clearml-docs/docs/guides/clearml_agent/executable_exp_containers.md

title
Executable Experiment Containers

This tutorial demonstrates using clearml-agent's build command to package an experiment into an executable container. In this example, you will build a Docker 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/allegroai/clearml.git)

Creating the ClearML Experiment

1. Set up the experiment's execution environment:

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

2. Run the experiment:

   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 Docker container, set up with the execution environment for this experiment in the specified --target folder. When the Docker 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.