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• PyTorchJobClient

PyTorchJobClient

PyTorchJobClient(config_file=None, context=None, client_configuration=None, persist_config=True)

User can loads authentication and cluster information from kube-config file and stores them in kubernetes.client.configuration. Parameters are as following:

The APIs for PyTorchJobClient are as following:

create

create(pytorchjob, namespace=None)

Create the provided pytorchjob in the specified namespace

Example

from kubernetes.client import V1PodTemplateSpec
from kubernetes.client import V1ObjectMeta
from kubernetes.client import V1PodSpec
from kubernetes.client import V1Container
from kubernetes.client import V1ResourceRequirements

from kubeflow.training import constants
from kubeflow.training import utils
from kubeflow.training import V1ReplicaSpec
from kubeflow.training import KubeflowOrgV1PyTorchJob
from kubeflow.training import KubeflowOrgV1PyTorchJobSpec
from kubeflow.training import PyTorchJobClient

  container = V1Container(
    name="pytorch",
    image="gcr.io/kubeflow-ci/pytorch-dist-mnist-test:v1.0",
    args=["--backend","gloo"],
  )

  master = V1ReplicaSpec(
    replicas=1,
    restart_policy="OnFailure",
    template=V1PodTemplateSpec(
      spec=V1PodSpec(
        containers=[container]
      )
    )
  )

  worker = V1ReplicaSpec(
    replicas=1,
    restart_policy="OnFailure",
    template=V1PodTemplateSpec(
      spec=V1PodSpec(
        containers=[container]
        )
    )
  )

  pytorchjob = KubeflowOrgV1PyTorchJob(
    api_version="kubeflow.org/v1",
    kind="PyTorchJob",
    metadata=V1ObjectMeta(name="mnist", namespace='default'),
    spec=KubeflowOrgV1PyTorchJobSpec(
      clean_pod_policy="None",
      pytorch_replica_specs={"Master": master,
                             "Worker": worker}
    )
  )

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.create(pytorchjob)

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- pytorchjob | KubeflowOrgV1PyTorchJob | pytorchjob defination| Required | namespace | str | Namespace for pytorchjob deploying to. If the namespace is not defined, will align with pytorchjob definition, or use current or default namespace if namespace is not specified in pytorchjob definition. | Optional |

Return type

object

get

get(name=None, namespace=None, watch=False, timeout_seconds=600)

Get the created pytorchjob in the specified namespace

Example

from kubeflow.training import pytorchjobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.get('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | pytorchjob name. If the name is not specified, it will get all pytorchjobs in the namespace.| Optional. | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional | watch | bool | Watch the created pytorchjob if True, otherwise will return the created pytorchjob object. Stop watching if pytorchjob reaches the optional specified timeout_seconds or once the PyTorchJob status Succeeded or Failed. | Optional | timeout_seconds | int | Timeout seconds for watching. Defaults to 600. | Optional |

Return type

object

patch

patch(name, pytorchjob, namespace=None)

Patch the created pytorchjob in the specified namespace.

Note that if you want to set the field from existing value to None, patch API may not work, you need to use replace API to remove the field value.

Example

pytorchjob = KubeflowOrgV1PyTorchJob(
    api_version="kubeflow.org/v1",
    ... #update something in PyTorchJob spec
)

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.patch('mnist', isvc)

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- pytorchjob | KubeflowOrgV1PyTorchJob | pytorchjob defination| Required | namespace | str | The pytorchjob’s namespace for patching. If the namespace is not defined, will align with pytorchjob definition, or use current or default namespace if namespace is not specified in pytorchjob definition. | Optional|

Return type

object

delete

delete(name, namespace=None)

Delete the created pytorchjob in the specified namespace

Example

from kubeflow.training import pytorchjobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.delete('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | pytorchjob name| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace. | Optional|

Return type

object

wait_for_job

wait_for_job(name, namespace=None, watch=False, timeout_seconds=600, polling_interval=30, status_callback=None):

Wait for the specified job to finish.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.wait_for_job('mnist', namespace='kubeflow')

# The API also supports watching the PyTorchJob status till it's Succeeded or Failed.
pytorchjob_client.wait_for_job('mnist', namespace='kubeflow', watch=True)
NAME                           STATE                TIME
pytorch-dist-mnist-gloo        Created              2020-01-02T09:21:22Z
pytorch-dist-mnist-gloo        Running              2020-01-02T09:21:36Z
pytorch-dist-mnist-gloo        Running              2020-01-02T09:21:36Z
pytorch-dist-mnist-gloo        Running              2020-01-02T09:21:36Z
pytorch-dist-mnist-gloo        Running              2020-01-02T09:21:36Z
pytorch-dist-mnist-gloo        Succeeded            2020-01-02T09:26:38Z

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace. | Optional| watch | bool | Watch the PyTorchJob if True. Stop watching if PyTorchJob reaches the optional specified timeout_seconds or once the PyTorchJob status Succeeded or Failed. | Optional | timeout_seconds | int | How long to wait for the job, default wait for 600 seconds. | Optional| polling_interval | int | How often to poll for the status of the job.| Optional| status_callback | str | Callable. If supplied this callable is invoked after we poll the job. Callable takes a single argument which is the pytorchjob.| Optional|

Return type

object

wait_for_condition

wait_for_condition(name, expected_condition, namespace=None, timeout_seconds=600, polling_interval=30, status_callback=None):

Waits until any of the specified conditions occur.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.wait_for_condition('mnist', expected_condition=["Succeeded", "Failed"], namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | expected_condition |List |A list of conditions. Function waits until any of the supplied conditions is reached.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace. | Optional| timeout_seconds | int | How long to wait for the job, default wait for 600 seconds. | Optional| polling_interval | int | How often to poll for the status of the job.| Optional| status_callback | str | Callable. If supplied this callable is invoked after we poll the job. Callable takes a single argument which is the pytorchjob.| Optional|

Return type

object

get_job_status

get_job_status(name, namespace=None)

Returns PyTorchJob status, such as Running, Failed or Succeeded.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.get_job_status('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name. | | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional |

Return type

Str

is_job_running

is_job_running(name, namespace=None)

Returns True if the PyTorchJob running; false otherwise.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.is_job_running('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional |

Return type

Bool

is_job_succeeded

is_job_succeeded(name, namespace=None)

Returns True if the PyTorchJob succeeded; false otherwise.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.is_job_succeeded('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional |

Return type

Bool

get_pod_names

get_pod_names(name, namespace=None, master=False, replica_type=None, replica_index=None)

Get pod names of the PyTorchJob.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.get_pod_names('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional | master | bool | Only get pod with label ‘job-role: master’ pod if True. | | replica_type | str | User can specify one of ‘master, worker’ to only get one type pods. By default get all type pods.| | replica_index | str | User can specfy replica index to get one pod of the PyTorchJob. | |

Return type

Set

get_logs

get_logs(name, namespace=None, master=True, replica_type=None, replica_index=None, follow=False)

Get training logs of the PyTorchJob. By default only get the logs of Pod that has labels ‘job-role: master’, to get all pods logs, specfy the master=False.

Example

from kubeflow.training import PyTorchJobClient

pytorchjob_client = PyTorchJobClient()
pytorchjob_client.get_logs('mnist', namespace='kubeflow')

Parameters

Name | Type | Description | Notes ———— | ————- | ————- | ————- name | str | The PyTorchJob name.| | namespace | str | The pytorchjob’s namespace. Defaults to current or default namespace.| Optional | master | bool | Only get pod with label ‘job-role: master’ pod if True. | | replica_type | str | User can specify one of ‘master, worker’ to only get one type pods. By default get all type pods.| | replica_index | str | User can specfy replica index to get one pod of the PyTorchJob. | | follow | bool | Follow the log stream of the pod. Defaults to false. | |

Return type

Str