The torchx CLI is a commandline tool around torchx.runner.Runner. It allows users to launch torchx.specs.AppDef directly onto one of the supported schedulers without authoring a pipeline (aka workflow). This is convenient for quickly iterating on the application logic without incurring both the technical and cognitive overhead of learning, writing, and dealing with pipelines.


When in doubt use torchx --help.

Listing the builtin components

Most of the components under the torchx.components module are what the CLI considers “built-in” apps. Before you write your own component you should browse through the builtins to see if there is one that fits your needs already. If so, no need to even author an app spec!

$ torchx builtins
Found <n> builtin configs:
 1. metrics.tensorboard
 2. serve.torchserve
 3. utils.binary
 ... <omitted for brevity>

Listing the supported schedulers and arguments

To get a list of supported schedulers that you can launch your job into run:

$ torchx runopts
{ 'log_dir': { 'default': 'None',
               'help': 'dir to write stdout/stderr log files of replicas',
               'type': 'str'}}

Running a component as a job

The run subcommand takes either one of:

  1. name of the builtin

    $ torchx run --scheduler <sched_name> utils.echo
  2. full python module path of the component function

    $ torchx run --scheduler <sched_name> torchx.components.utils.echo
  3. file path of the *.py file the defines the component along with the component function name in that file.

    $ cat ~/
    import torchx.specs as specs
    def my_trainer(foo: int, bar: str) -> specs.AppDef:
      <...spec file details omitted for brevity...>
    $ torchx run --scheduler <sched_name> ~/

Now that you have understood how to chose which app to launch, now it is time to see what parameters need to be passed. There are three sets of parameters:

  1. arguments to the run subcommand, see a list of them by running:

    $ torchx run --help
  2. arguments to the scheduler (--scheduler_args, also known as run_options or run_configs), each scheduler takes different args, to find out the args for a specific scheduler run (command for local_cwd scheduler shown below:

    $ torchx runopts local_cwd
    { 'log_dir': { 'default': 'None',
               'help': 'dir to write stdout/stderr log files of replicas',
               'type': 'str'}}
    # pass run options as comma-delimited k=v pairs
    $ torchx run --scheduler local_cwd --scheduler_args log_dir=/tmp ...
  3. arguments to the component (the app args are included here), this also depends on the component and can be seen with the --help string on the component

    $ torchx run --scheduler local_cwd utils.echo --help
    usage: torchx run echo.torchx [-h] [--msg MSG]
    Echos a message
    optional arguments:
    -h, --help  show this help message and exit
    --msg MSG   Message to echo

Putting everything together, running echo with the local_cwd scheduler:

$ torchx run --scheduler local_cwd --scheduler_args log_dir=/tmp utils.echo --msg "hello $USER"
=== RUN RESULT ===
torchx 2022-06-15 16:08:57 INFO     Log files located in: /tmp/torchx/echo-crls3hcpwjmhc/echo/0

By default the run subcommand does not block for the job to finish, instead it simply schedules the job on the specified scheduler and prints an app handle which is a URL of the form: $scheduler_name://torchx/$job_id. Keep note of this handle since this is what you’ll need to provide to other subcommands to identify your job.


If the --scheduler option is not provided, then it defaults to the scheduler backend default which points to local_cwd. To change the default scheduler, see: Registering Custom Schedulers.

Inspecting what will run (dryrun)

When you are authoring or debugging a component, you may want to find out and inspect both the scheduler request object that the runner submits as well as the AppDef object that is created by the component function. To do this, use the --dryrun option to the run subcommand:

$ torchx run --dryrun utils.echo --msg hello_world
{ 'metadata': {},
  'name': 'echo',
  'roles': [ { 'args': ['hello_world'],
               'entrypoint': '/bin/echo',
               'env': {},
               'image': '/tmp',
               'max_retries': 0,
               'name': 'echo',
               'num_replicas': 1,
               'port_map': {},
               'resource': { 'capabilities': {},
                             'cpu': -1,
                             'gpu': -1,
                             'memMB': -1},
               'retry_policy': <RetryPolicy.APPLICATION: 'APPLICATION'>}]}
        'echo': [
                args=['/bin/echo', 'hello_world'],
                env={'TORCHELASTIC_ERROR_FILE': '/tmp/torchx_asmtmyqj/torchx_kiuk/echo_c944ffb2/echo/0/error.json'},
    role_log_dirs={'echo': ['/tmp/torchx_asmtmyqj/torchx_kiuk/echo_c944ffb2/echo/0']})


The scheduler request print out will look different based on the scheduler type. The example above is a faux request since the scheduler is a local scheduler which simply uses subprocess.Popen to simulate replicas as a POSIX process. Nevertheless the scheduler request contains valuable insight into what the runner translates the AppDef to for a particular scheduler backend.

Describing and querying the status of a job

The describe subcommand is essentially the inverse of the run command. That is, it prints the app spec given an app_handle.

$ torchx describe <app handle>


The describe command attempts to recreate an app spec by querying the scheduler for the job description. So what you see printed is not always 100% the exact same app spec that was given to the run. The extent to which the runner can recreate the app spec depends on numerous factors such as how descriptive the scheduler’s describe_job API is as well as whether there were fields in the app spec that were ignored when submitting the job to the scheduler because the scheduler does not have support for such a parameter/functionality. NEVER rely on the describe API as a storage function for your app spec. It is simply there to help you spot check things.

To get the status of a running job:

$ torchx status <app_handle> # prints status for all replicas and roles
$ torchx status --role trainer <app_handle> # filters it down to the trainer role

Filtering by --role is useful for large jobs that have multiple roles.

Viewing Logs


This functionality depends on how long your scheduler setup retains logs TorchX DOES NOT archive logs on your behalf, but rather relies on the scheduler’s get_log API to obtain the logs. Refer to your scheduler’s user manual to setup log retention properly.

The log subcommand is a simple wrapper around the scheduler’s get_log API to let you pull/print the logs for all replicas and roles from one place. It also lets you pull replica or role specific logs. Below are a few log access patterns that are useful and self explanatory

$ torchx log <app_handle>
Prints all logs from all replicas and roles (each log line is prefixed with role name and replica id)

$ torchx log --tail <app_handle>
If the job is still running tail the logs

$ torchx log --regex ".*Exception.*" <app_handle>
regex filter to exceptions

$ torchx log <app_handle>/<role>
$ torchx log <app_handle>/trainer
pulls all logs for the role trainer

$ torchx log <app_handle>/<role_name>/<replica_id>
$ torchx log <app_handle>/trainer/0,1
only pulls trainer 0 and trainer 1 (node not rank) logs


Some schedulers do not support server-side regex filters. In this case the regex filter is applied on the client-side, meaning the full logs will have to be passed through the client. This may be very taxing to the local host. Please use your best judgment when using the logs API.

Listing Jobs

The list subcommand lets you list app handles and statuses of apps launched on a scheduler. You can then use the app handles to further describe the app, view the logs, etc.

$ torchx list -s <scheduler_name>

$ torchx list -s kubernetes
APP HANDLE                                          APP STATUS
--------------------------------------------------  ------------
kubernetes://torchx/default:trainer-a5qvfhe1hyq2w   SUCCEEDED
kubernetes://torchx/default:trainer-d796ei2tdtest   SUCCEEDED
kubernetes://torchx/default:trainer-em0iao2m90000   FAILED
kubernetes://torchx/default:trainer-ew33oxmdg0123   SUCCEEDED


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