Upgrading Server from v0.15 or Older to ClearML Server

important

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

In v0.16, the Elasticsearch subsystem of Trains Server was 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.

This page provides the instructions to carry out the migration process. Follow this process if using Trains Server version 0.15 or older and are upgrading to ClearML Server.

The migration process makes use of a script that automatically performs the following:

Backs up the existing Trains Server Elasticsearch data.
Launches a pair of Elasticsearch 5 and Elasticsearch 7 migration containers.
Copies the Elasticsearch indices using the migration containers.
Terminates the migration containers.
Renames the original data directory to avoid accidental reuse.

warning

Once the migration process completes successfully, the data is no longer accessible to the older version of Trains Server, and ClearML Server needs to be installed.

Prerequisites#

Read/write permissions for the default Trains Server data directory /opt/clearml/data and its subdirectories, or, if this default directory is not used, the permissions for the directory and subdirectories that are used.
A minimum of 8GB system RAM.
Minimum free disk space of at least 30% plus two times the size of the data.
Python version >=2.7 or >=3.6, and Python accessible from the command-line as python

Migrating the data#

To migrate the data:

If the Trains Server is up, shut it down:
- Linux and macOS
  docker-compose -f /opt/trains/docker-compose.yml down
- Windows
  docker-compose -f c:\opt\trains\docker-compose-win10.yml down
- Kubernetes
  kubectl delete -k overlays/current_version
- Kubernetes using Helm
  helm del --purge trains-server
  kubectl delete namespace trains
For Kubernetes and Kubernetes using Helm, connect to the node in the Kubernetes cluster labeled app=trains.
Download the migration package archive.
curl -L -O https://github.com/allegroai/clearml-server/releases/download/0.16.0/trains-server-0.16.0-migration.zip
If the file needs to be downloaded manually, use this direct link: trains-server-0.16.0-migration.zip.
Extract the archive.
unzip trains-server-0.16.0-migration.zip -d /opt/trains
Migrate the data.
- Linux, macOS, and Windows - if managing own containers.
  Run the migration script. If elevated privileges are used to run Docker (sudo in Linux, or admin in Windows), then use elevated privileges to run the migration script.
  python elastic_upgrade.py [-s|--source <source_path>] [-t|--target <target_path>] [-n|--no-backup] [-p|--parallel]
  The following optional command line parameters can be used to control the execution of the migration script:
  - <source_path> - The path to the Elasticsearch data directory in the current Trains Server deployment.
    If not specified, uses the default value of /opt/trains/data/elastic (or c:\opt\trains\data\elastic in Windows)
  - <target_path> - The path to the Elasticsearch data directory in the current Trains Server deployment.
    If not specified, uses the default value of /opt/trains/data/elastic_7 (or c:\opt\trains\data\elastic_7 in Windows)
  - no-backup - Skip creating a backup of the existing Elasticsearch data directory before performing the migration.
    If not specified, takes on the default value of False (Performs backup)
  - parallel - Copy several indices in parallel to utilize more CPU cores. If not specified, parallel indexing is turned off.
- Kubernetes
  1. Clone the trains-server-k8s repository and change to the new trains-server-k8s/upgrade-elastic directory:
    git clone https://github.com/allegroai/clearml-server-k8s.git && cd clearml-server-k8s/upgrade-elastic
  2. Create the upgrade-elastic namespace and deployments:
    kubectl apply -k overlays/current_version
    Wait for the job to be completed. To check if it's completed, run:
    kubectl get jobs -n upgrade-elastic
- Kubernetes using Helm
  1. Add the clearml-server repository to Helm client.
    helm repo add allegroai https://allegroai.github.io/clearml-server-helm/
    Confirm the clearml-server repository is now in the Helm client.
    helm search clearml
    The helm search results must include allegroai/upgrade-elastic-helm.
  2. Install upgrade-elastic-helm on the cluster:
    helm install allegroai/upgrade-elastic-helm --namespace=upgrade-elastic --name upgrade
    An upgrade-elastic namespace is created in the cluster, and the upgrade is deployed in it.
    Wait for the job to complete. To check if it completed, execute the following command:
    kubectl get jobs -n upgrade-elastic

Finishing up#

To finish up:

Verify the data migration
Conclude the upgrade.

Step 1. Verifying the data migration#

Upon successful completion, the migration script renames the original Trains Server directory, which contains the now migrated data, and prints a completion message:

Renaming the source directory /opt/trains/data/elastic to /opt/trains/data/elastic_migrated_<date_time>.
Upgrade completed.

All console output during the execution of the migration script is saved to a log file in the directory where the migration script executes:

<path_to_script>/upgrade_to_7_<date_time>.log

If the migration script does not complete successfully, the migration script prints the error.

important

For help in resolving migration issues, check the allegro-clearml Slack Channel, GitHub Issues, and the ClearML Server sections of the FAQ.

Step 2. Completing the installation#

After verifying the data migration completed successfully, conclude the ClearML Server installation process.

Linux or macOS#

For Linux or macOS, conclude with the steps in this section. For other deployment formats, see below.

Important: Upgrading from v0.14 or older

For Linux only, if upgrading from **Trains Server** v0.14 or older, configure the **ClearML Agent Services**.

If CLEARML_HOST_IP is not provided, then ClearML Agent Services will use the external public address of the ClearML Server.
If CLEARML_AGENT_GIT_USER / CLEARML_AGENT_GIT_PASS are not provided, then ClearML Agent Services will not be able to access any private repositories for running service tasks.

  export CLEARML_HOST_IP=server_host_ip_here
  export CLEARML_AGENT_GIT_USER=git_username_here
  export CLEARML_AGENT_GIT_PASS=git_password_here

note

For backwards compatibility, the environment variables TRAINS_HOST_IP, TRAINS_AGENT_GIT_USER, and TRAINS_AGENT_GIT_PASS are supported.

We recommend backing up data and, if the configuration folder is not empty, backing up the configuration.
For example, if the data and configuration folders are in /opt/trains, then archive all data into ~/trains_backup_data.tgz, and the configuration into ~/trains_backup_config.tgz:
sudo tar czvf ~/trains_backup_data.tgz -C /opt/trains/data .
sudo tar czvf ~/trains_backup_config.tgz -C /opt/trains/config .
Rename /opt/trains and its subdirectories to /opt/clearml.
sudo mv /opt/trains /opt/clearml
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
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 the upgrade, see the FAQ page, How do I fix Docker upgrade errors?.

Other deployment formats#

To conclude the upgrade for deployment formats other than Linux, follow their upgrade instructions: