Docker

class torchx.schedulers.docker_scheduler.DockerScheduler(session_name: str)

Bases: DockerWorkspaceMixin, Scheduler[DockerOpts]

DockerScheduler is a TorchX scheduling interface to Docker.

This is exposed via the scheduler local_docker.

This scheduler runs the provided app via the local docker runtime using the specified images in the AppDef. Docker must be installed and running. This provides the closest environment to schedulers that natively use Docker such as Kubernetes.

Note

docker doesn’t provide gang scheduling mechanisms. If one replica in a job fails, only that replica will be restarted.

Config Options

    usage:
        [copy_env=COPY_ENV],[env=ENV],[privileged=PRIVILEGED],[image_repo=IMAGE_REPO],[quiet=QUIET]

    optional arguments:
        copy_env=COPY_ENV (typing.List[str], None)
            list of glob patterns of environment variables to copy if not set in AppDef. Ex: FOO_*
        env=ENV (typing.Dict[str, str], None)
            environment variables to be passed to the run. The separator sign can be eiher comma or semicolon
            (e.g. ENV1:v1,ENV2:v2,ENV3:v3 or ENV1:V1;ENV2:V2). Environment variables from env will be applied on top
            of the ones from copy_env
        privileged=PRIVILEGED (bool, False)
            If true runs the container with elevated permissions. Equivalent to running with `docker run --privileged`.
        image_repo=IMAGE_REPO (str, None)
            (remote jobs) the image repository to use when pushing patched images, must have push access. Ex: example.com/your/container
        quiet=QUIET (bool, False)
            whether to suppress verbose output for image building. Defaults to ``False``.

Mounts

This class supports bind mounting directories and named volumes.

• bind mount: type=bind,src=<host path>,dst=<container path>[,readonly]

• named volume: type=volume,src=<name>,dst=<container path>[,readonly]

• devices: type=device,src=<name>[,dst=<container path>][,permissions=rwm]

See torchx.specs.parse_mounts() for more info.

Feature	Scheduler Support
Fetch Logs	✔️
Distributed Jobs	✔️
Cancel Job	✔️
Describe Job	Partial support. DockerScheduler will return job and replica status but does not provide the complete original AppSpec.
Workspaces / Patching	✔️
Mounts	✔️
Elasticity	❌

describe(app_id: str) → Optional[DescribeAppResponse]

Describes the specified application.

Returns:

AppDef description or None if the app does not exist.

list() → List[ListAppResponse]

For apps launched on the scheduler, this API returns a list of ListAppResponse objects each of which have app id and its status. Note: This API is in prototype phase and is subject to change.

log_iter(app_id: str, role_name: str, k: int = 0, regex: Optional[str] = None, since: Optional[datetime] = None, until: Optional[datetime] = None, should_tail: bool = False, streams: Optional[Stream] = None) → Iterable[str]

Returns an iterator to the log lines of the k``th replica of the ``role. The iterator ends when all qualifying log lines have been read.

If the scheduler supports time-based cursors fetching log lines for custom time ranges, then the since, until fields are honored, otherwise they are ignored. Not specifying since and until is equivalent to getting all available log lines. If the until is empty, then the iterator behaves like tail -f, following the log output until the job reaches a terminal state.

The exact definition of what constitutes a log is scheduler specific. Some schedulers may consider stderr or stdout as the log, others may read the logs from a log file.

Behaviors and assumptions:

1. Produces an undefined-behavior if called on an app that does not exist The caller should check that the app exists using exists(app_id) prior to calling this method.

2. Is not stateful, calling this method twice with same parameters returns a new iterator. Prior iteration progress is lost.

3. Does not always support log-tailing. Not all schedulers support live log iteration (e.g. tailing logs while the app is running). Refer to the specific scheduler’s documentation for the iterator’s behavior.

3.1 If the scheduler supports log-tailing, it should be controlled

by should_tail parameter.

4. Does not guarantee log retention. It is possible that by the time this method is called, the underlying scheduler may have purged the log records for this application. If so this method raises an arbitrary exception.

5. If should_tail is True, the method only raises a StopIteration exception when the accessible log lines have been fully exhausted and the app has reached a final state. For instance, if the app gets stuck and does not produce any log lines, then the iterator blocks until the app eventually gets killed (either via timeout or manually) at which point it raises a StopIteration.

   If should_tail is False, the method raises StopIteration when there are no more logs.

6. Need not be supported by all schedulers.

7. Some schedulers may support line cursors by supporting __getitem__ (e.g. iter[50] seeks to the 50th log line).

8. Whitespace is preserved, each new line should include \n. To

   support interactive progress bars the returned lines don’t need to include \n but should then be printed without a newline to correctly handle \r carriage returns.

Parameters:

streams – The IO output streams to select. One of: combined, stdout, stderr. If the selected stream isn’t supported by the scheduler it will throw an ValueError.

Returns:

An Iterator over log lines of the specified role replica

Raises:

NotImplementedError – if the scheduler does not support log iteration

schedule(dryrun_info: AppDryRunInfo[DockerJob]) → str

Same as submit except that it takes an AppDryRunInfo. Implementers are encouraged to implement this method rather than directly implementing submit since submit can be trivially implemented by:

dryrun_info = self.submit_dryrun(app, cfg)
return schedule(dryrun_info)

class torchx.schedulers.docker_scheduler.DockerJob(app_id: str, containers: List[torchx.schedulers.docker_scheduler.DockerContainer])

Reference

torchx.schedulers.docker_scheduler.create_scheduler(session_name: str, **kwargs: Any) → DockerScheduler

class torchx.schedulers.docker_scheduler.DockerContainer(image: str, command: List[str], kwargs: Dict[str, object])

torchx.schedulers.docker_scheduler.has_docker() → bool