Using a Single GPU

By default, docker run will make all GPUs on your workstation available inside of the container. This means that in caliban shell, caliban notebook or caliban run, any jobs executed on your workstation will attempt to use:

  • your huge GPU, custom-built and installed for ML Supremacy

  • the dinky GPU that exists solely to power your monitor, NOT to help train models

The second GPU will slow down everything.

To stop this from happening you need to set the CUDA_VISIBLE_DEVICES environment variable equal to 0, as described on this nvidia blog about the issue.

You can set the environment variable inside your container by passing --docker_run_args to caliban, like this:

caliban run --docker_run_args "--env CUDA_VISIBLE_DEVICES=0" trainer.train


you may have noticed that this problem doesn’t happen when you run a job inside caliban shell. Your local environment may have CUDA_VISIBLE_DEVICES set. caliban shell and caliban notebook mount your home directory by default, which loads all of your local environment variables into the container and, if you’ve set this environment variable, modifies this setting inside your container. This doesn’t happen with caliban run or caliban cloud. You will always need to use this trick with those modes.

There are two other ways to solve this problem using the custom ``docker run` arguments detailed here <>`_. You can directly limit the GPUs that mount into the container using the --gpus argument:

caliban run --docker_run_args "--gpus device=0" trainer.train

If you run nvidia-smi in the container after passing this argument you won’t see more than 1 GPU. This is useful if you know that some library you’re using doesn’t respect the CUDA_VISIBLE_DEVICES environment variable for any reason.

You could also pass this and other environment variables using an env file. Given some file, say, myvars.env, whose contents look like this:


The --env-file argument will load all of the referenced variables into the docker environment:

caliban run --docker_run_args "--env-file myvars.env" trainer.train

Check out Custom Docker Run Arguments for more information.