GCP Batch

This contains the TorchX GCP Batch scheduler which can be used to run TorchX components directly on GCP Batch.

This scheduler is in prototype stage and may change without notice.

Prerequisites

You need to have a GCP project configured to use Batch by enabling and setting it up. See https://cloud.google.com/batch/docs/get-started#prerequisites

class torchx.schedulers.gcp_batch_scheduler.GCPBatchScheduler(session_name: str, client: Optional[Any] = None)

Bases: Scheduler[GCPBatchOpts]

GCPBatchScheduler is a TorchX scheduling interface to GCP Batch.

$ pip install torchx[gcp_batch]
$ torchx run --scheduler gcp_batch utils.echo --msg hello
# This launches a job with app handle like gcp_batch://torchx/project:location:app_id1234 and prints it
$ torchx status gcp_batch://torchx/project:location:app_id1234
...

Authentication is loaded from the environment using the gcloud credential handling.

Config Options

    usage:
        [project=PROJECT],[location=LOCATION]

    optional arguments:
        project=PROJECT (str, None)
            Name of the GCP project. Defaults to the configured GCP project in the environment
        location=LOCATION (str, us-central1)
            Name of the location to schedule the job in. Defaults to us-central1

Compatibility

Feature	Scheduler Support
Fetch Logs	✔️
Distributed Jobs	✔️
Cancel Job	✔️
Describe Job	✔️
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

run_opts() → runopts

Returns the run configuration options expected by the scheduler. Basically a --help for the run API.

schedule(dryrun_info: AppDryRunInfo[GCPBatchJob]) → 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.gcp_batch_scheduler.GCPBatchJob(name: str, project: str, location: str, job_def: 'batch_v1.Job')

Reference

torchx.schedulers.gcp_batch_scheduler.create_scheduler(session_name: str, client: Optional[Any] = None, **kwargs: object) → GCPBatchScheduler