caliban run

This command bundles your code and any other directories you specify into an isolated Docker container and runs the resulting Python code on your local machine, but inside of the Docker environment.

caliban run supports the following arguments:

usage: caliban run [-h] [--helpfull] [--nogpu] [--cloud_key CLOUD_KEY]
                   [--extras EXTRAS] [-d DIR]
                   [--experiment_config EXPERIMENT_CONFIG] [--dry_run]
                   [--image_id IMAGE_ID] [--docker_run_args DOCKER_RUN_ARGS]
                   module ...

positional arguments:
  module                Code to execute, in either 'trainer.train' or
                        'trainer/' format. Accepts python scripts,
                        modules or a path to an arbitrary script.

optional arguments:
  -h, --help            show this help message and exit
  --helpfull            show full help message and exit
  --nogpu               Disable GPU mode and force CPU-only.
  --cloud_key CLOUD_KEY
                        Path to GCloud service account key. (Defaults to
  --extras EXTRAS dependency keys.
  -d DIR, --dir DIR     Extra directories to include. List these from large to
                        small to take full advantage of Docker's build cache.
  --experiment_config EXPERIMENT_CONFIG
                        Path to an experiment config, or 'stdin' to read from
  --dry_run             Don't actually submit; log everything that's going to
  --image_id IMAGE_ID   Docker image ID accessible in the local Docker
                        registry. If supplied, Caliban will skip the 'docker
                        build' step and use this image.
  --docker_run_args DOCKER_RUN_ARGS
                        String of args to add to Docker.

pass-through arguments:
  -- YOUR_ARGS          This is a catch-all for arguments you want to pass
                        through to your script. any arguments after '--' will
                        pass through.

Because the container is completely isolated, to get any results from caliban run you’ll have to depend on either:

  • stdout, if you’re just interested in checking if the job is running at all before submission to Cloud, for example, or

  • Cloud buckets for persistence.

Your credentials are set up inside the container and available via the required $GOOGLE_APPLICATION_CREDENTIALS environment variable, so all Cloud access via Python should Just Work. (See the guide on gcloud authentication for more detail.)

The base Caliban images also have gcloud installed; all gcloud and gsutil commands will work with the same permissions granted to the key found at $GOOGLE_APPLICATION_CREDENTIALS.

As with the other commands, the only python dependencies available in the container will be dependencies that you declare explicitly in either:

  • a requirements.txt file

  • a file.

Your setup file can declare groups of dependencies using the setuptools extras_require feature. (See the Declaring Requirements docs for more detail on how to use extras_require to create separate environments for GPU and CPU.)

Executing a Python script

The most basic way to trigger a run is by passing a file path or a module name. Any of the following will work:

caliban run trainer.train -- --epochs 2
caliban run trainer/
caliban run
caliban run mycode -- --learning_rates '[1,2,3]'

Any flags or commands you pass to the command after -- will be passed along, untouched, to your Python code. By configuring your job with flags you can get a large range of behavior out of the same module.

If you specify a Python module inside of a folder, caliban run will copy only that folder into the Docker environment. For example, if you have a trainer/ file and run either of the following:

caliban run trainer.train
caliban run trainer/

Caliban will copy only the trainer directory into the container.

If your script lives in the root of the directory, as in the example above, the entire current working directory will be copied in.

This could be inefficient if your directory has lots of data you don’t want, or a folder of notebooks; if you want a smaller build image you can move your script into a folder. Make sure to create inside the folder to make it a proper module.

In addition to the required module name, caliban run supports many optional arguments. All of these must be supplied before the module name.

Jobs run in GPU mode by default. To toggle GPU mode off, use --nogpu.

Extra Directories

If you want to make extra directories available inside your container, pass them like this:

caliban -d data -d models/stored trainer.train

This invocation will copy the data and models/stored directories into the container, where they can be accessed using a relative path. All directories must exist relative to the directory where you run caliban run.