This tutorial demonstrates using
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
clearml-agentinstalled and configured
clearmlinstalled and configured
- clearml repo cloned (
git clone https://github.com/allegroai/clearml.git)
Creating the ClearML Experiment
Set up the experiment's execution environment:
pip install -r requirements.txt
Run the experiment:
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
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_tasktip
If the container will not make use of a GPU, add the
This command will create a Docker container, set up with the execution environment for this experiment in the specified
--targetfolder. When the Docker container is launched, it will clone the task specified with
idand execute the clone (as designated by the
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.