Deploying ClearML Server: Google Cloud Platform

Important

This documentation page applies to deploying your own open source ClearML Server. It does not apply to ClearML Hosted Service users.

Deploy ClearML Server on the Google Cloud Platform (GCP) using use one of the pre-built GCP Custom Images. ClearML provides custom images for each released version of ClearML Server. For a list of the pre-built custom images, see ClearML Server GCP Custom Image.

After deploying ClearML Server, configure the ClearML Python Package for it, see Configuring ClearML for ClearML Server.

Warning

If you are reinstalling ClearML Server, we recommend clearing your browser cookies for ClearML Server. For example, go to Developer Tools > Storage > Cookies (Firefox), Developer Tools > Application > Cookies (Chrome) and deleting all cookies under the ClearML Server URL.

Default ClearML Server service ports

After deploying ClearML Server, the services expose the following node ports:

  • Web server on 8080

  • API server on 8008

  • File Server on 8081

Default ClearML Server storage paths

The persistent storage configuration:

  • MongoDB: /opt/clearml/data/mongo/

  • Elasticsearch: /opt/clearml/data/elastic_7/

  • File Server: /mnt/fileserver/

Importing the Custom Image to your GCP account

Before launching an instance using a ClearML Server GCP Custom Image, import the image to your custom images list.

Note

No upload of the image file is required. We 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 page.

  2. At the top of the page, click Create image.

  3. In Name, specify a unique name for the image.

  4. Optionally, specify an image family for your new image, or configure specific encryption settings for the image.

  5. In the Source menu, select Cloud Storage file.

  6. Enter the ClearML Server image bucket path (see ClearML Server GCP Custom Image), for example: allegro-files/clearml-server/clearml-server.tar.gz.

  7. Click Create 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 in the Compute Engine Documentation.

Launching

Warning

By default, ClearML Server with unrestricted access. To restrict ClearML Server access, follow the instructions in the Securing Your Own ClearML Server page.

To launch ClearML Server using a GCP Custom Image, see the Manually importing virtual disks in the “Google Cloud Storage” documentation, Compute Engine documentation. For more information on Custom Images, see Custom Images in the “Compute Engine documentation”.

The minimum requirements for ClearML Server are:

  • 2 vCPUs

  • 7.5GB RAM

Restarting

To restart your ClearML Server Docker deployment:

  • To restart ClearML Server, first stop and then restart the Docker containers by executing the following commands:

      docker-compose -f /opt/clearml/docker-compose.yml down
  docker-compose -f /opt/clearml/docker-compose.yml up -d

Upgrading

To upgrade your ClearML Server Docker deployment:

  1. Shut down the docker containers. Execute the following command:

     docker-compose -f docker-compose.yml down

  2. If you are upgrading from Trains Server, do the following:

    1. If you are upgrading from Trains Server version 0.15 or older to ClearML Server, a data migration is required before you upgrade. First follow these data migration instructions, and then continue this upgrade.

    2. Rename /opt/trains and its subdirectories to /opt/clearml.

       sudo mv /opt/trains /opt/clearml

  3. We recommend backing up your data and, if your configuration folder is not empty, backing up your configuration.

  4. Download the latest docker-compose.yml file.

     curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml

  5. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

     docker-compose -f /opt/clearml/docker-compose.yml pull
 docker-compose -f /opt/clearml/docker-compose.yml up -d

If issues arise during your upgrade, see the FAQ page, How do I fix Docker upgrade errors?.

Backing up and restoring data and configuration

The commands in this section are an example of how to backup and restore your data and configuration .

If your data and configuration folders are in /opt/clearml, then archive all data into ~/clearml_backup_data.tgz, and your configuration into ~/clearml_backup_config.tgz:

sudo tar czvf ~/clearml_backup_data.tgz -C /opt/clearml/data .
sudo tar czvf ~/clearml_backup_config.tgz -C /opt/clearml/config .

If you need to restore your data and configuration:

  1. Verify you have the backup files.

  2. Replace any existing data with the backup data:

     sudo rm -fR /opt/clearml/data/* /opt/clearml/config/*
 sudo tar -xzf ~/clearml_backup_data.tgz -C /opt/clearml/data
 sudo tar -xzf ~/clearml_backup_config.tgz -C /opt/clearml/config

  3. Grant access to the data:

     sudo chown -R 1000:1000 /opt/clearml

Examples and use cases

For examples and use cases, see the Examples section in this User Guide documentation, and the examples folder of the ClearML repository.

ClearML Server GCP Custom Image

The following a section contains a list of Custom Image URLs (exported in different formats) for each released ClearML Server version.