KServeClient¶

KServeClient(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 KServe in another remote cluster, user must set `config_file` to load kube-config file explicitly, e.g. `KServeClient(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 KServeClient are as following:

Class	Method	Description
KServeClient	set_credentials	Set Credentials
KServeClient	create	Create InferenceService
KServeClient	get	Get or watch the specified InferenceService or all InferenceServices in the namespace
KServeClient	patch	Patch the specified InferenceService
KServeClient	replace	Replace the specified InferenceService
KServeClient	delete	Delete the specified InferenceService
KServeClient	wait_isvc_ready	Wait for the InferenceService to be ready
KServeClient	is_isvc_ready	Check if the InferenceService is ready

set_credentials¶

set_credentials(storage_type, namespace=None, credentials_file=None, service_account='kfserving-service-credentials', **kwargs):

Create or update a Secret and Service Account for GCS and S3 for the provided credentials. Once the Service Account is applied, it may be used in the Service Account field of a InferenceService's V1beta1ModelSpec.

Example¶

Example for creating GCP credentials.

from kserve import KServeClient

kserve = KServeClient()
kserve.set_credentials(storage_type='GCS',
                          namespace='kubeflow',
                          credentials_file='/tmp/gcp.json',
                          service_account='user_specified_sa_name')

The API supports specifying a Service Account by service_account, or using default Service Account kfserving-service-credentials, if the Service Account does not exist, the API will create it and attach the created secret with the Service Account, if exists, only patch it to attach the created Secret.

Example for creating S3 credentials.

from kserve import KServeClient

kserve = KServeClient()
kserve.set_credentials(storage_type='S3',
                          namespace='kubeflow',
                          credentials_file='/tmp/awcredentials',
                          s3_profile='default',
                          s3_endpoint='s3.us-west-amazonaws.com',
                          s3_region='us-west-2',
                          s3_use_https='1',
                          s3_verify_ssl='0')

Example for creating Azure credentials.

from kserve import KServeClient

kserve = KServeClient()
kserve.set_credentials(storage_type='Azure',
                          namespace='kubeflow',
                          credentials_file='/path/azure_credentials.json')

The created or patched Secret and Service Account will be shown as following:

INFO:kfserving.api.set_credentials:Created Secret: kfserving-secret-6tv6l in namespace kubeflow
INFO:kfserving.api.set_credentials:Created (or Patched) Service account: kfserving-service-credentials in namespace kubeflow

Parameters¶

Name	Type	Storage Type	Description
storage_type	str	All	Required. Valid values: GCS, S3 or Azure
namespace	str	All	Optional. The kubernetes namespace. Defaults to current or default namespace.
credentials_file	str	All	Optional. The path for the credentials file. The default file for GCS is `~/.config/gcloud/application_default_credentials.json`, see the instructions on creating the GCS credentials file. For S3 is `~/.aws/credentials`, see the instructions on creating the S3 credentials file. For Azure is `~/.azure/azure_credentials.json`, see the instructions on creating the Azure credentials file.
service_account	str	All	Optional. The name of service account. Supports specifying the `service_account`, or using default Service Account `kfserving-service-credentials`. If the Service Account does not exist, the API will create it and attach the created Secret with the Service Account, if exists, only patch it to attach the created Secret.
s3_endpoint	str	S3 only	Optional. The S3 endpoint.
s3_region	str	S3 only	Optional. The S3 region By default, regional endpoint is used for S3.
s3_use_https	str	S3 only	Optional. HTTPS is used to access S3 by default, unless `s3_use_https=0`
s3_verify_ssl	str	S3 only	Optional. If HTTPS is used, SSL verification could be disabled with `s3_verify_ssl=0`

create¶

create(inferenceservice, namespace=None, watch=False, timeout_seconds=600)

Create the provided InferenceService in the specified namespace

Example¶

from kubernetes import client

from kserve import KServeClient
from kserve import constants
from kserve import V1beta1PredictorSpec
from kserve import V1beta1TFServingSpec
from kserve import V1beta1InferenceServiceSpec
from kserve import V1beta1InferenceService

default_model_spec = V1beta1InferenceServiceSpec(predictor=V1beta1PredictorSpec(tensorflow=V1beta1TFServingSpec(
    storage_uri='gs://kfserving-samples/models/tensorflow/flowers')))

isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                          kind=constants.KSERVE_KIND,
                          metadata=client.V1ObjectMeta(name='flower-sample', namespace='kserve-models'),
                          spec=default_model_spec)


kserve = KServeClient()
kserve.create(isvc)

# The API also supports watching the created InferenceService status till it's READY.
# kserve.create(isvc, watch=True)

Parameters¶

Name	Type	Description	Notes
inferenceservice	V1beta1InferenceService	InferenceService defination	Required
namespace	str	Namespace for InferenceService deploying to. If the `namespace` is not defined, will align with InferenceService definition, or use current or default namespace if namespace is not specified in InferenceService definition.	Optional
watch	bool	Watch the created InferenceService if `True`, otherwise will return the created InferenceService object. Stop watching if InferenceService reaches the optional specified `timeout_seconds` or once the InferenceService overall status `READY` is `True`.	Optional
timeout_seconds	int	Timeout seconds for watching. Defaults to 600.	Optional

Return type¶

object

get¶

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

Get the created InferenceService in the specified namespace

Example¶

from kserve import KServeClient

kserve = KServeClient()
kserve.get('flower-sample', namespace='kubeflow')

The API also support watching the specified InferenceService or all InferenceService in the namespace.

from kserve import KServeClient

kserve = KServeClient()
kserve.get('flower-sample', namespace='kubeflow', watch=True, timeout_seconds=120)

The outputs will be as following. Stop watching if InferenceService reaches the optional specified timeout_seconds or once the InferenceService overall status READY is True.

NAME                 READY      DEFAULT_TRAFFIC CANARY_TRAFFIC  URL                                               
flower-sample        Unknown                                    http://flower-sample.kubeflow.example.com         
flower-sample        Unknown    90               10             http://flower-sample.kubeflow.example.com         
flower-sample        True       90               10             http://flower-sample.kubeflow.example.com

Parameters¶

Name	Type	Description	Notes
name	str	InferenceService name. If the `name` is not specified, it will get or watch all InferenceServices in the namespace.	Optional.
namespace	str	The InferenceService's namespace. Defaults to current or default namespace.	Optional
watch	bool	Watch the specified InferenceService or all InferenceService in the namespace if `True`, otherwise will return object for the specified InferenceService or all InferenceService in the namespace. Stop watching if InferenceService reaches the optional specified `timeout_seconds` or once the speficed InferenceService overall status `READY` is `True` (Only if the `name` is speficed).	Optional
timeout_seconds	int	Timeout seconds for watching. Defaults to 600.	Optional

Return type¶

object

patch¶

patch(name, inferenceservice, namespace=None, watch=False, timeout_seconds=600)

Patch the created InferenceService 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¶

from kubernetes import client
from kserve import constants
from kserve import V1beta1PredictorSpec
from kserve import V1beta1TFServingSpec
from kserve import V1beta1InferenceServiceSpec
from kserve import V1beta1InferenceService
from kserve import KServeClient

service_name = 'flower-sample'
kserve = KServeClient()

default_model_spec = V1beta1InferenceServiceSpec(predictor=V1beta1PredictorSpec(tensorflow=V1beta1TFServingSpec(
    storage_uri='gs://kfserving-samples/models/tensorflow/flowers')))

isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                   kind=constants.KSERVE_KIND,
                                   metadata=client.V1ObjectMeta(
                                        name=service_name, namespace='kserve-models'),
                                   spec=default_model_spec)

kserve.create(isvc)
kserve.wait_isvc_ready(service_name, namespace='kserve-models')

canary_model_spec = V1beta1InferenceServiceSpec(predictor=V1beta1PredictorSpec(canary_traffic_percent=10,
    tensorflow=V1beta1TFServingSpec(
    storage_uri='gs://kfserving-samples/models/tensorflow/flowers-2')))

isvc = V1beta1InferenceService(api_version= constants.KSERVE_V1BETA1,
                          kind=constants.KSERVE_KIND,
                          metadata=client.V1ObjectMeta(name='flower-sample', namespace='kserve-models'),
                          spec=canary_model_spec)


kserve.patch(service_name, isvc)

# The API also supports watching the patached InferenceService status till it's READY.
# kserve.patch('flower-sample', isvc, watch=True)

Parameters¶

Name	Type	Description	Notes
inferenceservice	V1beta1InferenceService	InferenceService defination	Required
namespace	str	The InferenceService's namespace for patching. If the `namespace` is not defined, will align with InferenceService definition, or use current or default namespace if namespace is not specified in InferenceService definition.	Optional
watch	bool	Watch the patched InferenceService if `True`, otherwise will return the patched InferenceService object. Stop watching if InferenceService reaches the optional specified `timeout_seconds` or once the InferenceService overall status `READY` is `True`.	Optional
timeout_seconds	int	Timeout seconds for watching. Defaults to 600.	Optional

Return type¶

object

replace¶

replace(name, inferenceservice, namespace=None, watch=False, timeout_seconds=600)

Replace the created InferenceService in the specified namespace. Generally use the replace API to update whole InferenceService or remove a field such as canary or other components of the InferenceService.

Example¶

from kubernetes import client
from kserve import constants
from kserve import V1beta1PredictorSpec
from kserve import V1beta1TFServingSpec
from kserve import V1beta1InferenceServiceSpec
from kserve import V1beta1InferenceService
from kserve import KServeClient

service_name = 'flower-sample'
kserve = KServeClient()

default_model_spec = V1beta1InferenceServiceSpec(predictor=V1beta1PredictorSpec(tensorflow=V1beta1TFServingSpec(
    storage_uri='gs://kfserving-samples/models/tensorflow/flowers')))

isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                   kind=constants.KSERVE_KIND,
                                   metadata=client.V1ObjectMeta(
                                        name=service_name, namespace='kserve-models'),
                                   spec=default_model_spec)

kserve.create(isvc)
kserve.wait_isvc_ready(service_name, namespace='kserve-models')

canary_model_spec = V1beta1InferenceServiceSpec(predictor=V1beta1PredictorSpec(canary_traffic_percent=0,
    tensorflow=V1beta1TFServingSpec(
    storage_uri='gs://kfserving-samples/models/tensorflow/flowers-2')))

isvc = V1beta1InferenceService(api_version= constants.KSERVE_V1BETA1,
                          kind=constants.KSERVE_KIND,
                          metadata=client.V1ObjectMeta(name=service_name, namespace='kserve-models'),
                          spec=canary_model_spec)

kserve.replace(service_name, isvc)

# The API also supports watching the replaced InferenceService status till it's READY.
# kserve.replace('flower-sample', isvc, watch=True)

Parameters¶

Name	Type	Description	Notes
inferenceservice	V1beta1InferenceService	InferenceService defination	Required
namespace	str	The InferenceService's namespace. If the `namespace` is not defined, will align with InferenceService definition, or use current or default namespace if namespace is not specified in InferenceService definition.	Optional
watch	bool	Watch the patched InferenceService if `True`, otherwise will return the replaced InferenceService object. Stop watching if InferenceService reaches the optional specified `timeout_seconds` or once the InferenceService overall status `READY` is `True`.	Optional
timeout_seconds	int	Timeout seconds for watching. Defaults to 600.	Optional

Return type¶

object

delete¶

delete(name, namespace=None)

Delete the created InferenceService in the specified namespace

Example¶

from kserve import KServeClient

kserve = KServeClient()
kserve.delete('flower-sample', namespace='kubeflow')

Parameters¶

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

Return type¶

object

wait_isvc_ready¶

wait_isvc_ready(name, namespace=None, watch=False, timeout_seconds=600, polling_interval=10):

Wait for the InferenceService to be ready.

Example¶

from kserve import KServeClient

kserve = KServeClient()
kserve.wait_isvc_ready('flower-sample', namespace='kubeflow')

Parameters¶

Name	Type	Description	Notes
name	str	The InferenceService name.
namespace	str	The InferenceService namespace. Defaults to current or default namespace.	Optional
watch	bool	Watch the specified InferenceService if `True`.	Optional
timeout_seconds	int	How long to wait for the InferenceService, default wait for 600 seconds.	Optional
polling_interval	int	How often to poll for the status of the InferenceService.	Optional

Return type¶

object

is_isvc_ready¶

is_isvc_ready(name, namespace=None)

Returns True if the InferenceService is ready; false otherwise.

Example¶

from kserve import KServeClient

kserve = KServeClient()
kserve.is_isvc_ready('flower-sample', namespace='kubeflow')

Parameters¶

Name	Type	Description	Notes
name	str	The InferenceService name.
namespace	str	The InferenceService namespace. Defaults to current or default namespace.	Optional

Return type¶

Bool