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JobServiceAsyncClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.aiplatform_v1beta1.services.job_service.transports.base.JobServiceTransport, typing.Callable[[...], google.cloud.aiplatform_v1beta1.services.job_service.transports.base.JobServiceTransport]]] = 'grpc_asyncio', client_options: typing.Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
A service for creating and managing Vertex AI's jobs.
Properties
api_endpoint
Return the API endpoint used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The API endpoint used by the client instance. |
transport
Returns the transport used by the client instance.
Returns | |
---|---|
Type | Description |
JobServiceTransport |
The transport used by the client instance. |
universe_domain
Return the universe domain used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The universe domain used by the client instance. |
Methods
JobServiceAsyncClient
JobServiceAsyncClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.aiplatform_v1beta1.services.job_service.transports.base.JobServiceTransport, typing.Callable[[...], google.cloud.aiplatform_v1beta1.services.job_service.transports.base.JobServiceTransport]]] = 'grpc_asyncio', client_options: typing.Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
Instantiates the job service async client.
Parameters | |
---|---|
Name | Description |
credentials |
Optional[google.auth.credentials.Credentials]
The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. |
transport |
Optional[Union[str,JobServiceTransport,Callable[..., JobServiceTransport]]]
The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the JobServiceTransport constructor. If set to None, a transport is chosen automatically. NOTE: "rest" transport functionality is currently in a beta state (preview). We welcome your feedback via an issue in this library's source repository. |
client_options |
Optional[Union[google.api_core.client_options.ClientOptions, dict]]
Custom options for the client. 1. The |
client_info |
google.api_core.gapic_v1.client_info.ClientInfo
The client info used to send a user-agent string along with API requests. If |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTlsChannelError |
If mutual TLS transport creation failed for any reason. |
batch_prediction_job_path
batch_prediction_job_path(
project: str, location: str, batch_prediction_job: str
) -> str
Returns a fully-qualified batch_prediction_job string.
cancel_batch_prediction_job
cancel_batch_prediction_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CancelBatchPredictionJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Cancels a BatchPredictionJob.
Starts asynchronous cancellation on the BatchPredictionJob. The
server makes the best effort to cancel the job, but success is
not guaranteed. Clients can use
xref_JobService.GetBatchPredictionJob
or other methods to check whether the cancellation succeeded or
whether the job completed despite cancellation. On a successful
cancellation, the BatchPredictionJob is not deleted;instead its
xref_BatchPredictionJob.state
is set to CANCELLED
. Any files already outputted by the job
are not deleted.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_cancel_batch_prediction_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CancelBatchPredictionJobRequest(
name="name_value",
)
# Make the request
await client.cancel_batch_prediction_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CancelBatchPredictionJobRequest, dict]]
The request object. Request message for JobService.CancelBatchPredictionJob. |
name |
Required. The name of the BatchPredictionJob to cancel. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
cancel_custom_job
cancel_custom_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CancelCustomJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Cancels a CustomJob. Starts asynchronous cancellation on the
CustomJob. The server makes a best effort to cancel the job, but
success is not guaranteed. Clients can use
xref_JobService.GetCustomJob
or other methods to check whether the cancellation succeeded or
whether the job completed despite cancellation. On successful
cancellation, the CustomJob is not deleted; instead it becomes a
job with a
xref_CustomJob.error
value with a google.rpc.Status.code][google.rpc.Status.code]
of
1, corresponding to Code.CANCELLED
, and
xref_CustomJob.state
is set to CANCELLED
.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_cancel_custom_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CancelCustomJobRequest(
name="name_value",
)
# Make the request
await client.cancel_custom_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CancelCustomJobRequest, dict]]
The request object. Request message for JobService.CancelCustomJob. |
name |
Required. The name of the CustomJob to cancel. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
cancel_data_labeling_job
cancel_data_labeling_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CancelDataLabelingJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Cancels a DataLabelingJob. Success of cancellation is not guaranteed.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_cancel_data_labeling_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CancelDataLabelingJobRequest(
name="name_value",
)
# Make the request
await client.cancel_data_labeling_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CancelDataLabelingJobRequest, dict]]
The request object. Request message for JobService.CancelDataLabelingJob. |
name |
Required. The name of the DataLabelingJob. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
cancel_hyperparameter_tuning_job
cancel_hyperparameter_tuning_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CancelHyperparameterTuningJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Cancels a HyperparameterTuningJob. Starts asynchronous
cancellation on the HyperparameterTuningJob. The server makes a
best effort to cancel the job, but success is not guaranteed.
Clients can use
xref_JobService.GetHyperparameterTuningJob
or other methods to check whether the cancellation succeeded or
whether the job completed despite cancellation. On successful
cancellation, the HyperparameterTuningJob is not deleted;
instead it becomes a job with a
xref_HyperparameterTuningJob.error
value with a google.rpc.Status.code][google.rpc.Status.code]
of
1, corresponding to Code.CANCELLED
, and
xref_HyperparameterTuningJob.state
is set to CANCELLED
.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_cancel_hyperparameter_tuning_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CancelHyperparameterTuningJobRequest(
name="name_value",
)
# Make the request
await client.cancel_hyperparameter_tuning_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CancelHyperparameterTuningJobRequest, dict]]
The request object. Request message for JobService.CancelHyperparameterTuningJob. |
name |
Required. The name of the HyperparameterTuningJob to cancel. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
cancel_nas_job
cancel_nas_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CancelNasJobRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Cancels a NasJob. Starts asynchronous cancellation on the
NasJob. The server makes a best effort to cancel the job, but
success is not guaranteed. Clients can use
xref_JobService.GetNasJob
or other methods to check whether the cancellation succeeded or
whether the job completed despite cancellation. On successful
cancellation, the NasJob is not deleted; instead it becomes a
job with a
xref_NasJob.error
value with a google.rpc.Status.code][google.rpc.Status.code]
of
1, corresponding to Code.CANCELLED
, and
xref_NasJob.state is
set to CANCELLED
.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_cancel_nas_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CancelNasJobRequest(
name="name_value",
)
# Make the request
await client.cancel_nas_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CancelNasJobRequest, dict]]
The request object. Request message for JobService.CancelNasJob. |
name |
Required. The name of the NasJob to cancel. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
cancel_operation
cancel_operation(
request: typing.Optional[
google.longrunning.operations_pb2.CancelOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Starts asynchronous cancellation on a long-running operation.
The server makes a best effort to cancel the operation, but success
is not guaranteed. If the server doesn't support this method, it returns
google.rpc.Code.UNIMPLEMENTED
.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
common_billing_account_path
common_billing_account_path(billing_account: str) -> str
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str) -> str
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str) -> str
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str) -> str
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str) -> str
Returns a fully-qualified project string.
context_path
context_path(project: str, location: str, metadata_store: str, context: str) -> str
Returns a fully-qualified context string.
create_batch_prediction_job
create_batch_prediction_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateBatchPredictionJobRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
batch_prediction_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.batch_prediction_job.BatchPredictionJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.batch_prediction_job.BatchPredictionJob
Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_batch_prediction_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
batch_prediction_job = aiplatform_v1beta1.BatchPredictionJob()
batch_prediction_job.display_name = "display_name_value"
batch_prediction_job.input_config.gcs_source.uris = ['uris_value1', 'uris_value2']
batch_prediction_job.input_config.instances_format = "instances_format_value"
batch_prediction_job.output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
batch_prediction_job.output_config.predictions_format = "predictions_format_value"
request = aiplatform_v1beta1.CreateBatchPredictionJobRequest(
parent="parent_value",
batch_prediction_job=batch_prediction_job,
)
# Make the request
response = await client.create_batch_prediction_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateBatchPredictionJobRequest, dict]]
The request object. Request message for JobService.CreateBatchPredictionJob. |
parent |
Required. The resource name of the Location to create the BatchPredictionJob in. Format: |
batch_prediction_job |
BatchPredictionJob
Required. The BatchPredictionJob to create. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.BatchPredictionJob |
A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances. |
create_custom_job
create_custom_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateCustomJobRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
custom_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.custom_job.CustomJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.custom_job.CustomJob
Creates a CustomJob. A created CustomJob right away will be attempted to be run.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_custom_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
custom_job = aiplatform_v1beta1.CustomJob()
custom_job.display_name = "display_name_value"
custom_job.job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"
request = aiplatform_v1beta1.CreateCustomJobRequest(
parent="parent_value",
custom_job=custom_job,
)
# Make the request
response = await client.create_custom_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateCustomJobRequest, dict]]
The request object. Request message for JobService.CreateCustomJob. |
parent |
Required. The resource name of the Location to create the CustomJob in. Format: |
custom_job |
CustomJob
Required. The CustomJob to create. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.CustomJob |
Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded). |
create_data_labeling_job
create_data_labeling_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateDataLabelingJobRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
data_labeling_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.data_labeling_job.DataLabelingJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.data_labeling_job.DataLabelingJob
Creates a DataLabelingJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_data_labeling_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
data_labeling_job = aiplatform_v1beta1.DataLabelingJob()
data_labeling_job.display_name = "display_name_value"
data_labeling_job.datasets = ['datasets_value1', 'datasets_value2']
data_labeling_job.labeler_count = 1375
data_labeling_job.instruction_uri = "instruction_uri_value"
data_labeling_job.inputs_schema_uri = "inputs_schema_uri_value"
data_labeling_job.inputs.null_value = "NULL_VALUE"
request = aiplatform_v1beta1.CreateDataLabelingJobRequest(
parent="parent_value",
data_labeling_job=data_labeling_job,
)
# Make the request
response = await client.create_data_labeling_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateDataLabelingJobRequest, dict]]
The request object. Request message for JobService.CreateDataLabelingJob. |
parent |
Required. The parent of the DataLabelingJob. Format: |
data_labeling_job |
DataLabelingJob
Required. The DataLabelingJob to create. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.DataLabelingJob |
DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset: |
create_hyperparameter_tuning_job
create_hyperparameter_tuning_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateHyperparameterTuningJobRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
hyperparameter_tuning_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.hyperparameter_tuning_job.HyperparameterTuningJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.hyperparameter_tuning_job.HyperparameterTuningJob
)
Creates a HyperparameterTuningJob
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_hyperparameter_tuning_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
hyperparameter_tuning_job = aiplatform_v1beta1.HyperparameterTuningJob()
hyperparameter_tuning_job.display_name = "display_name_value"
hyperparameter_tuning_job.study_spec.metrics.metric_id = "metric_id_value"
hyperparameter_tuning_job.study_spec.metrics.goal = "MINIMIZE"
hyperparameter_tuning_job.study_spec.parameters.double_value_spec.min_value = 0.96
hyperparameter_tuning_job.study_spec.parameters.double_value_spec.max_value = 0.962
hyperparameter_tuning_job.study_spec.parameters.parameter_id = "parameter_id_value"
hyperparameter_tuning_job.max_trial_count = 1609
hyperparameter_tuning_job.parallel_trial_count = 2128
hyperparameter_tuning_job.trial_job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"
request = aiplatform_v1beta1.CreateHyperparameterTuningJobRequest(
parent="parent_value",
hyperparameter_tuning_job=hyperparameter_tuning_job,
)
# Make the request
response = await client.create_hyperparameter_tuning_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateHyperparameterTuningJobRequest, dict]]
The request object. Request message for JobService.CreateHyperparameterTuningJob. |
parent |
Required. The resource name of the Location to create the HyperparameterTuningJob in. Format: |
hyperparameter_tuning_job |
HyperparameterTuningJob
Required. The HyperparameterTuningJob to create. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.HyperparameterTuningJob |
Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification. |
create_model_deployment_monitoring_job
create_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
model_deployment_monitoring_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob
)
Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob()
model_deployment_monitoring_job.display_name = "display_name_value"
model_deployment_monitoring_job.endpoint = "endpoint_value"
request = aiplatform_v1beta1.CreateModelDeploymentMonitoringJobRequest(
parent="parent_value",
model_deployment_monitoring_job=model_deployment_monitoring_job,
)
# Make the request
response = await client.create_model_deployment_monitoring_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.CreateModelDeploymentMonitoringJob. |
parent |
Required. The parent of the ModelDeploymentMonitoringJob. Format: |
model_deployment_monitoring_job |
ModelDeploymentMonitoringJob
Required. The ModelDeploymentMonitoringJob to create This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringJob |
Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors. |
create_nas_job
create_nas_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.CreateNasJobRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
nas_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.nas_job.NasJob
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.nas_job.NasJob
Creates a NasJob
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_create_nas_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
nas_job = aiplatform_v1beta1.NasJob()
nas_job.display_name = "display_name_value"
nas_job.nas_job_spec.multi_trial_algorithm_spec.search_trial_spec.search_trial_job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"
nas_job.nas_job_spec.multi_trial_algorithm_spec.search_trial_spec.max_trial_count = 1609
nas_job.nas_job_spec.multi_trial_algorithm_spec.search_trial_spec.max_parallel_trial_count = 2549
request = aiplatform_v1beta1.CreateNasJobRequest(
parent="parent_value",
nas_job=nas_job,
)
# Make the request
response = await client.create_nas_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateNasJobRequest, dict]]
The request object. Request message for JobService.CreateNasJob. |
parent |
Required. The resource name of the Location to create the NasJob in. Format: |
nas_job |
NasJob
Required. The NasJob to create. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.NasJob |
Represents a Neural Architecture Search (NAS) job. |
custom_job_path
custom_job_path(project: str, location: str, custom_job: str) -> str
Returns a fully-qualified custom_job string.
data_labeling_job_path
data_labeling_job_path(project: str, location: str, data_labeling_job: str) -> str
Returns a fully-qualified data_labeling_job string.
dataset_path
dataset_path(project: str, location: str, dataset: str) -> str
Returns a fully-qualified dataset string.
delete_batch_prediction_job
delete_batch_prediction_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteBatchPredictionJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a BatchPredictionJob. Can only be called on jobs that already finished.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_batch_prediction_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteBatchPredictionJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_batch_prediction_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteBatchPredictionJobRequest, dict]]
The request object. Request message for JobService.DeleteBatchPredictionJob. |
name |
Required. The name of the BatchPredictionJob resource to be deleted. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_custom_job
delete_custom_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteCustomJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a CustomJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_custom_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteCustomJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_custom_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteCustomJobRequest, dict]]
The request object. Request message for JobService.DeleteCustomJob. |
name |
Required. The name of the CustomJob resource to be deleted. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_data_labeling_job
delete_data_labeling_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteDataLabelingJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a DataLabelingJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_data_labeling_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteDataLabelingJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_data_labeling_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteDataLabelingJobRequest, dict]]
The request object. Request message for JobService.DeleteDataLabelingJob. |
name |
Required. The name of the DataLabelingJob to be deleted. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_hyperparameter_tuning_job
delete_hyperparameter_tuning_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteHyperparameterTuningJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a HyperparameterTuningJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_hyperparameter_tuning_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteHyperparameterTuningJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_hyperparameter_tuning_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteHyperparameterTuningJobRequest, dict]]
The request object. Request message for JobService.DeleteHyperparameterTuningJob. |
name |
Required. The name of the HyperparameterTuningJob resource to be deleted. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_model_deployment_monitoring_job
delete_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a ModelDeploymentMonitoringJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteModelDeploymentMonitoringJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_model_deployment_monitoring_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.DeleteModelDeploymentMonitoringJob. |
name |
Required. The resource name of the model monitoring job to delete. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_nas_job
delete_nas_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.DeleteNasJobRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Deletes a NasJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_delete_nas_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteNasJobRequest(
name="name_value",
)
# Make the request
operation = client.delete_nas_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteNasJobRequest, dict]]
The request object. Request message for JobService.DeleteNasJob. |
name |
Required. The name of the NasJob resource to be deleted. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_operation
delete_operation(
request: typing.Optional[
google.longrunning.operations_pb2.DeleteOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Deletes a long-running operation.
This method indicates that the client is no longer interested
in the operation result. It does not cancel the operation.
If the server doesn't support this method, it returns
google.rpc.Code.UNIMPLEMENTED
.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
endpoint_path
endpoint_path(project: str, location: str, endpoint: str) -> str
Returns a fully-qualified endpoint string.
from_service_account_file
from_service_account_file(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Parameter | |
---|---|
Name | Description |
filename |
str
The path to the service account private key json file. |
Returns | |
---|---|
Type | Description |
JobServiceAsyncClient |
The constructed client. |
from_service_account_info
from_service_account_info(info: dict, *args, **kwargs)
Creates an instance of this client using the provided credentials info.
Parameter | |
---|---|
Name | Description |
info |
dict
The service account private key info. |
Returns | |
---|---|
Type | Description |
JobServiceAsyncClient |
The constructed client. |
from_service_account_json
from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Parameter | |
---|---|
Name | Description |
filename |
str
The path to the service account private key json file. |
Returns | |
---|---|
Type | Description |
JobServiceAsyncClient |
The constructed client. |
get_batch_prediction_job
get_batch_prediction_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetBatchPredictionJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.batch_prediction_job.BatchPredictionJob
Gets a BatchPredictionJob
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_batch_prediction_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetBatchPredictionJobRequest(
name="name_value",
)
# Make the request
response = await client.get_batch_prediction_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetBatchPredictionJobRequest, dict]]
The request object. Request message for JobService.GetBatchPredictionJob. |
name |
Required. The name of the BatchPredictionJob resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.BatchPredictionJob |
A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances. |
get_custom_job
get_custom_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetCustomJobRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.custom_job.CustomJob
Gets a CustomJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_custom_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetCustomJobRequest(
name="name_value",
)
# Make the request
response = await client.get_custom_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetCustomJobRequest, dict]]
The request object. Request message for JobService.GetCustomJob. |
name |
Required. The name of the CustomJob resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.CustomJob |
Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded). |
get_data_labeling_job
get_data_labeling_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetDataLabelingJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.data_labeling_job.DataLabelingJob
Gets a DataLabelingJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_data_labeling_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetDataLabelingJobRequest(
name="name_value",
)
# Make the request
response = await client.get_data_labeling_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetDataLabelingJobRequest, dict]]
The request object. Request message for JobService.GetDataLabelingJob. |
name |
Required. The name of the DataLabelingJob. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.DataLabelingJob |
DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset: |
get_hyperparameter_tuning_job
get_hyperparameter_tuning_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetHyperparameterTuningJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.hyperparameter_tuning_job.HyperparameterTuningJob
)
Gets a HyperparameterTuningJob
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_hyperparameter_tuning_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetHyperparameterTuningJobRequest(
name="name_value",
)
# Make the request
response = await client.get_hyperparameter_tuning_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetHyperparameterTuningJobRequest, dict]]
The request object. Request message for JobService.GetHyperparameterTuningJob. |
name |
Required. The name of the HyperparameterTuningJob resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.HyperparameterTuningJob |
Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification. |
get_iam_policy
get_iam_policy(
request: typing.Optional[google.iam.v1.iam_policy_pb2.GetIamPolicyRequest] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy
Gets the IAM access control policy for a function.
Returns an empty policy if the function exists and does not have a policy set.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings . A binding binds one or more members to a single role . Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition , which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01t00:00:00.000z')",="" }="" }="" ]="" }="" **yaml="" example**="" ::="" bindings:="" -="" members:="" -="" user:mike@example.com="" -="" group:admins@example.com="" -="" domain:google.com="" -="" serviceaccount:my-project-id@appspot.gserviceaccount.com="" role:="" roles/resourcemanager.organizationadmin="" -="" members:="" -="" user:eve@example.com="" role:="" roles/resourcemanager.organizationviewer="" condition:="" title:="" expirable="" access="" description:="" does="" not="" grant="" access="" after="" sep="" 2020="" expression:="" request.time="">< timestamp('2020-10-01t00:00:00.000z')="" for="" a="" description="" of="" iam="" and="" its="" features,="" see="" the="">IAM developer's guide __. |
get_location
get_location(
request: typing.Optional[
google.cloud.location.locations_pb2.GetLocationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.Location
Gets information about a location.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Location object. |
get_model_deployment_monitoring_job
get_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob
)
Gets a ModelDeploymentMonitoringJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetModelDeploymentMonitoringJobRequest(
name="name_value",
)
# Make the request
response = await client.get_model_deployment_monitoring_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.GetModelDeploymentMonitoringJob. |
name |
Required. The resource name of the ModelDeploymentMonitoringJob. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.ModelDeploymentMonitoringJob |
Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors. |
get_mtls_endpoint_and_cert_source
get_mtls_endpoint_and_cert_source(
client_options: typing.Optional[
google.api_core.client_options.ClientOptions
] = None,
)
Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is not "true", the
client cert source is None.
(2) if client_options.client_cert_source
is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if client_options.api_endpoint
if provided, use the provided one.
(2) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Parameter | |
---|---|
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTLSChannelError |
If any errors happen. |
Returns | |
---|---|
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] |
returns the API endpoint and the client cert source to use. |
get_nas_job
get_nas_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetNasJobRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.nas_job.NasJob
Gets a NasJob
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_nas_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetNasJobRequest(
name="name_value",
)
# Make the request
response = await client.get_nas_job(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetNasJobRequest, dict]]
The request object. Request message for JobService.GetNasJob. |
name |
Required. The name of the NasJob resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.NasJob |
Represents a Neural Architecture Search (NAS) job. |
get_nas_trial_detail
get_nas_trial_detail(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.GetNasTrialDetailRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.nas_job.NasTrialDetail
Gets a NasTrialDetail.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_get_nas_trial_detail():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetNasTrialDetailRequest(
name="name_value",
)
# Make the request
response = await client.get_nas_trial_detail(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.GetNasTrialDetailRequest, dict]]
The request object. Request message for JobService.GetNasTrialDetail. |
name |
Required. The name of the NasTrialDetail resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.NasTrialDetail |
Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned. |
get_operation
get_operation(
request: typing.Optional[
google.longrunning.operations_pb2.GetOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation
Gets the latest state of a long-running operation.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
An Operation object. |
get_transport_class
get_transport_class() -> (
typing.Type[
google.cloud.aiplatform_v1beta1.services.job_service.transports.base.JobServiceTransport
]
)
Returns an appropriate transport class.
Parameter | |
---|---|
Name | Description |
label |
typing.Optional[str]
The name of the desired transport. If none is provided, then the first transport in the registry is used. |
hyperparameter_tuning_job_path
hyperparameter_tuning_job_path(
project: str, location: str, hyperparameter_tuning_job: str
) -> str
Returns a fully-qualified hyperparameter_tuning_job string.
list_batch_prediction_jobs
list_batch_prediction_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListBatchPredictionJobsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListBatchPredictionJobsAsyncPager
)
Lists BatchPredictionJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_batch_prediction_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListBatchPredictionJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_batch_prediction_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListBatchPredictionJobsRequest, dict]]
The request object. Request message for JobService.ListBatchPredictionJobs. |
parent |
Required. The resource name of the Location to list the BatchPredictionJobs from. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListBatchPredictionJobsAsyncPager |
Response message for JobService.ListBatchPredictionJobs Iterating over this object will yield results and resolve additional pages automatically. |
list_custom_jobs
list_custom_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListCustomJobsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListCustomJobsAsyncPager
)
Lists CustomJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_custom_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListCustomJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_custom_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListCustomJobsRequest, dict]]
The request object. Request message for JobService.ListCustomJobs. |
parent |
Required. The resource name of the Location to list the CustomJobs from. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListCustomJobsAsyncPager |
Response message for JobService.ListCustomJobs Iterating over this object will yield results and resolve additional pages automatically. |
list_data_labeling_jobs
list_data_labeling_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListDataLabelingJobsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListDataLabelingJobsAsyncPager
)
Lists DataLabelingJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_data_labeling_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListDataLabelingJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_data_labeling_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListDataLabelingJobsRequest, dict]]
The request object. Request message for JobService.ListDataLabelingJobs. |
parent |
Required. The parent of the DataLabelingJob. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListDataLabelingJobsAsyncPager |
Response message for JobService.ListDataLabelingJobs. Iterating over this object will yield results and resolve additional pages automatically. |
list_hyperparameter_tuning_jobs
list_hyperparameter_tuning_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListHyperparameterTuningJobsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListHyperparameterTuningJobsAsyncPager
)
Lists HyperparameterTuningJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_hyperparameter_tuning_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListHyperparameterTuningJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_hyperparameter_tuning_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListHyperparameterTuningJobsRequest, dict]]
The request object. Request message for JobService.ListHyperparameterTuningJobs. |
parent |
Required. The resource name of the Location to list the HyperparameterTuningJobs from. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListHyperparameterTuningJobsAsyncPager |
Response message for JobService.ListHyperparameterTuningJobs Iterating over this object will yield results and resolve additional pages automatically. |
list_locations
list_locations(
request: typing.Optional[
google.cloud.location.locations_pb2.ListLocationsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.ListLocationsResponse
Lists information about the supported locations for this service.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for ListLocations method. |
list_model_deployment_monitoring_jobs
list_model_deployment_monitoring_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListModelDeploymentMonitoringJobsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListModelDeploymentMonitoringJobsAsyncPager
)
Lists ModelDeploymentMonitoringJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_model_deployment_monitoring_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListModelDeploymentMonitoringJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_model_deployment_monitoring_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelDeploymentMonitoringJobsRequest, dict]]
The request object. Request message for JobService.ListModelDeploymentMonitoringJobs. |
parent |
Required. The parent of the ModelDeploymentMonitoringJob. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListModelDeploymentMonitoringJobsAsyncPager |
Response message for JobService.ListModelDeploymentMonitoringJobs. Iterating over this object will yield results and resolve additional pages automatically. |
list_nas_jobs
list_nas_jobs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListNasJobsRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListNasJobsAsyncPager
Lists NasJobs in a Location.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_nas_jobs():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListNasJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_nas_jobs(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListNasJobsRequest, dict]]
The request object. Request message for JobService.ListNasJobs. |
parent |
Required. The resource name of the Location to list the NasJobs from. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListNasJobsAsyncPager |
Response message for JobService.ListNasJobs Iterating over this object will yield results and resolve additional pages automatically. |
list_nas_trial_details
list_nas_trial_details(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ListNasTrialDetailsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListNasTrialDetailsAsyncPager
)
List top NasTrialDetails of a NasJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_list_nas_trial_details():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListNasTrialDetailsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_nas_trial_details(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ListNasTrialDetailsRequest, dict]]
The request object. Request message for JobService.ListNasTrialDetails. |
parent |
Required. The name of the NasJob resource. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.ListNasTrialDetailsAsyncPager |
Response message for JobService.ListNasTrialDetails Iterating over this object will yield results and resolve additional pages automatically. |
list_operations
list_operations(
request: typing.Optional[
google.longrunning.operations_pb2.ListOperationsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.ListOperationsResponse
Lists operations that match the specified filter in the request.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for ListOperations method. |
model_deployment_monitoring_job_path
model_deployment_monitoring_job_path(
project: str, location: str, model_deployment_monitoring_job: str
) -> str
Returns a fully-qualified model_deployment_monitoring_job string.
model_path
model_path(project: str, location: str, model: str) -> str
Returns a fully-qualified model string.
nas_job_path
nas_job_path(project: str, location: str, nas_job: str) -> str
Returns a fully-qualified nas_job string.
nas_trial_detail_path
nas_trial_detail_path(
project: str, location: str, nas_job: str, nas_trial_detail: str
) -> str
Returns a fully-qualified nas_trial_detail string.
network_path
network_path(project: str, network: str) -> str
Returns a fully-qualified network string.
notification_channel_path
notification_channel_path(project: str, notification_channel: str) -> str
Returns a fully-qualified notification_channel string.
parse_batch_prediction_job_path
parse_batch_prediction_job_path(path: str) -> typing.Dict[str, str]
Parses a batch_prediction_job path into its component segments.
parse_common_billing_account_path
parse_common_billing_account_path(path: str) -> typing.Dict[str, str]
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str) -> typing.Dict[str, str]
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str) -> typing.Dict[str, str]
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str) -> typing.Dict[str, str]
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str) -> typing.Dict[str, str]
Parse a project path into its component segments.
parse_context_path
parse_context_path(path: str) -> typing.Dict[str, str]
Parses a context path into its component segments.
parse_custom_job_path
parse_custom_job_path(path: str) -> typing.Dict[str, str]
Parses a custom_job path into its component segments.
parse_data_labeling_job_path
parse_data_labeling_job_path(path: str) -> typing.Dict[str, str]
Parses a data_labeling_job path into its component segments.
parse_dataset_path
parse_dataset_path(path: str) -> typing.Dict[str, str]
Parses a dataset path into its component segments.
parse_endpoint_path
parse_endpoint_path(path: str) -> typing.Dict[str, str]
Parses a endpoint path into its component segments.
parse_hyperparameter_tuning_job_path
parse_hyperparameter_tuning_job_path(path: str) -> typing.Dict[str, str]
Parses a hyperparameter_tuning_job path into its component segments.
parse_model_deployment_monitoring_job_path
parse_model_deployment_monitoring_job_path(path: str) -> typing.Dict[str, str]
Parses a model_deployment_monitoring_job path into its component segments.
parse_model_path
parse_model_path(path: str) -> typing.Dict[str, str]
Parses a model path into its component segments.
parse_nas_job_path
parse_nas_job_path(path: str) -> typing.Dict[str, str]
Parses a nas_job path into its component segments.
parse_nas_trial_detail_path
parse_nas_trial_detail_path(path: str) -> typing.Dict[str, str]
Parses a nas_trial_detail path into its component segments.
parse_network_path
parse_network_path(path: str) -> typing.Dict[str, str]
Parses a network path into its component segments.
parse_notification_channel_path
parse_notification_channel_path(path: str) -> typing.Dict[str, str]
Parses a notification_channel path into its component segments.
parse_persistent_resource_path
parse_persistent_resource_path(path: str) -> typing.Dict[str, str]
Parses a persistent_resource path into its component segments.
parse_tensorboard_path
parse_tensorboard_path(path: str) -> typing.Dict[str, str]
Parses a tensorboard path into its component segments.
parse_trial_path
parse_trial_path(path: str) -> typing.Dict[str, str]
Parses a trial path into its component segments.
pause_model_deployment_monitoring_job
pause_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.PauseModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Pauses a ModelDeploymentMonitoringJob. If the job is running, the server makes a best effort to cancel the job. Will mark xref_ModelDeploymentMonitoringJob.state to 'PAUSED'.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_pause_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.PauseModelDeploymentMonitoringJobRequest(
name="name_value",
)
# Make the request
await client.pause_model_deployment_monitoring_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.PauseModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.PauseModelDeploymentMonitoringJob. |
name |
Required. The resource name of the ModelDeploymentMonitoringJob to pause. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
persistent_resource_path
persistent_resource_path(
project: str, location: str, persistent_resource: str
) -> str
Returns a fully-qualified persistent_resource string.
resume_model_deployment_monitoring_job
resume_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.ResumeModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
Resumes a paused ModelDeploymentMonitoringJob. It will start to run from next scheduled time. A deleted ModelDeploymentMonitoringJob can't be resumed.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_resume_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ResumeModelDeploymentMonitoringJobRequest(
name="name_value",
)
# Make the request
await client.resume_model_deployment_monitoring_job(request=request)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.ResumeModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.ResumeModelDeploymentMonitoringJob. |
name |
Required. The resource name of the ModelDeploymentMonitoringJob to resume. Format: |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
search_model_deployment_monitoring_stats_anomalies
search_model_deployment_monitoring_stats_anomalies(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.SearchModelDeploymentMonitoringStatsAnomaliesRequest,
dict,
]
] = None,
*,
model_deployment_monitoring_job: typing.Optional[str] = None,
deployed_model_id: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.job_service.pagers.SearchModelDeploymentMonitoringStatsAnomaliesAsyncPager
)
Searches Model Monitoring Statistics generated within a given time window.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_search_model_deployment_monitoring_stats_anomalies():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest(
model_deployment_monitoring_job="model_deployment_monitoring_job_value",
deployed_model_id="deployed_model_id_value",
)
# Make the request
page_result = client.search_model_deployment_monitoring_stats_anomalies(request=request)
# Handle the response
async for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.SearchModelDeploymentMonitoringStatsAnomaliesRequest, dict]]
The request object. Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies. |
model_deployment_monitoring_job |
Required. ModelDeploymentMonitoring Job resource name. Format: |
deployed_model_id |
Required. The DeployedModel ID of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id]. This corresponds to the |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.job_service.pagers.SearchModelDeploymentMonitoringStatsAnomaliesAsyncPager |
Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies. Iterating over this object will yield results and resolve additional pages automatically. |
set_iam_policy
set_iam_policy(
request: typing.Optional[google.iam.v1.iam_policy_pb2.SetIamPolicyRequest] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy
Sets the IAM access control policy on the specified function.
Replaces any existing policy.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings . A binding binds one or more members to a single role . Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition , which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01t00:00:00.000z')",="" }="" }="" ]="" }="" **yaml="" example**="" ::="" bindings:="" -="" members:="" -="" user:mike@example.com="" -="" group:admins@example.com="" -="" domain:google.com="" -="" serviceaccount:my-project-id@appspot.gserviceaccount.com="" role:="" roles/resourcemanager.organizationadmin="" -="" members:="" -="" user:eve@example.com="" role:="" roles/resourcemanager.organizationviewer="" condition:="" title:="" expirable="" access="" description:="" does="" not="" grant="" access="" after="" sep="" 2020="" expression:="" request.time="">< timestamp('2020-10-01t00:00:00.000z')="" for="" a="" description="" of="" iam="" and="" its="" features,="" see="" the="">IAM developer's guide __. |
tensorboard_path
tensorboard_path(project: str, location: str, tensorboard: str) -> str
Returns a fully-qualified tensorboard string.
test_iam_permissions
test_iam_permissions(
request: typing.Optional[
google.iam.v1.iam_policy_pb2.TestIamPermissionsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.iam_policy_pb2.TestIamPermissionsResponse
Tests the specified IAM permissions against the IAM access control policy for a function.
If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for TestIamPermissions method. |
trial_path
trial_path(project: str, location: str, study: str, trial: str) -> str
Returns a fully-qualified trial string.
update_model_deployment_monitoring_job
update_model_deployment_monitoring_job(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.job_service.UpdateModelDeploymentMonitoringJobRequest,
dict,
]
] = None,
*,
model_deployment_monitoring_job: typing.Optional[
google.cloud.aiplatform_v1beta1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob
] = None,
update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation_async.AsyncOperation
Updates a ModelDeploymentMonitoringJob.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1
async def sample_update_model_deployment_monitoring_job():
# Create a client
client = aiplatform_v1beta1.JobServiceAsyncClient()
# Initialize request argument(s)
model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob()
model_deployment_monitoring_job.display_name = "display_name_value"
model_deployment_monitoring_job.endpoint = "endpoint_value"
request = aiplatform_v1beta1.UpdateModelDeploymentMonitoringJobRequest(
model_deployment_monitoring_job=model_deployment_monitoring_job,
)
# Make the request
operation = client.update_model_deployment_monitoring_job(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateModelDeploymentMonitoringJobRequest, dict]]
The request object. Request message for JobService.UpdateModelDeploymentMonitoringJob. |
model_deployment_monitoring_job |
ModelDeploymentMonitoringJob
Required. The model monitoring configuration which replaces the resource on the server. This corresponds to the |
update_mask |
Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation_async.AsyncOperation |
An object representing a long-running operation. The result type for the operation will be ModelDeploymentMonitoringJob Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors. |
wait_operation
wait_operation(
request: typing.Optional[
google.longrunning.operations_pb2.WaitOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary_async.AsyncRetry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation
Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.
If the operation is already done, the latest state is immediately returned.
If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC
timeout is used. If the server does not support this method, it returns
google.rpc.Code.UNIMPLEMENTED
.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry_async.AsyncRetry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
An Operation object. |