trainer/sdk/python/docs/TFJobClient.md

TFJobClient

TFJobClient(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:

parameter	Description
config_file	Name of the kube-config file. Defaults to ~/.kube/config. Note that for the case that the SDK is running in cluster and you want to operate tfjob in another remote cluster, user must set config_file to load kube-config file explicitly, e.g. TFJobClient(config_file="~/.kube/config").
context	Set the active context. If is set to None, current_context from config file will be used.
client_configuration	The kubernetes.client.Configuration to set configs to.
persist_config	If True, config file will be updated when changed (e.g GCP token refresh).

The APIs for TFJobClient are as following:

Class	Method	Description
TFJobClient	create	Create TFJob
TFJobClient	get	Get the specified TFJob or all TFJob in the namespace
TFJobClient	patch	Patch the specified TFJob
TFJobClient	delete	Delete the specified TFJob
TFJobClient	wait_for_job	Wait for the specified job to finish
TFJobClient	wait_for_condition	Waits until any of the specified conditions occur
TFJobClient	get_job_status	Get the TFJob status
TFJobClient	if_job_running	Check if the TFJob running
TFJobClient	if_job_succeeded	Check if the TFJob Succeeded

create

create(tfjob, namespace=None)

Create the provided tfjob 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 kubeflow.tfjob import constants
from kubeflow.tfjob import utils
from kubeflow.tfjob import V1ReplicaSpec
from kubeflow.tfjob import V1TFJob
from kubeflow.tfjob import V1TFJobList
from kubeflow.tfjob import V1TFJobSpec
from kubeflow.tfjob import TFJobClient


container = V1Container(
    name="tensorflow",
    image="gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0",
    command=[
        "python",
        "/var/tf_mnist/mnist_with_summaries.py",
        "--log_dir=/train/logs", "--learning_rate=0.01",
        "--batch_size=150"
        ]
)

worker = V1ReplicaSpec(
    replicas=1,
    restart_policy="Never",
    template=V1PodTemplateSpec(
        spec=V1PodSpec(
            containers=[container]
        )
    )
)

tfjob = V1TFJob(
    api_version="kubeflow.org/v1",
    kind="TFJob",
    metadata=V1ObjectMeta(name="mnist",namespace=namespace),
    spec=V1TFJobSpec(
        clean_pod_policy="None",
        tf_replica_specs={"Worker": worker}
    )
)


tfjob_client = TFJobClient()
tfjob_client.create(tfjob)

Parameters

Name	Type	Description	Notes
tfjob	V1TFJob	tfjob defination	Required
namespace	str	Namespace for tfjob deploying to. If the namespace is not defined, will align with tfjob definition, or use current or default namespace if namespace is not specified in tfjob definition.	Optional

Return type

object

get

get(name=None, namespace=None)

Get the created tfjob in the specified namespace

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.get('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name. If the name is not specified, it will get all tfjobs in the namespace.	Optional.
namespace	str	The tfjob's namespace. Defaults to current or default namespace.	Optional

Return type

object

patch

patch(name, tfjob, namespace=None)

Patch the created tfjob 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

tfjob = V1TFJob(
    api_version="kubeflow.org/v1",
    ... #update something in TFJob spec
)

tfjob_client = TFJobClient()
tfjob_client.patch('mnist', isvc)

Parameters

Name	Type	Description	Notes
tfjob	V1TFJob	tfjob defination	Required
namespace	str	The tfjob's namespace for patching. If the namespace is not defined, will align with tfjob definition, or use current or default namespace if namespace is not specified in tfjob definition.	Optional

Return type

object

delete

delete(name, namespace=None)

Delete the created tfjob in the specified namespace

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.delete('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
namespace	str	The tfjob's namespace. Defaults to current or default namespace.	Optional

Return type

object

wait_for_job

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

Wait for the specified job to finish.

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.wait_for_job('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
namespace	str	The tfjob'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 tfjob.	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.tfjob import TFJobClient

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

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
expected_condition	List	A list of conditions. Function waits until any of the supplied conditions is reached.
namespace	str	The tfjob'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 tfjob.	Optional

Return type

object

get_job_status

get_job_status(name, namespace=None)

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

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.get_job_status('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
namespace	str	The tfjob's namespace. Defaults to current or default namespace.	Optional

Return type

Str

if_job_running

if_job_running(name, namespace=None)

Returns True if the TFJob running; false otherwise.

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.if_job_running('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
namespace	str	The tfjob's namespace. Defaults to current or default namespace.	Optional

Return type

Bool

if_job_succeeded

if_job_succeeded(name, namespace=None)

Returns True if the TFJob succeeded; false otherwise.

Example

from kubeflow.tfjob import TFJobClient

tfjob_client = TFJobClient()
tfjob_client.if_job_succeeded('mnist', namespace='kubeflow')

Parameters

Name	Type	Description	Notes
name	str	The TFJob name.
namespace	str	The tfjob's namespace. Defaults to current or default namespace.	Optional

Return type

Bool