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## **NOTE**: This page's information is deprecated. See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for up-to-date deployment instructions
Launching **trains-server**
* How do I launch **trains-server** on:
* [Stand alone Linux Ubuntu systems?](#ubuntu)
* [macOS?](#mac-osx)
* [Windows 10?](#docker_compose_win10)
* [How do I restart trains-server?](#restart)
Kubernetes
* [Can I deploy trains-server on Kubernetes clusters?](#kubernetes)
* [Can I create a Helm Chart for trains-server Kubernetes deployment?](#helm)
Configuration
* [How do I configure trains-server for sub-domains and load balancers?](#sub-domains)
* [Can I add web login authentication to trains-server?](#web-auth)
* [Can I modify the non-responsive experiment watchdog settings?](#watchdog)
Troubleshooting
* [How do I fix Docker upgrade errors?](#common-docker-upgrade-errors)
* [Why is web login authentication not working?](#port-conflict)
## Launching **trains-server**
### How do I launch trains-server on stand alone Linux Ubuntu systems? <a name="ubuntu"></a>
To launch **trains-server** on a stand alone Linux Ubuntu:
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)
### How do I launch trains-server on macOS? <a name="mac-osx"></a>
To launch **trains-server** on macOS:
1. Install [docker for macOS](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 docker compose file.
docker-compose -f docker-compose.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### How do I launch 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 launch **trains-server** on Windows 10:
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:
cd c:
mkdir c:\opt\trains\data
mkdir c:\opt\trains\logs
1. Download 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:
docker-compose -f up docker-compose-win10.yml
Your server is now running on [http://localhost:8080](http://localhost:8080)
### How do I restart trains-server? <a name="restart"></a>
Restart *trains-server* by first stopping the Docker containers and then restarting them.
```bash
docker-compose down
docker-compose up -f docker-compose.yml
```
**Note**: If you are using a different docker-compose YAML file, specify that file.
## Kubernetes
### Can I deploy 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.
### Can I create 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.
## Configuration
### How do I configure 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)).
### Can I add web login authentication to trains-server? <a name="web-auth"></a>
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.
To add web login authentication to **trains-server**:
1. If you are not using the current **trains-server** version, then [upgrade](https://github.com/allegroai/trains-server#upgrade).
1. In `/opt/trains/config/apiserver.conf`, add the `auth` section and in it specify the users, for example:
**Note**: A sample `apiserver.conf` configuration file is also available [here](https://github.com/allegroai/trains-server/blob/master/docs/apiserver.conf).
auth {
# Fixed users login credentials
# 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"
},
]
}
}
1. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).
### Can I modify the experiment watchdog settings? <a name="watchdog"></a>
The non-responsive experiment watchdog monitors experiments that were not updated for a specified period of time
and marks them as `aborted`. The watchdog is always active.
You can modify the following settings for the watchdog:
* the time threshold (in seconds) of experiment inactivity (default value is 7200 seconds (2 hours))
* the time interval (in seconds) between watchdog cycles
To change the watchdog's settings:
1. In `/opt/trains/config`, add the `services.conf` file and in it specify the watchdog settings, for example:
**Note**: A sample watchdog `services.conf` configuration file is also available [here](https://github.com/allegroai/trains-server/blob/master/docs/services.conf).
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
}
}
1. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).
## Troubleshooting
### How do I fix Docker upgrade errors? <a name="common-docker-upgrade-errors"></a>
To resolve the Docker error "... The container name "/trains-???" is already in use by ...", try removing deprecated images:
docker rm -f $(docker ps -a -q)
### Why is web login authentication not working?
A port conflict between the **trains-server** MongoDB and / or Elastic instances, and other
instances running on your system may prevent web login authentication
from working correctly.
**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, change the MongoDB and / or Elastic ports in the `docker-compose.yml`,
and then run the Docker compose commands to restart the **trains-server** instance.
To change the MongoDB and / or Elastic ports for **trains-server**:
1. Edit the `docker-compose.yml` file.
1. In the `services/trainsserver/environment` section, add the following environment variable(s):
* For MongoDB:
MONGODB_SERVICE_PORT: <new-mongodb-port>
* For Elastic:
ELASTIC_SERVICE_PORT: <new-elasticsearch-port>
For example:
MONGODB_SERVICE_PORT: 27018
ELASTIC_SERVICE_PORT: 9201
1. For MongoDB, in the `services/mongo/ports` section, expose the new MongoDB port:
<new-mongodb-port>:27017
For example:
20718:27017
1. For Elastic, in the `services/elasticsearch/ports` section, expose the new Elastic port:
<new-elsticsearch-port>:9200
For example:
9201:9200
2. Restart **trains-server** (see the [Restarting trains-server](#restart) FAQ).

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# Deploying **trains-server** on AWS
# Deploying ClearML Server on AWS
## **NOTE**: These instructions are deprecated. See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for up-to-date deployment instructions
To easily deploy **trains-server** on AWS, use one of our pre-built Amazon Machine Images (AMIs).
We provide AMIs per region for each released version of **trains-server**, see [Released versions](#released-versions) below.
Once the AMI is up and running, [configure the Trains client](https://github.com/allegroai/trains/blob/master/README.md#configuration) to use your **trains-server**.
The service port numbers on our **trains-server** AMIs:
- Web application: `8080`
- API Server: `8008`
- File Server: `8081`
The persistent storage configuration:
- MongoDB: `/opt/trains/data/mongo/`
- ElasticSearch: `/opt/trains/data/elastic/`
- File Server: `/mnt/fileserver/`
For examples and use cases, check the [Trains usage examples](https://github.com/allegroai/trains/blob/master/docs/trains_examples.md).
For instructions on launching a custom AMI from the EC2 console, see the [AWS Knowledge Center](https://aws.amazon.com/premiumsupport/knowledge-center/launch-instance-custom-ami/) or detailed instructions in the [AWS Documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/launching-instance.html).
The minimum recommended amount of RAM is 8GB. For example, **t3.large** or **t3a.large** would have the minimum recommended amount of resources.
## Upgrading
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**.
### Note on upgrading AMIs to v0.12
This upgrade includes the automatically updated AMI in Version 0.12. It also includes an additional REDIS docker to the **trains-server** setup.
To upgrade the AMI:
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
## See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_aws_ec2_ami/) for up-to-date deployment instructions
## Released versions
The following sections contain lists of AMI Image IDs, per region, for each released **trains-server** version.
### Latest version AMI - v0.15.1 (auto update)<a name="autoupdate"></a>
For easier upgrades, the following AMIs automatically update to the latest release every reboot:
* **eu-north-1** : ami-0f30c84b905d354b9
* **ap-south-1** : ami-050e7acec52c8c74e
* **eu-west-3** : ami-03911c5b5bc77ef75
* **eu-west-2** : ami-0a5ed8aa2573ccc70
* **eu-west-1** : ami-0a53c65e922ec0611
* **ap-northeast-2** : ami-08cd017a37b8e8aab
* **ap-northeast-1** : ami-056b3ca1ad5af9322
* **sa-east-1** : ami-01ddc9325bafb400c
* **ca-central-1** : ami-0fc3cbbd982b18b45
* **ap-southeast-1** : ami-04c7a358df7002ef5
* **ap-southeast-2** : ami-0eeaf54231b4ae22a
* **eu-central-1** : ami-00b8e44041f8175fd
* **us-east-2** : ami-0ac7deebb3f738f6d
* **us-west-1** : ami-06bc07deb8b8c44d6
* **us-west-2** : ami-01ba85ffe79a422f1
* **us-east-1** : ami-04cf5a66cb4928ac3
### v0.15.1 (static update)
* **eu-north-1** : ami-0cd314e267426d1b7
* **ap-south-1** : ami-086182cbe29151f96
* **eu-west-3** : ami-0062366012182815b
* **eu-west-2** : ami-022b8f2e32a9d18d0
* **eu-west-1** : ami-0d8cf60446e09aa3d
* **ap-northeast-2** : ami-0d4c168a815b56889
* **ap-northeast-1** : ami-0daf7887db1053ae4
* **sa-east-1** : ami-020a759a3ba4ff22b
* **ca-central-1** : ami-0c10b5e04b707f3e3
* **ap-southeast-1** : ami-0f61bb3529a165fcd
* **ap-southeast-2** : ami-032dcdc82749c66c5
* **eu-central-1** : ami-08f364f32d2eb3bae
* **us-east-2** : ami-0b7efc3591803eba4
* **us-west-1** : ami-08b2df27b0ada6faf
* **us-west-2** : ami-0693029c4bad28816
* **us-east-1** : ami-0200954fa9c2819ff
### v0.15.0 (static update)
* **eu-north-1** : ami-0bef15c03eab64c0c
* **ap-south-1** : ami-06ac6248e583e2cd2
* **eu-west-3** : ami-0541d86ef47a5714e
* **eu-west-2** : ami-01381ef4c4ed22482
* **eu-west-1** : ami-064626a0dd38b21f1
* **ap-northeast-2** : ami-0a2490a7a3a8aa675
* **ap-northeast-1** : ami-063f1de819a2524b8
* **sa-east-1** : ami-07980486741b94987
* **ca-central-1** : ami-0ced3b8b21ded839e
* **ap-southeast-1** : ami-0c493c5093fde8741
* **ap-southeast-2** : ami-0320a727eccb8dc6c
* **eu-central-1** : ami-0aa85cfc78674c526
* **us-east-2** : ami-01791485051e1880c
* **us-west-1** : ami-0d8eade4d5888ea73
* **us-west-2** : ami-02ceaef72cdf60f7e
* **us-east-1** : ami-0fc3f9d1d0eba1d62
### v0.14.2 (static update)
* **eu-north-1** : ami-006d491e9e8869248
* **ap-south-1** : ami-0e55ec221687f98e7
* **eu-west-3** : ami-06ad9cf3c05c83e91
* **eu-west-2** : ami-0d05839268e748cff
* **eu-west-1** : ami-0d14c297789ce0d7a
* **ap-northeast-2** : ami-0d7fd775f0e76cc6f
* **ap-northeast-1** : ami-0c0a6e1daeb3f7a9c
* **sa-east-1** : ami-01e0c5e30e94ec887
* **ca-central-1** : ami-07a31896832734897
* **ap-southeast-1** : ami-0886d5b2d4b7fccd5
* **ap-southeast-2** : ami-0397d5a2db3c356fe
* **eu-central-1** : ami-0629f26eea22f5c17
* **us-east-2** : ami-0499c3d7bb45a1a6e
* **us-west-1** : ami-02fa8a961a4daf9f0
* **us-west-2** : ami-05c711cfab4342468
* **us-east-1** : ami-0b97d99a08012c726
### v0.14.1 (static update)
* **eu-north-1** : ami-036defe1885dced2e
* **ap-south-1** : ami-0b403aa1da6a5dc17
* **eu-west-3** : ami-0d30c2d330d1255c4
* **eu-west-2** : ami-06f0e8d075e50a029
* **eu-west-1** : ami-0da721d874f282b6d
* **ap-northeast-2** : ami-03bffe94675dd5f8c
* **ap-northeast-1** : ami-0f96520d646423673
* **sa-east-1** : ami-0c2f706a3b7d97282
* **ca-central-1** : ami-0da74525dcfd74e32
* **ap-southeast-1** : ami-066368a21cf6d232b
* **ap-southeast-2** : ami-0bfd09170067f7318
* **eu-central-1** : ami-06aa99b1c41492986
* **us-east-2** : ami-065c1880f59d03272
* **us-west-1** : ami-0b7f6b896f5058eba
* **us-west-2** : ami-0041e10ca68eef29a
* **us-east-1** : ami-0b7125e4305bbd7eb
### v0.14.0 (static update)
* **eu-north-1** : ami-02de71586ec496e38
* **ap-south-1** : ami-074b03849b51852e5
* **eu-west-3** : ami-022c388835e0eeb03
* **eu-west-2** : ami-0a151c236c6b27707
* **eu-west-1** : ami-06de69b06b4e73312
* **ap-northeast-2** : ami-0ee821b72d9f669b1
* **ap-northeast-1** : ami-03687ae215e64e100
* **sa-east-1** : ami-01eb83364b7f667af
* **ca-central-1** : ami-02e9b35f9c90377e6
* **ap-southeast-1** : ami-0d3ab5ab0048fea51
* **ap-southeast-2** : ami-0bd39d908fe3a9e06
* **eu-central-1** : ami-0b8638701311b35c4
* **us-east-2** : ami-02ff039693fc3a614
* **us-west-1** : ami-08634f7dfb608a9a7
* **us-west-2** : ami-034d693ef742b9333
* **us-east-1** : ami-0b828b05c323dde7f
### v0.13.0 (static update)
* **eu-north-1** : ami-0d9c74a015e7510d8
* **ap-south-1** : ami-02acd6dd0659bb5c1
* **eu-west-3** : ami-0f0cc5cb6d9afd194
* **eu-west-2** : ami-0298fdc0860206ed9
* **eu-west-1** : ami-0cdc072e528401d5e
* **ap-northeast-2** : ami-0055579cc95b0e53e
* **ap-northeast-1** : ami-0ced7becb9b83b5d0
* **sa-east-1** : ami-033345d0f16a1b5e4
* **ca-central-1** : ami-06c63b05aed47ae67
* **ap-southeast-1** : ami-09f0355f367f30602
* **ap-southeast-2** : ami-0bd2314163ce0fba0
* **eu-central-1** : ami-05fbae957df63e366
* **us-east-2** : ami-050c51b5b4074d3fc
* **us-west-1** : ami-06ad513073d4e5a19
* **us-west-2** : ami-0c96e1361d1d4ca94
* **us-east-1** : ami-07b669040d1eea213
### v0.12.1 (static update)
* **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 (static update)
* **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 (static update)
* **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 (static update)
* **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 (static update)
* **eu-north-1** : ami-05ba33c763877e54e
* **ap-south-1** : ami-0529eec569161cae5
* **eu-west-3** : ami-03cb9396f63e26ff6
* **eu-west-2** : ami-0dd28cc97283cc201
* **eu-west-1** : ami-059cf379ae14b0a24
* **ap-northeast-2** : ami-031409d71f1280616
* **ap-northeast-1** : ami-0171437c68b3660aa
* **sa-east-1** : ami-0eb440a3b6e591c7a
* **ca-central-1** : ami-097da9ec155ee654a
* **ap-southeast-1** : ami-0ab7ff3ea09826e39
* **ap-southeast-2** : ami-00969c550ef2d1f60
* **eu-central-1** : ami-02246400c51990acb
* **us-east-2** : ami-0cafc1d730381d6fa
* **eu-central-1** : ami-02246400c51990acb
* **us-west-1** : ami-0e82a98ddbe995a65
* **us-west-2** : ami-04a522ecb2250fb44
* **us-east-1** : ami-0a66ddbd50959f91e
### v0.9.0 (static update)
* **us-east-1** : ami-0991ad536ecbacdac
* **eu-north-1** : ami-07cbcdff501b14afe
* **ap-south-1** : ami-014cf398b00d4db83
* **eu-west-3** : ami-0396ba51e9b733581
* **eu-west-2** : ami-09134f4c7a20bad09
* **eu-west-1** : ami-00427ed0a1bbfa7b0
* **ap-northeast-2** : ami-041756675ca1be954
* **ap-northeast-1** : ami-0c09ebad05c9128ff
* **sa-east-1** : ami-017a8de4e8d1e8c8e
* **ca-central-1** : ami-049ec444470f852be
* **ap-southeast-1** : ami-0c919b8f821a6c635
* **ap-southeast-2** : ami-04844a0594712d27b
* **eu-central-1** : ami-0b4e756e0f7c0617d
* **us-east-2** : ami-03b01914b07428488
* **us-west-1** : ami-0cf4768e9d47ed076
* **us-west-2** : ami-0b145f37da31eb9fb

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# Deploying Trains Server on Google Cloud Platform
# Deploying ClearML Server on Google Cloud Platform
# **NOTE**: These instructions are deprecated. See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for up-to-date deployment instructions
To easily deploy Trains Server on GCP, use one of our pre-built GCP Custom Images.
We provide Custom Images for each released version of Trains Server, see [Released versions](#released-versions) below.
Once your GCP instance is up and running using our Custom Image, [configure the Trains client](https://github.com/allegroai/trains/blob/master/README.md#configuration) to use your **trains-server**.
#### Default Trains Server Service ports
The service port numbers on our Trains Server GCP Custom Image are:
- Web application: `8080`
- API Server: `8008`
- File Server: `8081`
#### Default Trains Server Storage paths
The persistent storage configuration:
- MongoDB: `/opt/trains/data/mongo/`
- ElasticSearch: `/opt/trains/data/elastic/`
- File Server: `/mnt/fileserver/`
For examples and use cases, check the [Trains usage examples](https://github.com/allegroai/trains/blob/master/docs/trains_examples.md).
## Importing the Custom Image to your GCP account
In order to launch an instance using the Trains Server GCP Custom Image, you'll need to import the image to your custom images list.
**Note:** there's **no need** to upload the image file to Google Cloud Storage - we already provide links to image files stored in Google Storage
To import the image to your custom images list:
1. In the Cloud Console, go to the [Images](https://console.cloud.google.com/compute/images) page.
1. At the top of the page, click **Create image**.
1. In the **Name** field, specify a unique name for the image.
1. Optionally, specify an image family for your new image, or configure specific encryption settings for the image.
1. Click the **Source** menu and select **Cloud Storage file**.
1. Enter the Trains Server image bucket path (see [Trains Server GCP Custom Image](#released-versions)), for example:
`allegro-files/trains-server/trains-server.tar.gz`
1. Click the **Create** button to import the image. The process can take several minutes depending on the size of the boot disk image.
For more information see [Import the image to your custom images list](https://cloud.google.com/compute/docs/import/import-existing-image#import_image) in the [Compute Engine Documentation](https://cloud.google.com/compute/docs).
## Launching an instance with a Custom Image
For instructions on launching an instance using a GCP Custom Image, see the [Manually importing virtual disks](https://cloud.google.com/compute/docs/import/import-existing-image#overview) in the [Compute Engine Documentation](https://cloud.google.com/compute/docs).
For more information on Custom Images, see [Custom Images](https://cloud.google.com/compute/docs/images#custom_images) in the Compute Engine Documentation.
The minimum recommended requirements for Trains Server are:
- 2 vCPUs
- 7.5GB RAM
## Upgrading
To upgrade **trains-server** on an existing GCP instance based on one of these Custom Images, SSH into the instance and follow the [upgrade instructions](../README.md#upgrade) for **trains-server**.
## Network and Security
Please make sure your instance is properly secured.
If not specifically set, a GCP instance will use default firewall rules that allow public access to various ports.
If your instance is open for public access, we recommend you follow best practices for access management, including:
- Allow access only to the specific ports used by Trains Server (see [Default Trains Server Service ports](#default-trains-server-service-ports)). Remember to allow access to port `443` if `https` access is configured for your instance.
- Configure Trains Server to use fixed user names and passwords (see [Can I add web login authentication to trains-server?](./faq.md#web-auth))
## Released versions
The following sections contain lists of Custom Image URLs (exported in different formats) for each released **trains-server** version.
### Latest version image
- https://storage.googleapis.com/allegro-files/trains-server/trains-server.tar.gz
### All released images
- v0.15.1 - https://storage.googleapis.com/allegro-files/trains-server/trains-server-0-15-1.tar.gz
- v0.15.0 - https://storage.googleapis.com/allegro-files/trains-server/trains-server-0-15-0.tar.gz
- v0.14.1 - https://storage.googleapis.com/allegro-files/trains-server/trains-server-0-14-1.tar.gz
# See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_gcp) for up-to-date deployment instructions

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# Launching the **trains-server** Docker in Linux or macOS
# Launching ClearML Server Docker in Linux or macOS
## **NOTE**: These instructions are deprecated. See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for up-to-date deployment instructions
For Linux or macOS, use our pre-built Docker image for easy deployment. The latest Docker images can be found [here](https://hub.docker.com/r/allegroai/trains).
For Linux users:
* You must be logged in as a user with sudo privileges.
* Use `bash` for all command-line instructions in this installation.
To launch **trains-server** on Linux or macOS:
1. Install Docker.
* Linux - see [Docker for Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
* macOS - see [Docker for macOS](https://docs.docker.com/docker-for-mac/install/).
1. Verify the Docker CE installation. Execute the command:
docker run hello-world
The expected is output is:
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (amd64)
3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal.
1. For Linux only, install `docker-compose`. Execute the following commands (for more information, see [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. Increase `vm.max_map_count` for ElasticSearch docker.
Linux:
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:
screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
sysctl -w vm.max_map_count=262144
1. Remove any 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/data/redis
sudo mkdir -p /opt/trains/logs
sudo mkdir -p /opt/trains/config
sudo mkdir -p /opt/trains/data/fileserver
1. For macOS only, open the Docker app, select **Preferences**, and then on the **File Sharing** tab, add `/opt/trains`.
1. Grant access to the Dockers.
Linux:
sudo chown -R 1000:1000 /opt/trains
macOS:
sudo chown -R $(whoami):staff /opt/trains
1. Download the **trains-server** docker-compose YAML file.
cd /opt/trains
curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml
1. Run `docker-compose` with the downloaded configuration file.
docker-compose -f docker-compose.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
* Web server on port `8080`
* API server on port `8008`
* File server on port `8081`
## Next Step
Configure the [Trains client for trains-server](https://github.com/allegroai/trains/blob/master/README.md#configuration).
## See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_linux_mac) for up-to-date deployment instructions

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# Launching the **trains-server** Docker in Windows 10
# Launching the ClearML Server Docker in Windows 10
## **NOTE**: These instructions are deprecated. See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) for up-to-date deployment instructions
For Windows, we recommend launching our pre-built Docker image on a Linux virtual machine.
However, you can launch **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 launch **trains-server** on Windows 10:
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. Remove any previous installation of **trains-server**.
**WARNING**: This clears all existing **Trains** databases.
rmdir c:\opt\trains /s
1. Create local directories for data and logs. Open PowerShell and execute the following commands:
cd c:
mkdir c:\opt\trains\data
mkdir c:\opt\trains\logs
1. Save the **trains-server** docker-compose YAML file.
cd c:\opt\trains
curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose-win10.yml -o docker-compose-win10.yml
1. Run `docker-compose`. In PowerShell, execute the following commands:
docker-compose -f docker-compose-win10.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
* Web server on port `8080`
* API server on port `8008`
* File server on port `8081`
## Next Step
Configure the [Trains client for trains-server](https://github.com/allegroai/trains/blob/master/README.md#configuration).
## See the [ClearML documentation](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_win) for up-to-date deployment instructions