clearml-server/README.md

# Trains Server

##  Auto-Magical Experiment Manager & Version Control for AI - ε Devops Included!

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### Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)

## :rocket: Trains-Agent Services is now included, for more information see [services](https://github.com/allegroai/trains-server#services)

## v0.16 Upgrade Notice

In v0.16, the Elasticsearch subsystem of Trains Server has been upgraded from version 5.6 to version 7.6. This change necessitates the migration of the database contents to accommodate the change in index structure across the different versions.  

Follow [this procedure](https://allegro.ai/docs/deploying_trains/trains_server_es7_migration/) to migrate existing data.

## 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 launch **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 [deploy](#launching-trains-server)  your **trains-server** using Docker, AWS EC2 AMI, or Kubernetes. 

## 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

- 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.\*.\*
    
## Launching trains-server

### Prerequisites

The ports 8080/8081/8008 must be available for the **trains-server** services.
   
For example, to see if port `8080` is in use:

* Linux or macOS: 
   
        sudo lsof -Pn -i4 | grep :8080 | grep LISTEN

* Windows:

        netstat -an |find /i "8080"
   
### Launching   
    
Launch **trains-server** in any of the following formats:

- Pre-built [AWS EC2 AMI](https://allegro.ai/docs/deploying_trains/trains_server_aws_ec2_ami/)
- Pre-built [GCP Custom Image](https://allegro.ai/docs/deploying_trains/trains_server_gcp/)
- Pre-built Docker Image
    - [Linux](https://allegro.ai/docs/deploying_trains/trains_server_linux_mac/)
    - [macOS](https://allegro.ai/docs/deploying_trains/trains_server_linux_mac/)
    - [Windows 10](https://allegro.ai/docs/deploying_trains/trains_server_win/)
- Kubernetes    
    - [Kubernetes Helm](https://allegro.ai/docs/deploying_trains/trains_server_kubernetes_helm/)
    - Manual [Kubernetes installation](https://allegro.ai/docs/deploying_trains/trains_server_kubernetes/)

## Connecting Trains to your trains-server

By default, the **Trains** client is set up to work with the [**Trains** demo server](https://demoapp.trains.allegro.ai/).  
To have the **Trains** client use your **trains-server** instead:
- Run the `trains-init` command for an interactive setup.
- Or manually edit `~/trains.conf` file, making sure the server settings (`api_server`, `web_server`, `file_server`) are 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"
        }

**Note**: If you have set up **trains-server** in a sub-domain configuration, then there is no need to specify a port number,
it will be inferred from the http/s scheme.

After launching the **trains-server** and configuring the **Trains** client to use the **trains-server**,
you can [use](https://github.com/allegroai/trains#using-trains) **Trains** in your experiments and view them in your **trains-server** web server,
for example http://localhost:8080.  
For more information about the Trains client, see [**Trains**](https://github.com/allegroai/trains).

## Trains-Agent Services  <a name="services"></a> 

As of version 0.15 of **trains-server**, dockerized deployment includes a **Trains-Agent Services** container running as 
part of the docker container collection.

Trains-Agent Services is an extension of Trains-Agent that provides the ability to launch long-lasting jobs 
that previously had to be executed on local / dedicated machines. It allows a single agent to 
launch multiple dockers (Tasks) for different use cases. To name a few use cases, auto-scaler service (spinning instances 
when the need arises and the budget allows), Controllers (Implementing pipelines and more sophisticated DevOps logic),
Optimizer (such as Hyper-parameter Optimization or sweeping), and Application (such as interactive Bokeh apps for 
increased data transparency)

Trains-Agent Services container will spin **any** task enqueued into the dedicated `services` queue. 
Every task launched by Trains-Agent Services  will be registered as a new node in the system, 
providing tracking and transparency capabilities.  
You can also run the Trains-Agent Services manually, see details in [trains-agent services mode](https://github.com/allegroai/trains-agent#trains-agent-services-mode-)

**Note**: It is the user's responsibility to make sure the proper tasks are pushed into the `services` queue. 
Do not enqueue training / inference tasks into the `services` queue, as it will put unnecessary load on the server.

## Advanced Functionality

**trains-server** provides a few additional useful features, which can be manually enabled:
 
* [Web login authentication](https://allegro.ai/docs/faq/faq/#web-auth)
* [Non-responsive experiments watchdog](https://allegro.ai/docs/faq/faq/#watchdog)  

## Restarting trains-server

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
   ```

## Upgrading <a name="upgrade"></a>

**trains-server** releases are also reflected in the [docker compose configuration file](https://github.com/allegroai/trains-server/blob/master/docker-compose.yml).  
We strongly encourage you to keep your **trains-server** up to date, by keeping up with the current release.

**Note**: The following upgrade instructions use the Linux OS as an example.

To upgrade your existing **trains-server** deployment:

1. Shut down the docker containers
   ```bash
   docker-compose down
   ```

1. We highly recommend backing up your data directory before upgrading.

   Assuming your data directory is `/opt/trains`, to archive all data into `~/trains_backup.tgz` execute:

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

   <details>
   <summary>Restore instructions:</summary>

   To restore this example backup, execute:
   ```bash
   sudo rm -R /opt/trains/data
   sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
   ```
   </details>

1. Download the latest `docker-compose.yml` file.

   ```bash
   curl https://raw.githubusercontent.com/allegroai/trains-server/master/docker-compose.yml -o docker-compose.yml 
   ```

1. Configure the Trains-Agent Services (not supported on Windows installation). 
   If `TRAINS_HOST_IP` is not provided, Trains-Agent Services will use the external 
   public address of the **trains-server**. If `TRAINS_AGENT_GIT_USER` / `TRAINS_AGENT_GIT_PASS` are not provided, 
   the Trains-Agent Services will not be able to access any private repositories for running service tasks.
   
   ```bash
   export TRAINS_HOST_IP=server_host_ip_here
   export TRAINS_AGENT_GIT_USER=git_username_here
   export TRAINS_AGENT_GIT_PASS=git_password_here
   ```

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
   ```

**\* If something went wrong along the way, check our FAQ: [Common Docker Upgrade Errors](https://allegro.ai/docs/faq/faq/#common-docker-upgrade-errors).**


## Community & Support

If you have any questions, look to the Trains [FAQ](https://allegro.ai/docs/faq/faq/), 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
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!