schemas on a best-effort basis.
"""
d = {
"min_version": self.min_version,
"min_version": str(self.min_version),
"required_fields": self.required_fields,
"request_data_model": None,
"response_data_model": None,

View File

@@ -12,7 +12,7 @@ from config import config
log = config.logger(__file__)
@attr.s(auto_attribs=True, auto_exc=True)
@attr.s(auto_attribs=True, cmp=False)
class FastValidationError(Exception):
error: fastjsonschema.JsonSchemaException
data: dict

View File

@@ -1,10 +1,10 @@
import re
from importlib import import_module
from itertools import chain
from typing import cast, Iterable, List, MutableMapping
from pathlib import Path
from typing import cast, Iterable, List, MutableMapping, Optional, Tuple
import jsonmodels.models
from pathlib import Path
import timing_context
from apierrors import APIError
@@ -30,7 +30,11 @@ class ServiceRepo(object):
_version_required = config.get("apiserver.version.required")
""" If version is required, parsing will fail for endpoint paths that do not contain a valid version """
_max_version = PartialVersion("2.1")
_check_max_version = config.get("apiserver.version.check_max_version")
"""If the check is set, parsing will fail for endpoint request with the version that is grater than the current
maximum """
_max_version = PartialVersion("2.4")
""" Maximum version number (the highest min_version value across all endpoints) """
_endpoint_exp = (
@@ -133,7 +137,7 @@ class ServiceRepo(object):
return cls._max_version
@classmethod
def _get_endpoint(cls, name, version):
def _get_endpoint(cls, name, version) -> Optional[Endpoint]:
versions = cls._endpoints.get(name)
if not versions:
return None
@@ -144,7 +148,7 @@ class ServiceRepo(object):
return None
@classmethod
def _resolve_endpoint_from_call(cls, call):
def _resolve_endpoint_from_call(cls, call: APICall) -> Optional[Endpoint]:
assert isinstance(call, APICall)
endpoint = cls._get_endpoint(
call.endpoint_name, call.requested_endpoint_version
@@ -167,7 +171,7 @@ class ServiceRepo(object):
return endpoint
@classmethod
def parse_endpoint_path(cls, path):
def parse_endpoint_path(cls, path: str) -> Tuple[PartialVersion, str]:
""" Parse endpoint version, service and action from request path. """
m = cls._endpoint_exp.match(path)
if not m:
@@ -182,14 +186,14 @@ class ServiceRepo(object):
version = PartialVersion(version)
except ValueError as e:
raise RequestPathHasInvalidVersion(version=version, reason=e)
if version > cls._max_version:
if cls._check_max_version and version > cls._max_version:
raise InvalidVersionError(
f"Invalid API version (max. supported version is {cls._max_version})"
)
return version, endpoint_name
@classmethod
def _should_return_stack(cls, code, subcode):
def _should_return_stack(cls, code: int, subcode: int) -> bool:
if not cls._return_stack or code not in cls._return_stack_on_code:
return False
if subcode is None:
@@ -202,7 +206,7 @@ class ServiceRepo(object):
return subcode in subcode_list
@classmethod
def _validate_call(cls, call):
def _validate_call(cls, call: APICall) -> Optional[Endpoint]:
endpoint = cls._resolve_endpoint_from_call(call)
if call.failed:
return
@@ -210,11 +214,13 @@ class ServiceRepo(object):
return endpoint
@classmethod
def validate_call(cls, call):
def validate_call(cls, call: APICall):
cls._validate_call(call)
@classmethod
def _get_company(cls, call, endpoint=None, ignore_error=False):
def _get_company(
cls, call: APICall, endpoint: Endpoint = None, ignore_error: bool = False
) -> Optional[str]:
authorize = endpoint and endpoint.authorize
if ignore_error or not authorize:
try:
@@ -224,7 +230,7 @@ class ServiceRepo(object):
return call.identity.company
@classmethod
def handle_call(cls, call):
def handle_call(cls, call: APICall):
try:
assert isinstance(call, APICall)

View File

@@ -150,7 +150,7 @@ def validate_impersonation(endpoint, call):
),
service=service,
action=action,
call_data_items=call.batched_data,
call=call,
)
else:
return False

View File

@@ -31,10 +31,8 @@ log = config.logger(__file__)
request_data_model=GetTokenRequest,
response_data_model=GetTokenResponse,
)
def login(call):
def login(call: APICall, *_, **__):
""" Generates a token based on the authenticated user (intended for use with credentials) """
assert isinstance(call, APICall)
call.result.data_model = AuthBLL.get_token_for_user(
user_id=call.identity.user,
company_id=call.identity.company,
@@ -47,6 +45,11 @@ def login(call):
] = call.result.data_model.token
@endpoint("auth.logout", min_version="2.2")
def logout(call: APICall, *_, **__):
call.result.cookies[config.get("apiserver.auth.session_auth_cookie_name")] = None
@endpoint(
"auth.get_token_for_user",
request_data_model=GetTokenForUserRequest,
@@ -140,7 +143,8 @@ def get_credentials(call):
# we return ONLY the key IDs, never the secrets (want a secret? create new credentials)
call.result.data_model = GetCredentialsResponse(
credentials=[
CredentialsResponse(access_key=c.key) for c in user.credentials
CredentialsResponse(access_key=c.key, last_used=c.last_used)
for c in user.credentials
]
)

View File

@@ -5,7 +5,12 @@ from operator import itemgetter
import six
from apierrors import errors
from apimodels.events import (
MultiTaskScalarMetricsIterHistogramRequest,
ScalarMetricsIterHistogramRequest,
)
from bll.event import EventBLL
from bll.event.event_metrics import EventMetrics
from bll.task import TaskBLL
from service_repo import APICall, endpoint
from utilities import json
@@ -15,28 +20,24 @@ event_bll = EventBLL()
@endpoint("events.add")
def add(call, company_id, req_model):
assert isinstance(call, APICall)
added, batch_errors = event_bll.add_events(company_id, [call.data.copy()], call.worker)
call.result.data = dict(
added=added,
errors=len(batch_errors)
def add(call: APICall, company_id, req_model):
data = call.data.copy()
allow_locked = data.pop("allow_locked", False)
added, batch_errors = event_bll.add_events(
company_id, [data], call.worker, allow_locked_tasks=allow_locked
)
call.result.data = dict(added=added, errors=len(batch_errors))
call.kpis["events"] = 1
@endpoint("events.add_batch")
def add_batch(call, company_id, req_model):
assert isinstance(call, APICall)
def add_batch(call: APICall, company_id, req_model):
events = call.batched_data
if events is None or len(events) == 0:
raise errors.bad_request.BatchContainsNoItems()
added, batch_errors = event_bll.add_events(company_id, events, call.worker)
call.result.data = dict(
added=added,
errors=len(batch_errors)
)
call.result.data = dict(added=added, errors=len(batch_errors))
call.kpis["events"] = len(events)
@@ -48,16 +49,16 @@ def get_task_log(call, company_id, req_model):
scroll_id = call.data.get("scroll_id")
batch_size = int(call.data.get("batch_size") or 500)
events, scroll_id, total_events = event_bll.scroll_task_events(
company_id, task_id, order,
company_id,
task_id,
order,
event_type="log",
batch_size=batch_size,
scroll_id=scroll_id)
call.result.data = dict(
events=events,
returned=len(events),
total=total_events,
scroll_id=scroll_id,
)
call.result.data = dict(
events=events, returned=len(events), total=total_events, scroll_id=scroll_id
)
@endpoint("events.get_task_log", min_version="1.7", required_fields=["task"])
@@ -70,7 +71,7 @@ def get_task_log_v1_7(call, company_id, req_model):
scroll_id = call.data.get("scroll_id")
batch_size = int(call.data.get("batch_size") or 500)
scroll_order = 'asc' if (from_ == 'head') else 'desc'
scroll_order = "asc" if (from_ == "head") else "desc"
events, scroll_id, total_events = event_bll.scroll_task_events(
company_id=company_id,
@@ -78,54 +79,57 @@ def get_task_log_v1_7(call, company_id, req_model):
order=scroll_order,
event_type="log",
batch_size=batch_size,
scroll_id=scroll_id
scroll_id=scroll_id,
)
if scroll_order != order:
events = events[::-1]
call.result.data = dict(
events=events,
returned=len(events),
total=total_events,
scroll_id=scroll_id,
events=events, returned=len(events), total=total_events, scroll_id=scroll_id
)
@endpoint('events.download_task_log', required_fields=['task'])
@endpoint("events.download_task_log", required_fields=["task"])
def download_task_log(call, company_id, req_model):
task_id = call.data['task']
task_id = call.data["task"]
task_bll.assert_exists(company_id, task_id, allow_public=True)
line_type = call.data.get('line_type', 'json').lower()
line_format = str(call.data.get('line_format', '{asctime} {worker} {level} {msg}'))
line_type = call.data.get("line_type", "json").lower()
line_format = str(call.data.get("line_format", "{asctime} {worker} {level} {msg}"))
is_json = (line_type == 'json')
is_json = line_type == "json"
if not is_json:
if not line_format:
raise errors.bad_request.MissingRequiredFields('line_format is required for plain text lines')
raise errors.bad_request.MissingRequiredFields(
"line_format is required for plain text lines"
)
# validate line format placeholders
valid_task_log_fields = {'asctime', 'timestamp', 'level', 'worker', 'msg'}
valid_task_log_fields = {"asctime", "timestamp", "level", "worker", "msg"}
invalid_placeholders = set()
while True:
try:
line_format.format(**dict.fromkeys(valid_task_log_fields | invalid_placeholders))
line_format.format(
**dict.fromkeys(valid_task_log_fields | invalid_placeholders)
)
break
except KeyError as e:
invalid_placeholders.add(e.args[0])
except Exception as e:
raise errors.bad_request.FieldsValueError('invalid line format', error=e.args[0])
raise errors.bad_request.FieldsValueError(
"invalid line format", error=e.args[0]
)
if invalid_placeholders:
raise errors.bad_request.FieldsValueError(
'undefined placeholders in line format',
placeholders=invalid_placeholders
"undefined placeholders in line format",
placeholders=invalid_placeholders,
)
# make sure line_format has a trailing newline
line_format = line_format.rstrip('\n') + '\n'
line_format = line_format.rstrip("\n") + "\n"
def generate():
scroll_id = None
@@ -137,30 +141,30 @@ def download_task_log(call, company_id, req_model):
order="asc",
event_type="log",
batch_size=batch_size,
scroll_id=scroll_id
scroll_id=scroll_id,
)
if not log_events:
break
for ev in log_events:
ev['asctime'] = ev.pop('@timestamp')
ev["asctime"] = ev.pop("@timestamp")
if is_json:
ev.pop('type')
ev.pop('task')
yield json.dumps(ev) + '\n'
ev.pop("type")
ev.pop("task")
yield json.dumps(ev) + "\n"
else:
try:
yield line_format.format(**ev)
except KeyError as ex:
raise errors.bad_request.FieldsValueError(
'undefined placeholders in line format',
placeholders=[str(ex)]
"undefined placeholders in line format",
placeholders=[str(ex)],
)
if len(log_events) < batch_size:
break
call.result.filename = 'task_%s.log' % task_id
call.result.content_type = 'text/plain'
call.result.filename = "task_%s.log" % task_id
call.result.content_type = "text/plain"
call.result.raw_data = generate()
@@ -169,7 +173,9 @@ def get_vector_metrics_and_variants(call, company_id, req_model):
task_id = call.data["task"]
task_bll.assert_exists(company_id, task_id, allow_public=True)
call.result.data = dict(
metrics=event_bll.get_metrics_and_variants(company_id, task_id, "training_stats_vector")
metrics=event_bll.get_metrics_and_variants(
company_id, task_id, "training_stats_vector"
)
)
@@ -178,39 +184,46 @@ def get_scalar_metrics_and_variants(call, company_id, req_model):
task_id = call.data["task"]
task_bll.assert_exists(company_id, task_id, allow_public=True)
call.result.data = dict(
metrics=event_bll.get_metrics_and_variants(company_id, task_id, "training_stats_scalar")
metrics=event_bll.get_metrics_and_variants(
company_id, task_id, "training_stats_scalar"
)
)
# todo: !!! currently returning 10,000 records. should decide on a better way to control it
@endpoint("events.vector_metrics_iter_histogram", required_fields=["task", "metric", "variant"])
@endpoint(
"events.vector_metrics_iter_histogram",
required_fields=["task", "metric", "variant"],
)
def vector_metrics_iter_histogram(call, company_id, req_model):
task_id = call.data["task"]
task_bll.assert_exists(company_id, task_id, allow_public=True)
metric = call.data["metric"]
variant = call.data["variant"]
iterations, vectors = event_bll.get_vector_metrics_per_iter(company_id, task_id, metric, variant)
iterations, vectors = event_bll.get_vector_metrics_per_iter(
company_id, task_id, metric, variant
)
call.result.data = dict(
metric=metric,
variant=variant,
vectors=vectors,
iterations=iterations
metric=metric, variant=variant, vectors=vectors, iterations=iterations
)
@endpoint("events.get_task_events", required_fields=["task"])
def get_task_events(call, company_id, req_model):
def get_task_events(call, company_id, _):
task_id = call.data["task"]
batch_size = call.data.get("batch_size")
event_type = call.data.get("event_type")
scroll_id = call.data.get("scroll_id")
order = call.data.get("order") or "asc"
task_bll.assert_exists(company_id, task_id, allow_public=True)
result = event_bll.get_task_events(
company_id, task_id,
company_id,
task_id,
sort=[{"timestamp": {"order": order}}],
event_type=event_type,
scroll_id=scroll_id
scroll_id=scroll_id,
size=batch_size,
)
call.result.data = dict(
@@ -229,11 +242,12 @@ def get_scalar_metric_data(call, company_id, req_model):
task_bll.assert_exists(company_id, task_id, allow_public=True)
result = event_bll.get_task_events(
company_id, task_id,
company_id,
task_id,
event_type="training_stats_scalar",
sort=[{"iter": {"order": "desc"}}],
metric=metric,
scroll_id=scroll_id
scroll_id=scroll_id,
)
call.result.data = dict(
@@ -248,35 +262,54 @@ def get_scalar_metric_data(call, company_id, req_model):
def get_task_latest_scalar_values(call, company_id, req_model):
task_id = call.data["task"]
task = task_bll.assert_exists(company_id, task_id, allow_public=True)
metrics, last_timestamp = event_bll.get_task_latest_scalar_values(company_id, task_id)
es_index = EventBLL.get_index_name(company_id, "*")
metrics, last_timestamp = event_bll.get_task_latest_scalar_values(
company_id, task_id
)
es_index = EventMetrics.get_index_name(company_id, "*")
last_iters = event_bll.get_last_iters(es_index, task_id, None, 1)
call.result.data = dict(
metrics=metrics,
last_iter=last_iters[0] if last_iters else 0,
name=task.name,
status=task.status,
last_timestamp=last_timestamp
last_timestamp=last_timestamp,
)
# todo: should not repeat iter (x-axis) for each metric/variant, JS client should get raw data and fill gaps if needed
@endpoint("events.scalar_metrics_iter_histogram", required_fields=["task"])
def scalar_metrics_iter_histogram(call, company_id, req_model):
task_id = call.data["task"]
task_bll.assert_exists(call.identity.company, task_id, allow_public=True)
metrics = event_bll.get_scalar_metrics_average_per_iter(company_id, task_id)
@endpoint(
"events.scalar_metrics_iter_histogram",
request_data_model=ScalarMetricsIterHistogramRequest,
)
def scalar_metrics_iter_histogram(
call, company_id, req_model: ScalarMetricsIterHistogramRequest
):
task_bll.assert_exists(call.identity.company, req_model.task, allow_public=True)
metrics = event_bll.metrics.get_scalar_metrics_average_per_iter(
company_id, task_id=req_model.task, samples=req_model.samples, key=req_model.key
)
call.result.data = metrics
@endpoint("events.multi_task_scalar_metrics_iter_histogram", required_fields=["tasks"])
def multi_task_scalar_metrics_iter_histogram(call, company_id, req_model):
task_ids = call.data["tasks"]
@endpoint(
"events.multi_task_scalar_metrics_iter_histogram",
request_data_model=MultiTaskScalarMetricsIterHistogramRequest,
)
def multi_task_scalar_metrics_iter_histogram(
call, company_id, req_model: MultiTaskScalarMetricsIterHistogramRequest
):
task_ids = req_model.tasks
if isinstance(task_ids, six.string_types):
task_ids = [s.strip() for s in task_ids.split(",")]
# Note, bll already validates task ids as it needs their names
call.result.data = dict(
metrics=event_bll.compare_scalar_metrics_average_per_iter(company_id, task_ids, allow_public=True)
metrics=event_bll.metrics.compare_scalar_metrics_average_per_iter(
company_id,
task_ids=task_ids,
samples=req_model.samples,
allow_public=True,
key=req_model.key,
)
)
@@ -287,21 +320,27 @@ def get_multi_task_plots_v1_7(call, company_id, req_model):
scroll_id = call.data.get("scroll_id")
tasks = task_bll.assert_exists(
company_id=call.identity.company, only=('id', 'name'), task_ids=task_ids, allow_public=True
company_id=call.identity.company,
only=("id", "name"),
task_ids=task_ids,
allow_public=True,
)
# Get last 10K events by iteration and group them by unique metric+variant, returning top events for combination
result = event_bll.get_task_events(
company_id, task_ids,
company_id,
task_ids,
event_type="plot",
sort=[{"iter": {"order": "desc"}}],
size=10000,
scroll_id=scroll_id
scroll_id=scroll_id,
)
tasks = {t.id: t.name for t in tasks}
return_events = _get_top_iter_unique_events_per_task(result.events, max_iters=iters, tasks=tasks)
return_events = _get_top_iter_unique_events_per_task(
result.events, max_iters=iters, tasks=tasks
)
call.result.data = dict(
plots=return_events,
@@ -318,20 +357,26 @@ def get_multi_task_plots(call, company_id, req_model):
scroll_id = call.data.get("scroll_id")
tasks = task_bll.assert_exists(
company_id=call.identity.company, only=('id', 'name'), task_ids=task_ids, allow_public=True
company_id=call.identity.company,
only=("id", "name"),
task_ids=task_ids,
allow_public=True,
)
result = event_bll.get_task_events(
company_id, task_ids,
company_id,
task_ids,
event_type="plot",
sort=[{"iter": {"order": "desc"}}],
last_iter_count=iters,
scroll_id=scroll_id
scroll_id=scroll_id,
)
tasks = {t.id: t.name for t in tasks}
return_events = _get_top_iter_unique_events_per_task(result.events, max_iters=iters, tasks=tasks)
return_events = _get_top_iter_unique_events_per_task(
result.events, max_iters=iters, tasks=tasks
)
call.result.data = dict(
plots=return_events,
@@ -349,7 +394,7 @@ def get_task_plots_v1_7(call, company_id, req_model):
task_bll.assert_exists(call.identity.company, task_id, allow_public=True)
# events, next_scroll_id, total_events = event_bll.get_task_events(
# company_id, task_id,
# company, task_id,
# event_type="plot",
# sort=[{"iter": {"order": "desc"}}],
# last_iter_count=iters,
@@ -357,11 +402,12 @@ def get_task_plots_v1_7(call, company_id, req_model):
# get last 10K events by iteration and group them by unique metric+variant, returning top events for combination
result = event_bll.get_task_events(
company_id, task_id,
company_id,
task_id,
event_type="plot",
sort=[{"iter": {"order": "desc"}}],
size=10000,
scroll_id=scroll_id
scroll_id=scroll_id,
)
return_events = _get_top_iter_unique_events(result.events, max_iters=iters)
@@ -381,12 +427,12 @@ def get_task_plots(call, company_id, req_model):
scroll_id = call.data.get("scroll_id")
task_bll.assert_exists(call.identity.company, task_id, allow_public=True)
result = event_bll.get_task_events(
company_id, task_id,
event_type="plot",
result = event_bll.get_task_plots(
company_id,
tasks=[task_id],
sort=[{"iter": {"order": "desc"}}],
last_iter_count=iters,
scroll_id=scroll_id
last_iterations_per_plot=iters,
scroll_id=scroll_id,
)
return_events = result.events
@@ -407,7 +453,7 @@ def get_debug_images_v1_7(call, company_id, req_model):
task_bll.assert_exists(call.identity.company, task_id, allow_public=True)
# events, next_scroll_id, total_events = event_bll.get_task_events(
# company_id, task_id,
# company, task_id,
# event_type="training_debug_image",
# sort=[{"iter": {"order": "desc"}}],
# last_iter_count=iters,
@@ -415,11 +461,12 @@ def get_debug_images_v1_7(call, company_id, req_model):
# get last 10K events by iteration and group them by unique metric+variant, returning top events for combination
result = event_bll.get_task_events(
company_id, task_id,
company_id,
task_id,
event_type="training_debug_image",
sort=[{"iter": {"order": "desc"}}],
size=10000,
scroll_id=scroll_id
scroll_id=scroll_id,
)
return_events = _get_top_iter_unique_events(result.events, max_iters=iters)
@@ -441,11 +488,12 @@ def get_debug_images(call, company_id, req_model):
task_bll.assert_exists(call.identity.company, task_id, allow_public=True)
result = event_bll.get_task_events(
company_id, task_id,
company_id,
task_id,
event_type="training_debug_image",
sort=[{"iter": {"order": "desc"}}],
last_iter_count=iters,
scroll_id=scroll_id
scroll_id=scroll_id,
)
return_events = result.events
@@ -462,36 +510,39 @@ def get_debug_images(call, company_id, req_model):
@endpoint("events.delete_for_task", required_fields=["task"])
def delete_for_task(call, company_id, req_model):
task_id = call.data["task"]
allow_locked = call.data.get("allow_locked", False)
task_bll.assert_exists(company_id, task_id)
call.result.data = dict(
deleted=event_bll.delete_task_events(company_id, task_id)
deleted=event_bll.delete_task_events(
company_id, task_id, allow_locked=allow_locked
)
)
def _get_top_iter_unique_events_per_task(events, max_iters, tasks):
key = itemgetter('metric', 'variant', 'task', 'iter')
key = itemgetter("metric", "variant", "task", "iter")
unique_events = itertools.chain.from_iterable(
itertools.islice(group, max_iters)
for _, group in itertools.groupby(sorted(events, key=key, reverse=True), key=key))
for _, group in itertools.groupby(
sorted(events, key=key, reverse=True), key=key
)
)
def collect(evs, fields):
if not fields:
evs = list(evs)
return {
'name': tasks.get(evs[0].get('task')),
'plots': evs
}
return {"name": tasks.get(evs[0].get("task")), "plots": evs}
return {
str(k): collect(group, fields[1:])
for k, group in itertools.groupby(evs, key=itemgetter(fields[0]))
}
collect_fields = ('metric', 'variant', 'task', 'iter')
collect_fields = ("metric", "variant", "task", "iter")
return collect(
sorted(unique_events, key=itemgetter(*collect_fields), reverse=True),
collect_fields
collect_fields,
)
@@ -502,6 +553,8 @@ def _get_top_iter_unique_events(events, max_iters):
evs = top_unique_events[key]
if len(evs) < max_iters:
evs.append(e)
unique_events = list(itertools.chain.from_iterable(list(top_unique_events.values())))
unique_events = list(
itertools.chain.from_iterable(list(top_unique_events.values()))
)
unique_events.sort(key=lambda e: e["iter"], reverse=True)
return unique_events

View File

@@ -1,5 +1,4 @@
from datetime import datetime
from urllib.parse import urlparse
from mongoengine import Q, EmbeddedDocument
@@ -16,7 +15,6 @@ from apimodels.models import (
from bll.task import TaskBLL
from config import config
from database.errors import translate_errors_context
from database.fields import SupportedURLField
from database.model import validate_id
from database.model.model import Model
from database.model.project import Project
@@ -27,13 +25,23 @@ from database.utils import (
filter_fields,
)
from service_repo import APICall, endpoint
from services.utils import conform_tag_fields, conform_output_tags
from timing_context import TimingContext
log = config.logger(__file__)
get_all_query_options = Model.QueryParameterOptions(
pattern_fields=("name", "comment"),
fields=("ready",),
list_fields=("tags", "framework", "uri", "id", "project", "task", "parent"),
list_fields=(
"tags",
"system_tags",
"framework",
"uri",
"id",
"project",
"task",
"parent",
),
)
@@ -43,20 +51,20 @@ def get_by_id(call):
model_id = call.data["model"]
with translate_errors_context():
res = Model.get_many(
models = Model.get_many(
company=call.identity.company,
query_dict=call.data,
query=Q(id=model_id),
allow_public=True,
)
if not res:
if not models:
raise errors.bad_request.InvalidModelId(
"no such public or company model",
id=model_id,
company=call.identity.company,
)
call.result.data = {"model": res[0]}
conform_output_tags(call, models[0])
call.result.data = {"model": models[0]}
@endpoint("models.get_by_task_id", required_fields=["task"])
@@ -66,31 +74,32 @@ def get_by_task_id(call):
with translate_errors_context():
query = dict(id=task_id, company=call.identity.company)
res = Task.get(_only=["output"], **query)
if not res:
task = Task.get(_only=["output"], **query)
if not task:
raise errors.bad_request.InvalidTaskId(**query)
if not res.output:
if not task.output:
raise errors.bad_request.MissingTaskFields(field="output")
if not res.output.model:
if not task.output.model:
raise errors.bad_request.MissingTaskFields(field="output.model")
model_id = res.output.model
res = Model.objects(
model_id = task.output.model
model = Model.objects(
Q(id=model_id) & get_company_or_none_constraint(call.identity.company)
).first()
if not res:
if not model:
raise errors.bad_request.InvalidModelId(
"no such public or company model",
id=model_id,
company=call.identity.company,
)
call.result.data = {"model": res.to_proper_dict()}
model_dict = model.to_proper_dict()
conform_output_tags(call, model_dict)
call.result.data = {"model": model_dict}
@endpoint("models.get_all_ex", required_fields=[])
def get_all_ex(call):
assert isinstance(call, APICall)
def get_all_ex(call: APICall):
conform_tag_fields(call, call.data)
with translate_errors_context():
with TimingContext("mongo", "models_get_all_ex"):
models = Model.get_many_with_join(
@@ -99,14 +108,13 @@ def get_all_ex(call):
allow_public=True,
query_options=get_all_query_options,
)
conform_output_tags(call, models)
call.result.data = {"models": models}
@endpoint("models.get_all", required_fields=[])
def get_all(call):
assert isinstance(call, APICall)
def get_all(call: APICall):
conform_tag_fields(call, call.data)
with translate_errors_context():
with TimingContext("mongo", "models_get_all"):
models = Model.get_many(
@@ -116,13 +124,14 @@ def get_all(call):
allow_public=True,
query_options=get_all_query_options,
)
conform_output_tags(call, models)
call.result.data = {"models": models}
create_fields = {
"name": None,
"tags": list,
"system_tags": list,
"task": Task,
"comment": None,
"uri": None,
@@ -134,22 +143,10 @@ create_fields = {
"ready": None,
}
schemes = list(SupportedURLField.schemes)
def _validate_uri(uri):
parsed_uri = urlparse(uri)
if parsed_uri.scheme not in schemes:
raise errors.bad_request.InvalidModelUri("unsupported scheme", uri=uri)
elif not parsed_uri.path:
raise errors.bad_request.InvalidModelUri("missing path", uri=uri)
def parse_model_fields(call, valid_fields):
fields = parse_from_call(call.data, valid_fields, Model.get_fields())
tags = fields.get("tags")
if tags:
fields["tags"] = list(set(tags))
conform_tag_fields(call, fields)
return fields
@@ -251,15 +248,14 @@ def create(call, company, req_model):
if project:
validate_id(Project, company=company, project=project)
uri = req_model.uri
if uri:
_validate_uri(uri)
task = req_model.task
req_data = req_model.to_struct()
if task:
validate_task(call, req_data)
fields = filter_fields(Model, req_data)
conform_tag_fields(call, fields)
# create and save model
model = Model(
id=database.utils.id(),
@@ -276,12 +272,31 @@ def create(call, company, req_model):
def prepare_update_fields(call, fields):
fields = fields.copy()
if "uri" in fields:
_validate_uri(fields["uri"])
# clear UI cache if URI is provided (model updated)
fields["ui_cache"] = fields.pop("ui_cache", {})
if "task" in fields:
validate_task(call, fields)
if "labels" in fields:
labels = fields["labels"]
def find_other_types(iterable, type_):
res = [x for x in iterable if not isinstance(x, type_)]
try:
return set(res)
except TypeError:
# Un-hashable, probably
return res
invalid_keys = find_other_types(labels.keys(), str)
if invalid_keys:
raise errors.bad_request.ValidationError("labels keys must be strings", keys=invalid_keys)
invalid_values = find_other_types(labels.values(), int)
if invalid_values:
raise errors.bad_request.ValidationError("labels values must be integers", values=invalid_values)
conform_tag_fields(call, fields)
return fields
@@ -290,8 +305,7 @@ def validate_task(call, fields):
@endpoint("models.edit", required_fields=["model"], response_data_model=UpdateResponse)
def edit(call):
assert isinstance(call, APICall)
def edit(call: APICall):
identity = call.identity
model_id = call.data["model"]
@@ -327,13 +341,13 @@ def edit(call):
if fields:
updated = model.update(upsert=False, **fields)
conform_output_tags(call, fields)
call.result.data_model = UpdateResponse(updated=updated, fields=fields)
else:
call.result.data_model = UpdateResponse(updated=0)
def _update_model(call, model_id=None):
assert isinstance(call, APICall)
def _update_model(call: APICall, model_id=None):
identity = call.identity
model_id = model_id or call.data["model"]
@@ -358,6 +372,7 @@ def _update_model(call, model_id=None):
updated_count, updated_fields = Model.safe_update(
call.identity.company, model.id, data
)
conform_output_tags(call, updated_fields)
return UpdateResponse(updated=updated_count, fields=updated_fields)

View File

@@ -9,27 +9,32 @@ from mongoengine import Q
import database
from apierrors import errors
from apimodels.base import UpdateResponse
from apimodels.projects import GetHyperParamReq, GetHyperParamResp, ProjectReq
from bll.task import TaskBLL
from database.errors import translate_errors_context
from database.model import EntityVisibility
from database.model.model import Model
from database.model.project import Project
from database.model.task.task import Task, TaskStatus, TaskVisibility
from database.model.task.task import Task, TaskStatus
from database.utils import parse_from_call, get_options, get_company_or_none_constraint
from service_repo import APICall, endpoint
from services.utils import conform_tag_fields, conform_output_tags
from timing_context import TimingContext
task_bll = TaskBLL()
archived_tasks_cond = {"$in": [TaskVisibility.archived.value, "$tags"]}
archived_tasks_cond = {"$in": [EntityVisibility.archived.value, "$system_tags"]}
create_fields = {
"name": None,
"description": None,
"tags": list,
"system_tags": list,
"default_output_destination": None,
}
get_all_query_options = Project.QueryParameterOptions(
pattern_fields=("name", "description"), list_fields=("tags", "id")
pattern_fields=("name", "description"),
list_fields=("tags", "system_tags", "id"),
)
@@ -43,32 +48,39 @@ def get_by_id(call):
query = Q(id=project_id) & get_company_or_none_constraint(
call.identity.company
)
res = Project.objects(query).first()
if not res:
project = Project.objects(query).first()
if not project:
raise errors.bad_request.InvalidProjectId(id=project_id)
res = res.to_proper_dict()
project_dict = project.to_proper_dict()
conform_output_tags(call, project_dict)
call.result.data = {"project": res}
call.result.data = {"project": project_dict}
def make_projects_get_all_pipelines(project_ids, specific_state=None):
archived = TaskVisibility.archived.value
status_count_pipeline = [
# count tasks per project per status
{"$match": {"project": {"$in": project_ids}}},
# make sure tags is always an array (required by subsequent $in in archived_tasks_cond)
{
archived = EntityVisibility.archived.value
def ensure_system_tags():
"""
Make sure system tags is always an array (required by subsequent $in in archived_tasks_cond
"""
return {
"$addFields": {
"tags": {
"system_tags": {
"$cond": {
"if": {"$ne": [{"$type": "$tags"}, "array"]},
"if": {"$ne": [{"$type": "$system_tags"}, "array"]},
"then": [],
"else": "$tags",
"else": "$system_tags",
}
}
}
},
}
status_count_pipeline = [
# count tasks per project per status
{"$match": {"project": {"$in": project_ids}}},
ensure_system_tags(),
{
"$group": {
"_id": {
@@ -125,12 +137,12 @@ def make_projects_get_all_pipelines(project_ids, specific_state=None):
group_step = {"_id": "$project"}
for state in TaskVisibility:
for state in EntityVisibility:
if specific_state and state != specific_state:
continue
if state == TaskVisibility.active:
if state == EntityVisibility.active:
group_step[state.value] = runtime_subquery({"$not": archived_tasks_cond})
elif state == TaskVisibility.archived:
elif state == EntityVisibility.archived:
group_step[state.value] = runtime_subquery(archived_tasks_cond)
runtime_pipeline = [
@@ -141,6 +153,7 @@ def make_projects_get_all_pipelines(project_ids, specific_state=None):
"project": {"$in": project_ids},
}
},
ensure_system_tags(),
{
# for each project
"$group": group_step
@@ -151,32 +164,33 @@ def make_projects_get_all_pipelines(project_ids, specific_state=None):
@endpoint("projects.get_all_ex")
def get_all_ex(call):
assert isinstance(call, APICall)
def get_all_ex(call: APICall):
include_stats = call.data.get("include_stats")
stats_for_state = call.data.get("stats_for_state", TaskVisibility.active.value)
stats_for_state = call.data.get("stats_for_state", EntityVisibility.active.value)
if stats_for_state:
try:
specific_state = TaskVisibility(stats_for_state)
specific_state = EntityVisibility(stats_for_state)
except ValueError:
raise errors.bad_request.FieldsValueError(stats_for_state=stats_for_state)
else:
specific_state = None
conform_tag_fields(call, call.data)
with translate_errors_context(), TimingContext("mongo", "projects_get_all"):
res = Project.get_many_with_join(
projects = Project.get_many_with_join(
company=call.identity.company,
query_dict=call.data,
query_options=get_all_query_options,
allow_public=True,
)
conform_output_tags(call, projects)
if not include_stats:
call.result.data = {"projects": res}
call.result.data = {"projects": projects}
return
ids = [project["id"] for project in res]
ids = [project["id"] for project in projects]
status_count_pipeline, runtime_pipeline = make_projects_get_all_pipelines(
ids, specific_state=specific_state
)
@@ -187,11 +201,11 @@ def get_all_ex(call):
return dict(default_counts, **entry)
status_count = defaultdict(lambda: {})
key = itemgetter(TaskVisibility.archived.value)
for result in Task.objects.aggregate(*status_count_pipeline):
key = itemgetter(EntityVisibility.archived.value)
for result in Task.aggregate(*status_count_pipeline):
for k, group in groupby(sorted(result["counts"], key=key), key):
section = (
TaskVisibility.archived if k else TaskVisibility.active
EntityVisibility.archived if k else EntityVisibility.active
).value
status_count[result["_id"]][section] = set_default_count(
{
@@ -202,7 +216,7 @@ def get_all_ex(call):
runtime = {
result["_id"]: {k: v for k, v in result.items() if k != "_id"}
for result in Task.objects.aggregate(*runtime_pipeline)
for result in Task.aggregate(*runtime_pipeline)
}
def safe_get(obj, path, default=None):
@@ -219,32 +233,32 @@ def get_all_ex(call):
}
report_for_states = [
s for s in TaskVisibility if not specific_state or specific_state == s
s for s in EntityVisibility if not specific_state or specific_state == s
]
for project in res:
for project in projects:
project["stats"] = {
task_state.value: get_status_counts(project["id"], task_state.value)
for task_state in report_for_states
}
call.result.data = {"projects": res}
call.result.data = {"projects": projects}
@endpoint("projects.get_all")
def get_all(call):
assert isinstance(call, APICall)
def get_all(call: APICall):
conform_tag_fields(call, call.data)
with translate_errors_context(), TimingContext("mongo", "projects_get_all"):
res = Project.get_many(
projects = Project.get_many(
company=call.identity.company,
query_dict=call.data,
query_options=get_all_query_options,
parameters=call.data,
allow_public=True,
)
conform_output_tags(call, projects)
call.result.data = {"projects": res}
call.result.data = {"projects": projects}
@endpoint("projects.create", required_fields=["name", "description"])
@@ -254,6 +268,7 @@ def create(call):
with translate_errors_context():
fields = parse_from_call(call.data, create_fields, Project.get_fields())
conform_tag_fields(call, fields)
now = datetime.utcnow()
project = Project(
id=database.utils.id(),
@@ -271,7 +286,7 @@ def create(call):
@endpoint(
"projects.update", required_fields=["project"], response_data_model=UpdateResponse
)
def update(call):
def update(call: APICall):
"""
update
@@ -280,7 +295,6 @@ def update(call):
:return: updated - `int` - number of projects updated
fields - `[string]` - updated fields
"""
assert isinstance(call, APICall)
project_id = call.data["project"]
with translate_errors_context():
@@ -291,9 +305,11 @@ def update(call):
fields = parse_from_call(
call.data, create_fields, Project.get_fields(), discard_none_values=False
)
conform_tag_fields(call, fields)
fields["last_update"] = datetime.utcnow()
with TimingContext("mongo", "projects_update"):
updated = project.update(upsert=False, **fields)
conform_output_tags(call, fields)
call.result.data_model = UpdateResponse(updated=updated, fields=fields)
@@ -317,7 +333,7 @@ def delete(call):
(Model, errors.bad_request.ProjectHasModels),
):
res = cls.objects(
project=project_id, tags__nin=[TaskVisibility.archived.value]
project=project_id, system_tags__nin=[EntityVisibility.archived.value]
).only("id")
if res and not force:
raise error("use force=true to delete", id=project_id)
@@ -329,12 +345,33 @@ def delete(call):
call.result.data = {"deleted": 1, "disassociated_tasks": updated_count}
@endpoint("projects.get_unique_metric_variants")
def get_unique_metric_variants(call, company_id, req_model):
project_id = call.data.get("project")
@endpoint("projects.get_unique_metric_variants", request_data_model=ProjectReq)
def get_unique_metric_variants(call: APICall, company_id: str, request: ProjectReq):
metrics = task_bll.get_unique_metric_variants(
company_id, [project_id] if project_id else None
company_id, [request.project] if request.project else None
)
call.result.data = {"metrics": metrics}
@endpoint(
"projects.get_hyper_parameters",
min_version="2.2",
request_data_model=GetHyperParamReq,
response_data_model=GetHyperParamResp,
)
def get_hyper_parameters(call: APICall, company_id: str, request: GetHyperParamReq):
total, remaining, parameters = TaskBLL.get_aggregated_project_execution_parameters(
company_id,
project_ids=[request.project] if request.project else None,
page=request.page,
page_size=request.page_size,
)
call.result.data = {
"total": total,
"remaining": remaining,
"parameters": parameters,
}

218
server/services/queues.py Normal file
View File

@@ -0,0 +1,218 @@
from apimodels.base import UpdateResponse
from apimodels.queues import (
GetDefaultResp,
CreateRequest,
DeleteRequest,
UpdateRequest,
MoveTaskRequest,
MoveTaskResponse,
TaskRequest,
QueueRequest,
GetMetricsRequest,
GetMetricsResponse,
QueueMetrics,
)
from bll.queue import QueueBLL
from bll.util import extract_properties_to_lists
from bll.workers import WorkerBLL
from service_repo import APICall, endpoint
from services.utils import conform_tag_fields, conform_output_tags, conform_tags
worker_bll = WorkerBLL()
queue_bll = QueueBLL(worker_bll)
@endpoint("queues.get_by_id", min_version="2.4", request_data_model=QueueRequest)
def get_by_id(call: APICall, company_id, req_model: QueueRequest):
queue = queue_bll.get_by_id(company_id, req_model.queue)
queue_dict = queue.to_proper_dict()
conform_output_tags(call, queue_dict)
call.result.data = {"queue": queue_dict}
@endpoint("queues.get_default", min_version="2.4", response_data_model=GetDefaultResp)
def get_by_id(call: APICall):
queue = queue_bll.get_default(call.identity.company)
call.result.data_model = GetDefaultResp(id=queue.id, name=queue.name)
@endpoint("queues.get_all_ex", min_version="2.4")
def get_all_ex(call: APICall):
conform_tag_fields(call, call.data)
queues = queue_bll.get_queue_infos(
company_id=call.identity.company, query_dict=call.data
)
conform_output_tags(call, queues)
call.result.data = {"queues": queues}
@endpoint("queues.get_all", min_version="2.4")
def get_all(call: APICall):
conform_tag_fields(call, call.data)
queues = queue_bll.get_all(company_id=call.identity.company, query_dict=call.data)
conform_output_tags(call, queues)
call.result.data = {"queues": queues}
@endpoint("queues.create", min_version="2.4", request_data_model=CreateRequest)
def create(call: APICall, company_id, request: CreateRequest):
tags, system_tags = conform_tags(call, request.tags, request.system_tags)
queue = queue_bll.create(
company_id=company_id, name=request.name, tags=tags, system_tags=system_tags
)
call.result.data = {"id": queue.id}
@endpoint(
"queues.update",
min_version="2.4",
request_data_model=UpdateRequest,
response_data_model=UpdateResponse,
)
def update(call: APICall, company_id, req_model: UpdateRequest):
data = call.data_model_for_partial_update
conform_tag_fields(call, data)
updated, fields = queue_bll.update(
company_id=company_id, queue_id=req_model.queue, **data
)
conform_output_tags(call, fields)
call.result.data_model = UpdateResponse(updated=updated, fields=fields)
@endpoint("queues.delete", min_version="2.4", request_data_model=DeleteRequest)
def delete(call: APICall, company_id, req_model: DeleteRequest):
queue_bll.delete(
company_id=company_id, queue_id=req_model.queue, force=req_model.force
)
call.result.data = {"deleted": 1}
@endpoint("queues.add_task", min_version="2.4", request_data_model=TaskRequest)
def add_task(call: APICall, company_id, req_model: TaskRequest):
call.result.data = {
"added": queue_bll.add_task(
company_id=company_id, queue_id=req_model.queue, task_id=req_model.task
)
}
@endpoint("queues.get_next_task", min_version="2.4", request_data_model=QueueRequest)
def get_next_task(call: APICall, company_id, req_model: QueueRequest):
task = queue_bll.get_next_task(company_id=company_id, queue_id=req_model.queue)
if task:
call.result.data = {"entry": task.to_proper_dict()}
@endpoint("queues.remove_task", min_version="2.4", request_data_model=TaskRequest)
def remove_task(call: APICall, company_id, req_model: TaskRequest):
call.result.data = {
"removed": queue_bll.remove_task(
company_id=company_id, queue_id=req_model.queue, task_id=req_model.task
)
}
@endpoint(
"queues.move_task_forward",
min_version="2.4",
request_data_model=MoveTaskRequest,
response_data_model=MoveTaskResponse,
)
def move_task_forward(call: APICall, company_id, req_model: MoveTaskRequest):
call.result.data_model = MoveTaskResponse(
position=queue_bll.reposition_task(
company_id=company_id,
queue_id=req_model.queue,
task_id=req_model.task,
pos_func=lambda p: max(0, p - req_model.count),
)
)
@endpoint(
"queues.move_task_backward",
min_version="2.4",
request_data_model=MoveTaskRequest,
response_data_model=MoveTaskResponse,
)
def move_task_backward(call: APICall, company_id, req_model: MoveTaskRequest):
call.result.data_model = MoveTaskResponse(
position=queue_bll.reposition_task(
company_id=company_id,
queue_id=req_model.queue,
task_id=req_model.task,
pos_func=lambda p: max(0, p + req_model.count),
)
)
@endpoint(
"queues.move_task_to_front",
min_version="2.4",
request_data_model=TaskRequest,
response_data_model=MoveTaskResponse,
)
def move_task_to_front(call: APICall, company_id, req_model: TaskRequest):
call.result.data_model = MoveTaskResponse(
position=queue_bll.reposition_task(
company_id=company_id,
queue_id=req_model.queue,
task_id=req_model.task,
pos_func=lambda p: 0,
)
)
@endpoint(
"queues.move_task_to_back",
min_version="2.4",
request_data_model=TaskRequest,
response_data_model=MoveTaskResponse,
)
def move_task_to_back(call: APICall, company_id, req_model: TaskRequest):
call.result.data_model = MoveTaskResponse(
position=queue_bll.reposition_task(
company_id=company_id,
queue_id=req_model.queue,
task_id=req_model.task,
pos_func=lambda p: -1,
)
)
@endpoint(
"queues.get_queue_metrics",
min_version="2.4",
request_data_model=GetMetricsRequest,
response_data_model=GetMetricsResponse,
)
def get_queue_metrics(
call: APICall, company_id, req_model: GetMetricsRequest
) -> GetMetricsResponse:
ret = queue_bll.metrics.get_queue_metrics(
company_id=company_id,
from_date=req_model.from_date,
to_date=req_model.to_date,
interval=req_model.interval,
queue_ids=req_model.queue_ids,
)
queue_dicts = {
queue: extract_properties_to_lists(
["date", "avg_waiting_time", "queue_length"], data
)
for queue, data in ret.items()
}
return GetMetricsResponse(
queues=[
QueueMetrics(
queue=queue,
dates=data["date"],
avg_waiting_times=data["avg_waiting_time"],
queue_lengths=data["queue_length"],
) if data else QueueMetrics(queue=queue)
for queue, data in queue_dicts.items()
]
)

View File

@@ -0,0 +1,93 @@
from datetime import datetime
from pyhocon.config_tree import NoneValue
from apierrors import errors
from apimodels.server import ReportStatsOptionRequest, ReportStatsOptionResponse
from bll.statistics.stats_reporter import StatisticsReporter
from config import config
from config.info import get_version, get_build_number, get_commit_number
from database.errors import translate_errors_context
from database.model import Company
from database.model.company import ReportStatsOption
from service_repo import ServiceRepo, APICall, endpoint
from version import __version__ as current_version
@endpoint("server.get_stats")
def get_stats(call: APICall):
call.result.data = StatisticsReporter.get_statistics(
company_id=call.identity.company
)
@endpoint("server.config")
def get_config(call: APICall):
path = call.data.get("path")
if path:
c = dict(config.get(path))
else:
c = config.to_dict()
def remove_none_value(x):
"""
Pyhocon bug in Python 3: leaves dummy "NoneValue"s in tree,
see: https://github.com/chimpler/pyhocon/issues/111
"""
if isinstance(x, dict):
return {key: remove_none_value(value) for key, value in x.items()}
if isinstance(x, list):
return list(map(remove_none_value, x))
if isinstance(x, NoneValue):
return None
return x
c.pop("secure", None)
call.result.data = remove_none_value(c)
@endpoint("server.endpoints")
def get_endpoints(call: APICall):
call.result.data = ServiceRepo.endpoints_summary()
@endpoint("server.info")
def info(call: APICall):
call.result.data = {
"version": get_version(),
"build": get_build_number(),
"commit": get_commit_number(),
}
@endpoint(
"server.report_stats_option",
request_data_model=ReportStatsOptionRequest,
response_data_model=ReportStatsOptionResponse,
)
def report_stats(call: APICall, company: str, request: ReportStatsOptionRequest):
if not StatisticsReporter.supported:
result = ReportStatsOptionResponse(supported=False)
else:
enabled = request.enabled
with translate_errors_context():
query = Company.objects(id=company)
if enabled is None:
stats_option = query.first().defaults.stats_option
else:
stats_option = ReportStatsOption(
enabled=enabled,
enabled_time=datetime.utcnow(),
enabled_version=current_version,
enabled_user=call.identity.user,
)
updated = query.update(defaults__stats_option=stats_option)
if not updated:
raise errors.server_error.InternalError(
f"Failed setting report_stats to {enabled}"
)
result = ReportStatsOptionResponse(**stats_option.to_mongo())
call.result.data_model = result

View File

@@ -11,7 +11,7 @@ from mongoengine import EmbeddedDocument, Q
from mongoengine.queryset.transform import COMPARISON_OPERATORS
from pymongo import UpdateOne
from apierrors import errors
from apierrors import errors, APIError
from apimodels.base import UpdateResponse
from apimodels.tasks import (
StartedResponse,
@@ -24,22 +24,34 @@ from apimodels.tasks import (
TaskRequest,
DeleteRequest,
PingRequest,
EnqueueRequest,
EnqueueResponse,
DequeueResponse,
)
from bll.event import EventBLL
from bll.queue import QueueBLL
from bll.task import TaskBLL, ChangeStatusRequest, update_project_time, split_by
from bll.util import SetFieldsResolver
from database.errors import translate_errors_context
from database.model.model import Model
from database.model.task.output import Output
from database.model.task.task import Task, TaskStatus, Script, DEFAULT_LAST_ITERATION
from database.model.task.task import (
Task,
TaskStatus,
Script,
DEFAULT_LAST_ITERATION,
Execution,
)
from database.utils import get_fields, parse_from_call
from service_repo import APICall, endpoint
from services.utils import conform_tag_fields, conform_output_tags
from timing_context import TimingContext
from utilities import safe_get
task_fields = set(Task.get_fields())
task_script_fields = set(get_fields(Script))
get_all_query_options = Task.QueryParameterOptions(
list_fields=("id", "user", "tags", "type", "status", "project"),
list_fields=("id", "user", "tags", "system_tags", "type", "status", "project"),
datetime_fields=("status_changed",),
pattern_fields=("name", "comment"),
fields=("parent",),
@@ -47,20 +59,23 @@ get_all_query_options = Task.QueryParameterOptions(
task_bll = TaskBLL()
event_bll = EventBLL()
queue_bll = QueueBLL()
TaskBLL.start_non_responsive_tasks_watchdog()
def set_task_status_from_call(
request: UpdateRequest, company_id, new_status=None, **kwargs
request: UpdateRequest, company_id, new_status=None, **set_fields
) -> dict:
fields_resolver = SetFieldsResolver(set_fields)
task = TaskBLL.get_task_with_access(
request.task,
company_id=company_id,
only=("status", "project"),
only=tuple({"status", "project"} | fields_resolver.get_names()),
requires_write_access=True,
)
status_reason = request.status_reason
status_message = request.status_message
force = request.force
@@ -70,7 +85,7 @@ def set_task_status_from_call(
status_reason=status_reason,
status_message=status_message,
force=force,
).execute(**kwargs)
).execute(**fields_resolver.get_fields(task))
@endpoint("tasks.get_by_id", request_data_model=TaskRequest)
@@ -79,11 +94,13 @@ def get_by_id(call: APICall, company_id, req_model: TaskRequest):
req_model.task, company_id=company_id, allow_public=True
)
task_dict = task.to_proper_dict()
conform_output_tags(call, task_dict)
call.result.data = {"task": task_dict}
@endpoint("tasks.get_all_ex", required_fields=[])
def get_all_ex(call: APICall):
conform_tag_fields(call, call.data)
with translate_errors_context():
with TimingContext("mongo", "task_get_all_ex"):
tasks = Task.get_many_with_join(
@@ -92,12 +109,13 @@ def get_all_ex(call: APICall):
query_options=get_all_query_options,
allow_public=True, # required in case projection is requested for public dataset/versions
)
conform_output_tags(call, tasks)
call.result.data = {"tasks": tasks}
@endpoint("tasks.get_all", required_fields=[])
def get_all(call: APICall):
conform_tag_fields(call, call.data)
with translate_errors_context():
with TimingContext("mongo", "task_get_all"):
tasks = Task.get_many(
@@ -107,6 +125,7 @@ def get_all(call: APICall):
query_options=get_all_query_options,
allow_public=True, # required in case projection is requested for public dataset/versions
)
conform_output_tags(call, tasks)
call.result.data = {"tasks": tasks}
@@ -160,7 +179,7 @@ def started(call: APICall, company_id, req_model: UpdateRequest):
req_model,
company_id,
new_status=TaskStatus.in_progress,
started=datetime.utcnow(),
min__started=datetime.utcnow(), # don't override a previous, smaller "started" field value
)
)
res.started = res.updated
@@ -188,6 +207,7 @@ def close(call: APICall, company_id, req_model: UpdateRequest):
create_fields = {
"name": None,
"tags": list,
"system_tags": list,
"type": None,
"error": None,
"comment": None,
@@ -219,10 +239,7 @@ def prepare_create_fields(
output = Output(destination=output_dest)
fields["output"] = output
# Make sure there are no duplicate tags
tags = fields.get("tags")
if tags:
fields["tags"] = list(set(tags))
conform_tag_fields(call, fields)
# Strip all script fields (remove leading and trailing whitespace chars) to avoid unusable names and paths
for field in task_script_fields:
@@ -251,7 +268,7 @@ def _validate_and_get_task_from_call(call: APICall, **kwargs):
task = task_bll.create(call, fields)
with TimingContext("code", "validate"):
task_bll.validate(task, force=call.data.get("force", False))
task_bll.validate(task)
return task
@@ -272,16 +289,14 @@ def create(call: APICall, company_id, req_model: CreateRequest):
call.result.data = {"id": task.id}
def prepare_update_fields(task, call_data):
def prepare_update_fields(call: APICall, task, call_data):
valid_fields = deepcopy(task.__class__.user_set_allowed())
update_fields = {k: v for k, v in create_fields.items() if k in valid_fields}
update_fields["output__error"] = None
t_fields = task_fields
t_fields.add("output__error")
fields = parse_from_call(call_data, update_fields, t_fields)
tags = fields.get("tags")
if tags:
fields["tags"] = list(set(tags))
conform_tag_fields(call, fields)
return fields, valid_fields
@@ -296,7 +311,7 @@ def update(call: APICall, company_id, req_model: UpdateRequest):
if not task:
raise errors.bad_request.InvalidTaskId(id=task_id)
partial_update_dict, valid_fields = prepare_update_fields(task, call.data)
partial_update_dict, valid_fields = prepare_update_fields(call, task, call.data)
if not partial_update_dict:
return UpdateResponse(updated=0)
@@ -309,7 +324,7 @@ def update(call: APICall, company_id, req_model: UpdateRequest):
)
update_project_time(updated_fields.get("project"))
conform_output_tags(call, updated_fields)
return UpdateResponse(updated=updated_count, fields=updated_fields)
@@ -364,7 +379,7 @@ def update_batch(call: APICall):
bulk_ops = []
for id, data in items.items():
fields, valid_fields = prepare_update_fields(tasks[id], data)
fields, valid_fields = prepare_update_fields(call, tasks[id], data)
partial_update_dict = Task.get_safe_update_dict(fields)
if not partial_update_dict:
continue
@@ -421,7 +436,7 @@ def edit(call: APICall, company_id, req_model: UpdateRequest):
d.update(value)
fields[key] = d
task_bll.validate(task_bll.create(call, fields), force=force)
task_bll.validate(task_bll.create(call, fields))
# make sure field names do not end in mongoengine comparison operators
fixed_fields = {
@@ -434,11 +449,131 @@ def edit(call: APICall, company_id, req_model: UpdateRequest):
fixed_fields.update(last_update=now)
updated = task.update(upsert=False, **fixed_fields)
update_project_time(fields.get("project"))
conform_output_tags(call, fields)
call.result.data_model = UpdateResponse(updated=updated, fields=fields)
else:
call.result.data_model = UpdateResponse(updated=0)
@endpoint(
"tasks.enqueue",
request_data_model=EnqueueRequest,
response_data_model=EnqueueResponse,
)
def enqueue(call: APICall, company_id, req_model: EnqueueRequest):
task_id = req_model.task
queue_id = req_model.queue
status_message = req_model.status_message
status_reason = req_model.status_reason
if not queue_id:
# try to get default queue
queue_id = queue_bll.get_default(company_id).id
with translate_errors_context():
query = dict(id=task_id, company=company_id)
task = Task.get_for_writing(
_only=("type", "script", "execution", "status", "project", "id"), **query
)
if not task:
raise errors.bad_request.InvalidTaskId(**query)
res = EnqueueResponse(
**ChangeStatusRequest(
task=task,
new_status=TaskStatus.queued,
status_reason=status_reason,
status_message=status_message,
allow_same_state_transition=False,
).execute()
)
try:
queue_bll.add_task(
company_id=company_id, queue_id=queue_id, task_id=task.id
)
except Exception:
# failed enqueueing, revert to previous state
ChangeStatusRequest(
task=task,
current_status_override=TaskStatus.queued,
new_status=task.status,
force=True,
status_reason="failed enqueueing",
).execute()
raise
# set the current queue ID in the task
if task.execution:
Task.objects(**query).update(execution__queue=queue_id, multi=False)
else:
Task.objects(**query).update(
execution=Execution(queue=queue_id), multi=False
)
res.queued = 1
res.fields.update(**{"execution.queue": queue_id})
call.result.data_model = res
@endpoint(
"tasks.dequeue",
request_data_model=UpdateRequest,
response_data_model=DequeueResponse,
)
def dequeue(call: APICall, company_id, req_model: UpdateRequest):
task = TaskBLL.get_task_with_access(
req_model.task,
company_id=company_id,
only=("id", "execution", "status", "project"),
requires_write_access=True,
)
if task.status not in (TaskStatus.queued,):
raise errors.bad_request.InvalidTaskId(
status=task.status, expected=TaskStatus.queued
)
_dequeue(task, company_id)
status_message = req_model.status_message
status_reason = req_model.status_reason
res = DequeueResponse(
**ChangeStatusRequest(
task=task,
new_status=TaskStatus.created,
status_reason=status_reason,
status_message=status_message,
).execute(unset__execution__queue=1)
)
res.dequeued = 1
call.result.data_model = res
def _dequeue(task: Task, company_id: str, silent_fail=False):
"""
Dequeue the task from the queue
:param task: task to dequeue
:param silent_fail: do not throw exceptions. APIError is still thrown
:raise errors.bad_request.MissingRequiredFields: if the task is not queued
:raise APIError or errors.server_error.TransactionError: if internal call to queues.remove_task fails
:return: the result of queues.remove_task call. None in case of silent failure
"""
if not task.execution or not task.execution.queue:
if silent_fail:
return
raise errors.bad_request.MissingRequiredFields(
"task has no queue value", field="execution.queue"
)
return {
"removed": queue_bll.remove_task(
company_id=company_id, queue_id=task.execution.queue, task_id=task.id
)
}
@endpoint(
"tasks.reset", request_data_model=UpdateRequest, response_data_model=ResetResponse
)
@@ -455,6 +590,16 @@ def reset(call: APICall, company_id, req_model: UpdateRequest):
api_results = {}
updates = {}
try:
dequeued = _dequeue(task, company_id, silent_fail=True)
except APIError:
# dequeue may fail if the task was not enqueued
pass
else:
if dequeued:
api_results.update(dequeued=dequeued)
updates.update(unset__execution__queue=1)
cleaned_up = cleanup_task(task, force)
api_results.update(attr.asdict(cleaned_up))
@@ -463,6 +608,7 @@ def reset(call: APICall, company_id, req_model: UpdateRequest):
set__last_metrics={},
unset__output__result=1,
unset__output__model=1,
__raw__={"$pull": {"execution.artifacts": {"mode": {"$ne": "input"}}}},
)
res = ResetResponse(
@@ -670,7 +816,10 @@ def publish(call: APICall, company_id, req_model: PublishRequest):
@endpoint(
"tasks.completed", min_version="2.2", request_data_model=UpdateRequest, response_data_model=UpdateResponse
"tasks.completed",
min_version="2.2",
request_data_model=UpdateRequest,
response_data_model=UpdateResponse,
)
def completed(call: APICall, company_id, request: PublishRequest):
call.result.data_model = UpdateResponse(
@@ -688,4 +837,3 @@ def ping(_, company_id, request: PingRequest):
TaskBLL.set_last_update(
task_ids=[request.task], company_id=company_id, last_update=datetime.utcnow()
)

63
server/services/utils.py Normal file
View File

@@ -0,0 +1,63 @@
from typing import Union, Sequence, Tuple
from database.utils import partition_tags
from service_repo import APICall
from service_repo.base import PartialVersion
def conform_output_tags(call: APICall, documents: Union[dict, Sequence[dict]]):
"""
For old clients both tags and system tags are returned in 'tags' field
"""
if call.requested_endpoint_version >= PartialVersion("2.3"):
return
if isinstance(documents, dict):
documents = [documents]
for doc in documents:
system_tags = doc.get("system_tags")
if system_tags:
doc["tags"] = list(set(doc.get("tags", [])) | set(system_tags))
def conform_tag_fields(call: APICall, document: dict):
"""
Upgrade old client tags in place
"""
if "tags" in document:
tags, system_tags = conform_tags(
call, document["tags"], document.get("system_tags")
)
if tags != document.get("tags"):
document["tags"] = tags
if system_tags != document.get("system_tags"):
document["system_tags"] = system_tags
def conform_tags(
call: APICall, tags: Sequence, system_tags: Sequence
) -> Tuple[Sequence, Sequence]:
"""
Make sure that 'tags' from the old SDK clients
are correctly split into 'tags' and 'system_tags'
Make sure that there are no duplicate tags
"""
if call.requested_endpoint_version < PartialVersion("2.3"):
tags, system_tags = _upgrade_tags(call, tags, system_tags)
return _get_unique_values(tags), _get_unique_values(system_tags)
def _upgrade_tags(call: APICall, tags: Sequence, system_tags: Sequence):
if tags is not None and not system_tags:
service_name = call.endpoint_name.partition(".")[0]
entity = service_name[:-1] if service_name.endswith("s") else service_name
return partition_tags(entity, tags)
return tags, system_tags
def _get_unique_values(values: Sequence) -> Sequence:
"""Get unique values from the given sequence"""
if not values:
return values
return list(set(values))

202
server/services/workers.py Normal file
View File

@@ -0,0 +1,202 @@
import itertools
from operator import attrgetter
from typing import Optional, Sequence, Union
from boltons.iterutils import bucketize
from apierrors.errors import bad_request
from apimodels.workers import (
WorkerRequest,
StatusReportRequest,
GetAllRequest,
GetAllResponse,
RegisterRequest,
GetStatsRequest,
MetricCategory,
GetMetricKeysRequest,
GetMetricKeysResponse,
GetStatsResponse,
WorkerStatistics,
MetricStats,
AggregationStats,
GetActivityReportRequest,
GetActivityReportResponse,
ActivityReportSeries,
)
from bll.util import extract_properties_to_lists
from bll.workers import WorkerBLL
from config import config
from service_repo import APICall, endpoint
log = config.logger(__file__)
worker_bll = WorkerBLL()
@endpoint(
"workers.get_all",
min_version="2.4",
request_data_model=GetAllRequest,
response_data_model=GetAllResponse,
)
def get_all(call: APICall, company_id: str, request: GetAllRequest):
call.result.data_model = GetAllResponse(
workers=worker_bll.get_all_with_projection(company_id, request.last_seen)
)
@endpoint("workers.register", min_version="2.4", request_data_model=RegisterRequest)
def register(call: APICall, company_id, req_model: RegisterRequest):
worker = req_model.worker
timeout = req_model.timeout
queues = req_model.queues
if not timeout or timeout <= 0:
raise bad_request.WorkerRegistrationFailed(
"invalid timeout", timeout=timeout, worker=worker
)
worker_bll.register_worker(
company_id=company_id,
user_id=call.identity.user,
worker=worker,
ip=call.real_ip,
queues=queues,
timeout=timeout,
)
@endpoint("workers.unregister", min_version="2.4", request_data_model=WorkerRequest)
def unregister(call: APICall, company_id, req_model: WorkerRequest):
worker_bll.unregister_worker(company_id, call.identity.user, req_model.worker)
@endpoint("workers.status_report", min_version="2.4", request_data_model=StatusReportRequest)
def status_report(call: APICall, company_id, request: StatusReportRequest):
worker_bll.status_report(
company_id=company_id,
user_id=call.identity.user,
ip=call.real_ip,
report=request,
)
@endpoint(
"workers.get_metric_keys",
min_version="2.4",
request_data_model=GetMetricKeysRequest,
response_data_model=GetMetricKeysResponse,
validate_schema=True,
)
def get_metric_keys(
call: APICall, company_id, req_model: GetMetricKeysRequest
) -> GetMetricKeysResponse:
ret = worker_bll.stats.get_worker_stats_keys(
company_id, worker_ids=req_model.worker_ids
)
return GetMetricKeysResponse(
categories=[MetricCategory(name=k, metric_keys=v) for k, v in ret.items()]
)
@endpoint(
"workers.get_activity_report",
min_version="2.4",
request_data_model=GetActivityReportRequest,
response_data_model=GetActivityReportResponse,
validate_schema=True,
)
def get_activity_report(
call: APICall, company_id, req_model: GetActivityReportRequest
) -> GetActivityReportResponse:
def get_activity_series(active_only: bool = False) -> ActivityReportSeries:
ret = worker_bll.stats.get_activity_report(
company_id=company_id,
from_date=req_model.from_date,
to_date=req_model.to_date,
interval=req_model.interval,
active_only=active_only,
)
if not ret:
return ActivityReportSeries(dates=[], counts=[])
count_by_date = extract_properties_to_lists(["date", "count"], ret)
return ActivityReportSeries(
dates=count_by_date["date"], counts=count_by_date["count"]
)
return GetActivityReportResponse(
total=get_activity_series(), active=get_activity_series(active_only=True)
)
@endpoint(
"workers.get_stats",
min_version="2.4",
request_data_model=GetStatsRequest,
response_data_model=GetStatsResponse,
validate_schema=True,
)
def get_stats(call: APICall, company_id, request: GetStatsRequest):
ret = worker_bll.stats.get_worker_stats(company_id, request)
def _get_variant_metric_stats(
metric: str,
agg_names: Sequence[str],
stats: Sequence[dict],
variant: Optional[str] = None,
) -> MetricStats:
stat_by_name = extract_properties_to_lists(agg_names, stats)
return MetricStats(
metric=metric,
variant=variant,
dates=stat_by_name["date"],
stats=[
AggregationStats(aggregation=name, values=aggs)
for name, aggs in stat_by_name.items()
if name != "date"
],
)
def _get_metric_stats(
metric: str, stats: Union[dict, Sequence[dict]], agg_types: Sequence[str]
) -> Sequence[MetricStats]:
"""
Return statistics for a certain metric or a list of statistic for
metric variants if break_by_variant was requested
"""
agg_names = ["date"] + list(set(agg_types))
if not isinstance(stats, dict):
# no variants were requested
return [_get_variant_metric_stats(metric, agg_names, stats)]
return [
_get_variant_metric_stats(metric, agg_names, variant_stats, variant)
for variant, variant_stats in stats.items()
]
def _get_worker_metrics(stats: dict) -> Sequence[MetricStats]:
"""
Convert the worker statistics data from the internal format of lists of structs
to a more "compact" format for json transfer (arrays of dates and arrays of values)
"""
# removed metrics that were requested but for some reason
# do not exist in stats data
metrics = [metric for metric in request.items if metric.key in stats]
aggs_by_metric = bucketize(
metrics, key=attrgetter("key"), value_transform=attrgetter("aggregation")
)
return list(
itertools.chain.from_iterable(
_get_metric_stats(metric, metric_stats, aggs_by_metric[metric])
for metric, metric_stats in stats.items()
)
)
return GetStatsResponse(
workers=[
WorkerStatistics(worker=worker, metrics=_get_worker_metrics(stats))
for worker, stats in ret.items()
]
)

View File

@@ -1,6 +1,8 @@
import abc
import sys
from datetime import datetime, timezone
from functools import partial
from typing import Iterable
from unittest import TestCase
from tests.api_client import APIClient
@@ -88,6 +90,19 @@ class TestService(TestCase, TestServiceInterface):
log.exception(ex)
self._deferred = []
def assertEqualNoOrder(self, first: Iterable, second: Iterable):
"""Compares 2 sequences regardless of their items order"""
self.assertEqual(set(first), set(second))
def header(info, title="=" * 20):
print(title, info, title, file=sys.stderr)
def utc_now_tz_aware() -> datetime:
"""
Returns utc now with the utc time zone.
Suitable for subsequent usage with functions that
make use of tz info like 'timestamp'
"""
return datetime.now(timezone.utc)

View File

@@ -1,159 +0,0 @@
"""
Comprehensive test of all(?) use cases of datasets and frames
"""
import json
import unittest
import es_factory
from tests.api_client import APIClient
from config import config
log = config.logger(__file__)
class TestDatasetsService(unittest.TestCase):
def setUp(self):
self.api = APIClient(base_url="http://localhost:5100/v1.0")
self.created_tasks = []
self.task = dict(
name="test task events",
type="training",
)
res, self.task_id = self.api.send('tasks.create', self.task, extract="id")
assert (res.meta.result_code == 200)
self.created_tasks.append(self.task_id)
def tearDown(self):
log.info("Cleanup...")
for task_id in self.created_tasks:
try:
self.api.send('tasks.delete', dict(task=task_id, force=True))
except Exception as ex:
log.exception(ex)
def create_task_event(self, type, iteration):
return {
"worker": "test",
"type": type,
"task": self.task_id,
"iter": iteration,
"timestamp": es_factory.get_timestamp_millis()
}
def copy_and_update(self, src_obj, new_data):
obj = src_obj.copy()
obj.update(new_data)
return obj
def test_task_logs(self):
events = []
for iter in range(10):
log_event = self.create_task_event("log", iteration=iter)
events.append(self.copy_and_update(log_event, {
"msg": "This is a log message from test task iter " + str(iter)
}))
# sleep so timestamp is not the same
import time
time.sleep(0.01)
self.send_batch(events)
data = self.api.events.get_task_log(task=self.task_id)
assert len(data["events"]) == 10
self.api.tasks.reset(task=self.task_id)
data = self.api.events.get_task_log(task=self.task_id)
assert len(data["events"]) == 0
def test_task_plots(self):
event = self.create_task_event("plot", 0)
event["metric"] = "roc"
event.update({
"plot_str": json.dumps({
"data": [
{
"x": [0, 1, 2, 3, 4, 5, 6, 7, 8],
"y": [0, 1, 2, 3, 4, 5, 6, 7, 8],
"text": ["Th=0.1", "Th=0.2", "Th=0.3", "Th=0.4", "Th=0.5", "Th=0.6", "Th=0.7", "Th=0.8"],
"name": 'class1'
},
{
"x": [0, 1, 2, 3, 4, 5, 6, 7, 8],
"y": [2.0, 3.0, 5.0, 8.2, 6.4, 7.5, 9.2, 8.1, 10.0],
"text": ["Th=0.1", "Th=0.2", "Th=0.3", "Th=0.4", "Th=0.5", "Th=0.6", "Th=0.7", "Th=0.8"],
"name": 'class2',
}
],
"layout": {
"title": "ROC for iter 0",
"xaxis": {
"title": 'my x axis'
},
"yaxis": {
"title": 'my y axis'
}
}
})
})
self.send(event)
event = self.create_task_event("plot", 100)
event["metric"] = "confusion"
event.update({
"plot_str": json.dumps({
"data": [
{
"y": [
"lying",
"sitting",
"standing",
"people",
"backgroun"
],
"x": [
"lying",
"sitting",
"standing",
"people",
"backgroun"
],
"z": [
[758, 163, 0, 0, 23],
[63, 858, 3, 0, 0],
[0, 50, 188, 21, 35],
[0, 22, 8, 40, 4, ],
[12, 91, 26, 29, 368]
],
"type": "heatmap"
}
],
"layout": {
"title": "Confusion Matrix for iter 100",
"xaxis": {
"title": "Predicted value"
},
"yaxis": {
"title": "Real value"
}
}
})
})
self.send(event)
data = self.api.events.get_task_plots(task=self.task_id)
assert len(data["plots"]) == 2
self.api.tasks.reset(task=self.task_id)
data = self.api.events.get_task_plots(task=self.task_id)
assert len(data["plots"]) == 0
def send_batch(self, events):
self.api.send_batch('events.add_batch', events)
def send(self, event):
self.api.send('events.add', event)
if __name__ == '__main__':
unittest.main()

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