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Kubernetes

This contains the TorchX Kubernetes scheduler which can be used to run TorchX components on a Kubernetes cluster.

Prerequisites

TorchX kubernetes scheduler depends on volcano and requires etcd intalled for distributed job execution.

Install volcano 1.4.0 version

kubectl apply -f https://raw.githubusercontent.com/volcano-sh/volcano/v1.4.0/installer/volcano-development.yaml

TorchX uses torch.distributed.run to run distributed training. This requires the installation of etcd service on your kubernetes cluster:

kubectl apply -f https://github.com/pytorch/torchx/blob/main/resources/etcd.yaml

Learn more about running distributed trainers torchx.components.dist

class torchx.schedulers.kubernetes_scheduler.KubernetesScheduler(session_name: str, client: Optional[ApiClient] = None)[source]

KubernetesScheduler is a TorchX scheduling interface to Kubernetes.

Important: Volcano is required to be installed on the Kubernetes cluster. TorchX requires gang scheduling for multi-replica/multi-role execution and Volcano is currently the only supported scheduler with Kubernetes. For installation instructions see: https://github.com/volcano-sh/volcano

This has been confirmed to work with Volcano v1.3.0 and Kubernetes versions v1.18-1.21. See https://github.com/pytorch/torchx/issues/120 which is tracking Volcano support for Kubernetes v1.22.

Note

AppDefs that have more than 0 retries may not be displayed as pods if they failed. This occurs due to known bug in Volcano(as per 1.4.0 release): https://github.com/volcano-sh/volcano/issues/1651

$ pip install torchx[kubernetes]
$ torchx run --scheduler kubernetes --scheduler_args namespace=default,queue=test utils.echo --image alpine:latest --msg hello
kubernetes://torchx_user/1234
$ torchx status kubernetes://torchx_user/1234
...

Feature

Scheduler Support

Fetch Logs

✔️

Distributed Jobs

✔️

Cancel Job

✔️

Describe Job

Partial support. KubernetesScheduler will return job and replica status but does not provide the complete original AppSpec.

describe(app_id: str)Optional[torchx.schedulers.api.DescribeAppResponse][source]

Describes the specified application.

Returns

AppDef description or None if the app does not exist.

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

Returns an iterator to the log lines of the k``th replica of the ``role. The iterator ends end 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.

  1. 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.

  2. 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.

  3. Need not be supported by all schedulers.

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

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()torchx.specs.api.runopts[source]

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

schedule(dryrun_info: torchx.specs.api.AppDryRunInfo[torchx.schedulers.kubernetes_scheduler.KubernetesJob])str[source]

Same as submit except that it takes an AppDryRunInfo. Implementors 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)

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