# TRAINS Server

##  Auto-Magical Experiment Manager & Version Control for AI

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## Introduction

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. 
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:

* The TRAINS Web-App, a single-page UI for experiment management and browsing
* RESTful API for:
    * Documenting and logging experiment information, statistics and results
    * 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)).

When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)).

## System diagram


![Alt Text](https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true)


## Install / Upgrade - AWS

Use our pre-installed Amazon Machine Image for easy deployment in AWS. 

Details and instructions can be found  [here](docs/install_aws.md).

## Installation - Docker

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.

**Please Note**:
* This Docker image was tested with Linux, only. For Windows users, we recommend running the server
on a Linux virtual machine.

* All command-line instructions below assume you're using `bash`.

### 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):

    ```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: Setup the Docker daemon

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:

    ```bash
    OPTIONS="--default-ulimit nofile=1024:65536 --default-ulimit memlock=-1:-1"
    ```

* Otherwise, edit `/etc/docker/daemon.json` (if it exists) or create it (if it does not exist).

    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`:

    ```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
    ```

* 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 5: 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 will include 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 chown -R 1000:1000 /opt/trains
```

### 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.

<!---
#### Fixed users mode (basic users management)

In this mode, the server authenticates users based on a pre-configured users list.

Enable this feature by placing an `apiserver.conf` file under `/opt/trains/config`, containing for example:

    fixed_users {
        enabled: true
        users: [
            {
                username: "jane"
                password: "123456"
                name: "Jane Doe"
            },
            {
                username: "john"
                password: "abcdef"
                name: "John Doe"
            }
        ]
    }
-->
#### Non-responsive experiments watchdog

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 change the watchdog's timeouts, place a `services.conf` file under `/opt/trains/config`, containing for example:

    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
        }
    }

### Launching Docker Containers

**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.

## 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, 
for example http://localhost:8080

## Upgrade

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:

        sudo docker stop <docker-name>
        sudo docker rm -v <docker-name>

    The Docker names are (see [Launching Docker Containers](#launching-docker-containers)):

    * `trains-elastic`
    * `trains-mongo`
    * `trains-fileserver`
    * `trains-apiserver`
    * `trains-webserver`

2. Pull the new **trains-server** docker image using the following command:

        sudo docker pull allegroai/trains:latest
    
    If you wish to pull a different version, replace `latest` with the required version number, for example:

        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`:

    For example, if your data directory is `/opt/trains`, use the following command:

        sudo tar czvf ~/trains_backup.tgz /opt/trains/data

    This back ups all data to an archive in your home directory.

    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

4. Launch the newly released Docker image (see [Launching Docker Containers](#launching-docker-containers)).

## 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 
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, 
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!