**ClearML - Auto-Magical Suite of tools to streamline your ML workflow
Experiment Manager, ML-Ops and Data-Management** [![GitHub license](https://img.shields.io/badge/license-SSPL-green.svg)](https://img.shields.io/badge/license-SSPL-green.svg) [![Python versions](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg) [![GitHub version](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)
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**v0.16 Upgrade Notice**
In v0.16, the Elasticsearch subsystem of ClearML 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. --- ### ClearML Server #### *Formerly known as Trains Server* The **ClearML Server** is the backend service infrastructure for [ClearML](https://github.com/allegroai/clearml). It allows multiple users to collaborate and manage their experiments. By default, **ClearML** is set up to work with the **ClearML** demo server, which is open to anyone and resets periodically. In order to host your own server, you will need to launch the **ClearML Server** and point **ClearML** to it. The **ClearML Server** contains the following components: * The **ClearML** 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-the-clearml-server) your **ClearML Server** using Docker, AWS EC2 AMI, or Kubernetes. ## System design ![Alt Text](https://allegro.ai/clearml/docs/_images/ClearML_Server_Diagram.png) The **ClearML 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 The ClearML Server ### Prerequisites The ports 8080/8081/8008 must be available for the **ClearML 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 The **ClearML 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 ClearML to your ClearML Server By default, the **ClearML** client is set up to work with the [**ClearML** demo server](https://demoapp.demo.clear.ml/). To have the **ClearML** client use your **ClearML Server** instead: - Run the `clearml-init` command for an interactive setup. - Or manually edit `~/clearml.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 your **ClearML 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 **ClearML Server** and configuring the **ClearML** client to use the **ClearML Server**, you can [use](https://github.com/allegroai/clearml) **ClearML** in your experiments and view them in your **ClearML Server** web server, for example http://localhost:8080. For more information about the ClearML client, see [**ClearML**](https://github.com/allegroai/clearml). ## ClearML-Agent Services As of version 0.15 of **ClearML Server**, dockerized deployment includes a **ClearML-Agent Services** container running as part of the docker container collection. ClearML-Agent Services is an extension of ClearML-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) ClearML-Agent Services container will spin **any** task enqueued into the dedicated `services` queue. Every task launched by ClearML-Agent Services will be registered as a new node in the system, providing tracking and transparency capabilities. You can also run the ClearML-Agent Services manually, see details in [ClearML-agent services mode](https://github.com/allegroai/clearml-agent#clearml-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 The **ClearML Server** provides a few additional useful features, which can be manually enabled: * [Web login authentication](https://allegro.ai/clearml/docs/deploying_clearml/clearml_server_config/#web-login-authentication) * [Non-responsive experiments watchdog](https://allegro.ai/clearml/docs/deploying_clearml/clearml_server_config/#task_watchdog) ## Restarting ClearML Server To restart the **ClearML Server**, you must first stop the containers, and then restart them. ```bash docker-compose down docker-compose -f docker-compose.yml up ``` ## Upgrading **ClearML 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 **ClearML 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 **ClearML 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/clearml`, to archive all data into `~/clearml_backup.tgz` execute: ```bash sudo tar czvf ~/clearml_backup.tgz /opt/clearml/data ```
Restore instructions: To restore this example backup, execute: ```bash sudo rm -R /opt/clearml/data sudo tar -xzf ~/clearml_backup.tgz -C /opt/clearml/data ```
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 ClearML-Agent Services (not supported on Windows installation). If `TRAINS_HOST_IP` is not provided, ClearML-Agent Services will use the external public address of the **ClearML Server**. If `TRAINS_AGENT_GIT_USER` / `TRAINS_AGENT_GIT_PASS` are not provided, the ClearML-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 **ClearML 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/clearml/docs/docs/faq/faq.html).** ## Community & Support If you have any questions, look to the ClearML [FAQ](https://allegro.ai/clearml/docs/docs/faq/faq.html), 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 *clearml@allegro.ai* ## License [Server Side Public License v1.0](https://github.com/mongodb/mongo/blob/master/LICENSE-Community.txt) The **ClearML 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!