ClearML Task is ClearML's Zero Code Integration Module. Using only the command line and zero additional lines of code, you can easily track your work and integrate ClearML with your existing code.
clearml-task automatically integrates ClearML into any script or any python repository.
clearml-task has the option
to send the Task to a queue, where a ClearML Agent listening to the queue will fetch the Task it and executes it on a
remote or local machine. It's even possible to provide command line arguments and provide Python module dependencies and requirements.txt file!
clearml-task, pointing it to your script or repository, and optionally an execution queue.
clearml-taskdoes its magic! It creates a new experiment on the ClearML Server, and, if a queue was specified, it sends the experiment to the queue to be fetched and executed by a ClearML Agent.
- The command line will provide you with a link to your Task's page in the ClearML web UI, where you will be able to view the Task's details.
Specify a docker container to run the code in by with the
--docker <docker_image> flag.
The ClearML Agent will pull it from dockerhub or a docker artifactory automatically.
If the local script requires packages to be installed installed or the remote repository doesn't have a requirements.txt file,
specify manually the required python packages using
--packages "<package_name>", for example
--packages "keras" "tensorflow>2.2".
Tasks are passed to ClearML Agents via Queues. Specify a queue to enqueue the Task to.
If a queue isn't chosen in the
clearml-task command, the Task will not be executed; it will be left in draft mode,
and can be enqueued at a later point.
A specific branch and commit ID, other than latest commit in master, to be executed can be specified by passing
--branch <branch_name> --commit <commit_id> flags.
clearml-task will use the latest commit from the master branch.
Learn how to use the
clearml-task feature here.