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Executable Task Containers

This tutorial demonstrates using clearml-agent's build command to package a task into an executable container. In this example, you will build a container image that, when run, will automatically execute the keras_tensorboard.py script.

Prerequisites​

  • clearml-agent installed and configured
  • clearml installed and configured
  • clearml repo cloned (git clone https://github.com/clearml/clearml.git)

Creating the ClearML Task​

  1. Set up the task's execution environment:

    cd clearml/examples/frameworks/keras
    pip install -r requirements.txt

  2. Run the task:

    python keras_tensorboard.py

    This creates a ClearML task called "Keras with TensorBoard example" in the "examples" project.

    Note the task ID in the console output when running the script above:

    ClearML Task: created new task id=<TASK_ID>

    This ID will be used in the following section.

Building and Launching a Containerized Task​

  1. Execute the following command to build the container. Input the ID of the task created above:

    clearml-agent build --id <TASK_ID> --docker --target new-docker --entry-point clone_task
    tip

    If the container will not make use of a GPU, add the --cpu-only flag.

    This command will create a container, set up with the execution environment for this task in the specified --target folder. When the container is launched, it will clone the task specified with id and execute the clone (as designated by the --entry-point parameter).

  2. Run the Docker, pointing to the new container:

    docker run new-docker

    The task will be executed inside the container. Task details can be viewed in the ClearML Web UI.

For additional ClearML Agent options, see the ClearML Agent reference page.