def get_create_feature_request(
self, ) -> gca_featurestore_service.CreateFeatureRequest:
"""Return create feature request."""
gapic_feature = gca_feature.Feature(
value_type=self._get_value_type_enum(), )
if self.labels:
utils.validate_labels(self.labels)
gapic_feature.labels = self.labels
if self.description:
gapic_feature.description = self.description
create_feature_request = gca_featurestore_service.CreateFeatureRequest(
feature=gapic_feature, feature_id=self._get_feature_id())
return create_feature_request
def create(
cls,
display_name: Optional[str] = None,
gcs_source: Optional[Union[str, Sequence[str]]] = None,
bq_source: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
labels: Optional[Dict[str, str]] = None,
encryption_spec_key_name: Optional[str] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
) -> "TabularDataset":
"""Creates a new tabular dataset.
Args:
display_name (str):
Optional. The user-defined name of the Dataset.
The name can be up to 128 characters long and can be consist
of any UTF-8 characters.
gcs_source (Union[str, Sequence[str]]):
Google Cloud Storage URI(-s) to the
input file(s). May contain wildcards. For more
information on wildcards, see
https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
examples:
str: "gs://bucket/file.csv"
Sequence[str]: ["gs://bucket/file1.csv", "gs://bucket/file2.csv"]
bq_source (str):
BigQuery URI to the input table.
example:
"bq://project.dataset.table_name"
project (str):
Project to upload this model to. Overrides project set in
aiplatform.init.
location (str):
Location to upload this model to. Overrides location set in
aiplatform.init.
credentials (auth_credentials.Credentials):
Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Strings which should be sent along with the request as metadata.
labels (Dict[str, str]):
Optional. Labels with user-defined metadata to organize your Tensorboards.
Label keys and values can be no longer than 64 characters
(Unicode codepoints), can only contain lowercase letters, numeric
characters, underscores and dashes. International characters are allowed.
No more than 64 user labels can be associated with one Tensorboard
(System labels are excluded).
See https://goo.gl/xmQnxf for more information and examples of labels.
System reserved label keys are prefixed with "aiplatform.googleapis.com/"
and are immutable.
encryption_spec_key_name (Optional[str]):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the dataset. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
Overrides encryption_spec_key_name set in aiplatform.init.
sync (bool):
Whether to execute this method synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
create_request_timeout (float):
Optional. The timeout for the create request in seconds.
Returns:
tabular_dataset (TabularDataset):
Instantiated representation of the managed tabular dataset resource.
"""
if not display_name:
display_name = cls._generate_display_name()
utils.validate_display_name(display_name)
if labels:
utils.validate_labels(labels)
api_client = cls._instantiate_client(location=location,
credentials=credentials)
metadata_schema_uri = schema.dataset.metadata.tabular
datasource = _datasources.create_datasource(
metadata_schema_uri=metadata_schema_uri,
gcs_source=gcs_source,
bq_source=bq_source,
)
return cls._create_and_import(
api_client=api_client,
parent=initializer.global_config.common_location_path(
project=project, location=location),
display_name=display_name,
metadata_schema_uri=metadata_schema_uri,
datasource=datasource,
project=project or initializer.global_config.project,
location=location or initializer.global_config.location,
credentials=credentials or initializer.global_config.credentials,
request_metadata=request_metadata,
labels=labels,
encryption_spec=initializer.global_config.get_encryption_spec(
encryption_spec_key_name=encryption_spec_key_name),
sync=sync,
create_request_timeout=create_request_timeout,
)
def create(
cls,
display_name: Optional[str] = None,
gcs_source: Optional[Union[str, Sequence[str]]] = None,
import_schema_uri: Optional[str] = None,
data_item_labels: Optional[Dict] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
labels: Optional[Dict[str, str]] = None,
encryption_spec_key_name: Optional[str] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
) -> "VideoDataset":
"""Creates a new video dataset and optionally imports data into dataset
when source and import_schema_uri are passed.
Args:
display_name (str):
Optional. The user-defined name of the Dataset.
The name can be up to 128 characters long and can be consist
of any UTF-8 characters.
gcs_source (Union[str, Sequence[str]]):
Google Cloud Storage URI(-s) to the
input file(s). May contain wildcards. For more
information on wildcards, see
https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
examples:
str: "gs://bucket/file.csv"
Sequence[str]: ["gs://bucket/file1.csv", "gs://bucket/file2.csv"]
import_schema_uri (str):
Points to a YAML file stored on Google Cloud
Storage describing the import format. Validation will be
done against the schema. The schema is defined as an
`OpenAPI 3.0.2 Schema
Object <https://tinyurl.com/y538mdwt>`__.
data_item_labels (Dict):
Labels that will be applied to newly imported DataItems. If
an identical DataItem as one being imported already exists
in the Dataset, then these labels will be appended to these
of the already existing one, and if labels with identical
key is imported before, the old label value will be
overwritten. If two DataItems are identical in the same
import data operation, the labels will be combined and if
key collision happens in this case, one of the values will
be picked randomly. Two DataItems are considered identical
if their content bytes are identical (e.g. image bytes or
pdf bytes). These labels will be overridden by Annotation
labels specified inside index file referenced by
``import_schema_uri``,
e.g. jsonl file.
project (str):
Project to upload this model to. Overrides project set in
aiplatform.init.
location (str):
Location to upload this model to. Overrides location set in
aiplatform.init.
credentials (auth_credentials.Credentials):
Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Strings which should be sent along with the request as metadata.
labels (Dict[str, str]):
Optional. Labels with user-defined metadata to organize your Tensorboards.
Label keys and values can be no longer than 64 characters
(Unicode codepoints), can only contain lowercase letters, numeric
characters, underscores and dashes. International characters are allowed.
No more than 64 user labels can be associated with one Tensorboard
(System labels are excluded).
See https://goo.gl/xmQnxf for more information and examples of labels.
System reserved label keys are prefixed with "aiplatform.googleapis.com/"
and are immutable.
encryption_spec_key_name (Optional[str]):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the dataset. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
Overrides encryption_spec_key_name set in aiplatform.init.
create_request_timeout (float):
Optional. The timeout for the create request in seconds.
sync (bool):
Whether to execute this method synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
Returns:
video_dataset (VideoDataset):
Instantiated representation of the managed video dataset resource.
"""
if not display_name:
display_name = cls._generate_display_name()
utils.validate_display_name(display_name)
if labels:
utils.validate_labels(labels)
api_client = cls._instantiate_client(location=location,
credentials=credentials)
metadata_schema_uri = schema.dataset.metadata.video
datasource = _datasources.create_datasource(
metadata_schema_uri=metadata_schema_uri,
import_schema_uri=import_schema_uri,
gcs_source=gcs_source,
data_item_labels=data_item_labels,
)
return cls._create_and_import(
api_client=api_client,
parent=initializer.global_config.common_location_path(
project=project, location=location),
display_name=display_name,
metadata_schema_uri=metadata_schema_uri,
datasource=datasource,
project=project or initializer.global_config.project,
location=location or initializer.global_config.location,
credentials=credentials or initializer.global_config.credentials,
request_metadata=request_metadata,
labels=labels,
encryption_spec=initializer.global_config.get_encryption_spec(
encryption_spec_key_name=encryption_spec_key_name),
sync=sync,
create_request_timeout=create_request_timeout,
)
def __init__(
self,
# TODO(b/223262536): Make the display_name parameter optional in the next major release
display_name: str,
template_path: str,
job_id: Optional[str] = None,
pipeline_root: Optional[str] = None,
parameter_values: Optional[Dict[str, Any]] = None,
enable_caching: Optional[bool] = None,
encryption_spec_key_name: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
credentials: Optional[auth_credentials.Credentials] = None,
project: Optional[str] = None,
location: Optional[str] = None,
):
"""Retrieves a PipelineJob resource and instantiates its
representation.
Args:
display_name (str):
Required. The user-defined name of this Pipeline.
template_path (str):
Required. The path of PipelineJob or PipelineSpec JSON or YAML file. It
can be a local path or a Google Cloud Storage URI.
Example: "gs://project.name"
job_id (str):
Optional. The unique ID of the job run.
If not specified, pipeline name + timestamp will be used.
pipeline_root (str):
Optional. The root of the pipeline outputs. Default to be staging bucket.
parameter_values (Dict[str, Any]):
Optional. The mapping from runtime parameter names to its values that
control the pipeline run.
enable_caching (bool):
Optional. Whether to turn on caching for the run.
If this is not set, defaults to the compile time settings, which
are True for all tasks by default, while users may specify
different caching options for individual tasks.
If this is set, the setting applies to all tasks in the pipeline.
Overrides the compile time settings.
encryption_spec_key_name (str):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the job. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If this is set, then all
resources created by the PipelineJob will
be encrypted with the provided encryption key.
Overrides encryption_spec_key_name set in aiplatform.init.
labels (Dict[str,str]):
Optional. The user defined metadata to organize PipelineJob.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to create this PipelineJob.
Overrides credentials set in aiplatform.init.
project (str),
Optional. The project that you want to run this PipelineJob in. If not set,
the project set in aiplatform.init will be used.
location (str),
Optional. Location to create PipelineJob. If not set,
location set in aiplatform.init will be used.
Raises:
ValueError: If job_id or labels have incorrect format.
"""
if not display_name:
display_name = self.__class__._generate_display_name()
utils.validate_display_name(display_name)
if labels:
utils.validate_labels(labels)
super().__init__(project=project,
location=location,
credentials=credentials)
self._parent = initializer.global_config.common_location_path(
project=project, location=location)
# this loads both .yaml and .json files because YAML is a superset of JSON
pipeline_json = yaml_utils.load_yaml(template_path, self.project,
self.credentials)
# Pipeline_json can be either PipelineJob or PipelineSpec.
if pipeline_json.get("pipelineSpec") is not None:
pipeline_job = pipeline_json
pipeline_root = (
pipeline_root
or pipeline_job["pipelineSpec"].get("defaultPipelineRoot")
or pipeline_job["runtimeConfig"].get("gcsOutputDirectory")
or initializer.global_config.staging_bucket)
else:
pipeline_job = {
"pipelineSpec": pipeline_json,
"runtimeConfig": {},
}
pipeline_root = (
pipeline_root
or pipeline_job["pipelineSpec"].get("defaultPipelineRoot")
or initializer.global_config.staging_bucket)
builder = pipeline_utils.PipelineRuntimeConfigBuilder.from_job_spec_json(
pipeline_job)
builder.update_pipeline_root(pipeline_root)
builder.update_runtime_parameters(parameter_values)
runtime_config_dict = builder.build()
runtime_config = gca_pipeline_job_v1.PipelineJob.RuntimeConfig()._pb
json_format.ParseDict(runtime_config_dict, runtime_config)
pipeline_name = pipeline_job["pipelineSpec"]["pipelineInfo"]["name"]
self.job_id = job_id or "{pipeline_name}-{timestamp}".format(
pipeline_name=re.sub(
"[^-0-9a-z]+", "-",
pipeline_name.lower()).lstrip("-").rstrip("-"),
timestamp=_get_current_time().strftime("%Y%m%d%H%M%S"),
)
if not _VALID_NAME_PATTERN.match(self.job_id):
raise ValueError(
"Generated job ID: {} is illegal as a Vertex pipelines job ID. "
"Expecting an ID following the regex pattern "
'"[a-z][-a-z0-9]{{0,127}}"'.format(job_id))
if enable_caching is not None:
_set_enable_caching_value(pipeline_job["pipelineSpec"],
enable_caching)
self._gca_resource = gca_pipeline_job_v1.PipelineJob(
display_name=display_name,
pipeline_spec=pipeline_job["pipelineSpec"],
labels=labels,
runtime_config=runtime_config,
encryption_spec=initializer.global_config.get_encryption_spec(
encryption_spec_key_name=encryption_spec_key_name),
)
def _create(
cls,
display_name: str,
contents_delta_uri: str,
config: matching_engine_index_config.MatchingEngineIndexConfig,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
) -> "MatchingEngineIndex":
"""Creates a MatchingEngineIndex resource.
Args:
display_name (str):
Required. The display name of the Index.
The name can be up to 128 characters long and
can be consist of any UTF-8 characters.
contents_delta_uri (str):
Required. Allows inserting, updating or deleting the contents of the Matching Engine Index.
The string must be a valid Google Cloud Storage directory path. If this
field is set when calling IndexService.UpdateIndex, then no other
Index field can be also updated as part of the same call.
The expected structure and format of the files this URI points to is
described at
https://docs.google.com/document/d/12DLVB6Nq6rdv8grxfBsPhUA283KWrQ9ZenPBp0zUC30
config (matching_engine_index_config.MatchingEngineIndexConfig):
Required. The configuration with regard to the algorithms used for efficient search.
description (str):
Optional. The description of the Index.
labels (Dict[str, str]):
Optional. The labels with user-defined
metadata to organize your Index.
Label keys and values can be no longer than 64
characters (Unicode codepoints), can only
contain lowercase letters, numeric characters,
underscores and dashes. International characters
are allowed.
See https://goo.gl/xmQnxf for more information
on and examples of labels. No more than 64 user
labels can be associated with one
Index(System labels are excluded)."
System reserved label keys are prefixed with
"aiplatform.googleapis.com/" and are immutable.
project (str):
Optional. Project to create EntityType in. If not set, project
set in aiplatform.init will be used.
location (str):
Optional. Location to create EntityType in. If not set, location
set in aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to create EntityTypes. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
encryption_spec (str):
Optional. Customer-managed encryption key
spec for data storage. If set, both of the
online and offline data storage will be secured
by this key.
sync (bool):
Optional. Whether to execute this creation synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
Returns:
MatchingEngineIndex - Index resource object
"""
gapic_index = gca_matching_engine_index.Index(
display_name=display_name,
description=description,
metadata={
"config": config.as_dict(),
"contentsDeltaUri": contents_delta_uri,
},
)
if labels:
utils.validate_labels(labels)
gapic_index.labels = labels
api_client = cls._instantiate_client(location=location, credentials=credentials)
create_lro = api_client.create_index(
parent=initializer.global_config.common_location_path(
project=project, location=location
),
index=gapic_index,
metadata=request_metadata,
)
_LOGGER.log_create_with_lro(cls, create_lro)
created_index = create_lro.result()
_LOGGER.log_create_complete(cls, created_index, "index")
index_obj = cls(
index_name=created_index.name,
project=project,
location=location,
credentials=credentials,
)
return index_obj
def update_metadata(
self,
display_name: Optional[str] = None,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
) -> "MatchingEngineIndex":
"""Updates the metadata for this index.
Args:
display_name (str):
Optional. The display name of the Index.
The name can be up to 128 characters long and
can be consist of any UTF-8 characters.
description (str):
Optional. The description of the Index.
labels (Dict[str, str]):
Optional. The labels with user-defined
metadata to organize your Indexs.
Label keys and values can be no longer than 64
characters (Unicode codepoints), can only
contain lowercase letters, numeric characters,
underscores and dashes. International characters
are allowed.
See https://goo.gl/xmQnxf for more information
on and examples of labels. No more than 64 user
labels can be associated with one Index
(System labels are excluded)."
System reserved label keys are prefixed with
"aiplatform.googleapis.com/" and are immutable.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
Returns:
MatchingEngineIndex - The updated index resource object.
"""
self.wait()
update_mask = list()
if labels:
utils.validate_labels(labels)
update_mask.append("labels")
if display_name is not None:
update_mask.append("display_name")
if description is not None:
update_mask.append("description")
update_mask = field_mask_pb2.FieldMask(paths=update_mask)
gapic_index = gca_matching_engine_index.Index(
name=self.resource_name,
display_name=display_name,
description=description,
labels=labels,
)
_LOGGER.log_action_start_against_resource(
"Updating",
"index",
self,
)
update_lro = self.api_client.update_index(
index=gapic_index,
update_mask=update_mask,
metadata=request_metadata,
)
_LOGGER.log_action_started_against_resource_with_lro(
"Update", "index", self.__class__, update_lro
)
self._gca_resource = update_lro.result()
_LOGGER.log_action_completed_against_resource("index", "Updated", self)
return self
def create(
cls,
display_name: str,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
encryption_spec_key_name: Optional[str] = None,
) -> "Tensorboard":
"""Creates a new tensorboard.
Example Usage:
tb = aiplatform.Tensorboard.create(
display_name='my display name',
description='my description',
labels={
'key1': 'value1',
'key2': 'value2'
}
)
Args:
display_name (str):
Required. The user-defined name of the Tensorboard.
The name can be up to 128 characters long and can be consist
of any UTF-8 characters.
description (str):
Optional. Description of this Tensorboard.
labels (Dict[str, str]):
Optional. Labels with user-defined metadata to organize your Tensorboards.
Label keys and values can be no longer than 64 characters
(Unicode codepoints), can only contain lowercase letters, numeric
characters, underscores and dashes. International characters are allowed.
No more than 64 user labels can be associated with one Tensorboard
(System labels are excluded).
See https://goo.gl/xmQnxf for more information and examples of labels.
System reserved label keys are prefixed with "aiplatform.googleapis.com/"
and are immutable.
project (str):
Optional. Project to upload this model to. Overrides project set in
aiplatform.init.
location (str):
Optional. Location to upload this model to. Overrides location set in
aiplatform.init.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
encryption_spec_key_name (str):
Optional. Cloud KMS resource identifier of the customer
managed encryption key used to protect the tensorboard. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this Tensorboard and all sub-resources of this Tensorboard will be secured by this key.
Overrides encryption_spec_key_name set in aiplatform.init.
Returns:
tensorboard (Tensorboard):
Instantiated representation of the managed tensorboard resource.
"""
utils.validate_display_name(display_name)
if labels:
utils.validate_labels(labels)
api_client = cls._instantiate_client(location=location,
credentials=credentials)
parent = initializer.global_config.common_location_path(
project=project, location=location)
encryption_spec = initializer.global_config.get_encryption_spec(
encryption_spec_key_name=encryption_spec_key_name)
gapic_tensorboard = gca_tensorboard.Tensorboard(
display_name=display_name,
description=description,
labels=labels,
encryption_spec=encryption_spec,
)
create_tensorboard_lro = api_client.create_tensorboard(
parent=parent,
tensorboard=gapic_tensorboard,
metadata=request_metadata)
_LOGGER.log_create_with_lro(cls, create_tensorboard_lro)
created_tensorboard = create_tensorboard_lro.result()
_LOGGER.log_create_complete(cls, created_tensorboard, "tb")
return cls(
tensorboard_name=created_tensorboard.name,
credentials=credentials,
)
def create(
cls,
tensorboard_run_id: str,
tensorboard_experiment_name: str,
tensorboard_id: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Sequence[Tuple[str, str]] = (),
) -> "TensorboardRun":
"""Creates a new tensorboard.
Example Usage:
tb = aiplatform.TensorboardExperiment.create(
tensorboard_experiment_id='my-experiment'
tensorboard_id='456'
display_name='my display name',
description='my description',
labels={
'key1': 'value1',
'key2': 'value2'
}
)
Args:
tensorboard_run_id (str):
Required. The ID to use for the Tensorboard run, which
will become the final component of the Tensorboard run's
resource name.
This value should be 1-128 characters, and valid:
characters are /[a-z][0-9]-/.
tensorboard_experiment_name (str):
Required. The resource name or ID of the TensorboardExperiment
to create the TensorboardRun in. Resource name format:
``projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}``
If resource ID is provided then tensorboard_id must be provided.
tensorboard_id (str):
Optional. The resource ID of the Tensorboard to create
the TensorboardRun in. Format of resource name.
display_name (str):
Optional. The user-defined name of the Tensorboard Run.
This value must be unique among all TensorboardRuns belonging to the
same parent TensorboardExperiment.
If not provided tensorboard_run_id will be used.
description (str):
Optional. Description of this Tensorboard Run.
labels (Dict[str, str]):
Optional. Labels with user-defined metadata to organize your Tensorboards.
Label keys and values can be no longer than 64 characters
(Unicode codepoints), can only contain lowercase letters, numeric
characters, underscores and dashes. International characters are allowed.
No more than 64 user labels can be associated with one Tensorboard
(System labels are excluded).
See https://goo.gl/xmQnxf for more information and examples of labels.
System reserved label keys are prefixed with "aiplatform.googleapis.com/"
and are immutable.
project (str):
Optional. Project to upload this model to. Overrides project set in
aiplatform.init.
location (str):
Optional. Location to upload this model to. Overrides location set in
aiplatform.init.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
Returns:
TensorboardExperiment: The TensorboardExperiment resource.
"""
if display_name:
utils.validate_display_name(display_name)
if labels:
utils.validate_labels(labels)
display_name = display_name or tensorboard_run_id
api_client = cls._instantiate_client(location=location,
credentials=credentials)
parent = utils.full_resource_name(
resource_name=tensorboard_experiment_name,
resource_noun=TensorboardExperiment._resource_noun,
parse_resource_name_method=TensorboardExperiment.
_parse_resource_name,
format_resource_name_method=TensorboardExperiment.
_format_resource_name,
parent_resource_name_fields={
Tensorboard._resource_noun: tensorboard_id
},
project=project,
location=location,
)
gapic_tensorboard_run = gca_tensorboard_run.TensorboardRun(
display_name=display_name,
description=description,
labels=labels,
)
_LOGGER.log_create_with_lro(cls)
tensorboard_run = api_client.create_tensorboard_run(
parent=parent,
tensorboard_run=gapic_tensorboard_run,
tensorboard_run_id=tensorboard_run_id,
metadata=request_metadata,
)
_LOGGER.log_create_complete(cls, tensorboard_run, "tb_run")
return cls(
tensorboard_run_name=tensorboard_run.name,
credentials=credentials,
)
def update(
self,
display_name: Optional[str] = None,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
encryption_spec_key_name: Optional[str] = None,
) -> "Tensorboard":
"""Updates an existing tensorboard.
Example Usage:
tb = aiplatform.Tensorboard(tensorboard_name='123456')
tb.update(
display_name='update my display name',
description='update my description',
)
Args:
display_name (str):
Optional. User-defined name of the Tensorboard.
The name can be up to 128 characters long and can be consist
of any UTF-8 characters.
description (str):
Optional. Description of this Tensorboard.
labels (Dict[str, str]):
Optional. Labels with user-defined metadata to organize your Tensorboards.
Label keys and values can be no longer than 64 characters
(Unicode codepoints), can only contain lowercase letters, numeric
characters, underscores and dashes. International characters are allowed.
No more than 64 user labels can be associated with one Tensorboard
(System labels are excluded).
See https://goo.gl/xmQnxf for more information and examples of labels.
System reserved label keys are prefixed with "aiplatform.googleapis.com/"
and are immutable.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
encryption_spec_key_name (str):
Optional. Cloud KMS resource identifier of the customer
managed encryption key used to protect the tensorboard. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this Tensorboard and all sub-resources of this Tensorboard will be secured by this key.
Overrides encryption_spec_key_name set in aiplatform.init.
Returns:
Tensorboard: The managed tensorboard resource.
"""
update_mask = list()
if display_name:
utils.validate_display_name(display_name)
update_mask.append("display_name")
if description:
update_mask.append("description")
if labels:
utils.validate_labels(labels)
update_mask.append("labels")
encryption_spec = None
if encryption_spec_key_name:
encryption_spec = initializer.global_config.get_encryption_spec(
encryption_spec_key_name=encryption_spec_key_name, )
update_mask.append("encryption_spec")
update_mask = field_mask_pb2.FieldMask(paths=update_mask)
gapic_tensorboard = gca_tensorboard.Tensorboard(
name=self.resource_name,
display_name=display_name,
description=description,
labels=labels,
encryption_spec=encryption_spec,
)
_LOGGER.log_action_start_against_resource(
"Updating",
"tensorboard",
self,
)
update_tensorboard_lro = self.api_client.update_tensorboard(
tensorboard=gapic_tensorboard,
update_mask=update_mask,
metadata=request_metadata,
)
_LOGGER.log_action_started_against_resource_with_lro(
"Update", "tensorboard", self.__class__, update_tensorboard_lro)
update_tensorboard_lro.result()
_LOGGER.log_action_completed_against_resource("tensorboard", "updated",
self)
return self
def update(
self,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
update_request_timeout: Optional[float] = None,
) -> "Feature":
"""Updates an existing managed feature resource.
Example Usage:
my_feature = aiplatform.Feature(
feature_name='my_feature_id',
featurestore_id='my_featurestore_id',
entity_type_id='my_entity_type_id',
)
my_feature.update(
description='update my description',
)
Args:
description (str):
Optional. Description of the Feature.
labels (Dict[str, str]):
Optional. The labels with user-defined
metadata to organize your Features.
Label keys and values can be no longer than 64
characters (Unicode codepoints), can only
contain lowercase letters, numeric characters,
underscores and dashes. International characters
are allowed.
See https://goo.gl/xmQnxf for more information
on and examples of labels. No more than 64 user
labels can be associated with one Feature
(System labels are excluded)."
System reserved label keys are prefixed with
"aiplatform.googleapis.com/" and are immutable.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
update_request_timeout (float):
Optional. The timeout for the update request in seconds.
Returns:
Feature - The updated feature resource object.
"""
self.wait()
update_mask = list()
if description:
update_mask.append("description")
if labels:
utils.validate_labels(labels)
update_mask.append("labels")
update_mask = field_mask_pb2.FieldMask(paths=update_mask)
gapic_feature = gca_feature.Feature(
name=self.resource_name,
description=description,
labels=labels,
)
_LOGGER.log_action_start_against_resource(
"Updating",
"feature",
self,
)
update_feature_lro = self.api_client.update_feature(
feature=gapic_feature,
update_mask=update_mask,
metadata=request_metadata,
timeout=update_request_timeout,
)
_LOGGER.log_action_started_against_resource_with_lro(
"Update", "feature", self.__class__, update_feature_lro)
update_feature_lro.result()
_LOGGER.log_action_completed_against_resource("feature", "updated",
self)
return self
def create(
cls,
display_name: str,
network: str,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
) -> "MatchingEngineIndexEndpoint":
"""Creates a MatchingEngineIndexEndpoint resource.
Example Usage:
my_index_endpoint = aiplatform.IndexEndpoint.create(
display_name='my_endpoint',
)
Args:
display_name (str):
Required. The display name of the IndexEndpoint.
The name can be up to 128 characters long and
can be consist of any UTF-8 characters.
network (str):
Required. The full name of the Google Compute Engine
`network <https://cloud.google.com/compute/docs/networks-and-firewalls#networks>`__
to which the IndexEndpoint should be peered.
Private services access must already be configured for the
network. If left unspecified, the Endpoint is not peered
with any network.
`Format <https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert>`__:
projects/{project}/global/networks/{network}. Where
{project} is a project number, as in '12345', and {network}
is network name.
description (str):
Optional. The description of the IndexEndpoint.
labels (Dict[str, str]):
Optional. The labels with user-defined
metadata to organize your IndexEndpoint.
Label keys and values can be no longer than 64
characters (Unicode codepoints), can only
contain lowercase letters, numeric characters,
underscores and dashes. International characters
are allowed.
See https://goo.gl/xmQnxf for more information
on and examples of labels. No more than 64 user
labels can be associated with one
IndexEndpoint (System labels are excluded)."
System reserved label keys are prefixed with
"aiplatform.googleapis.com/" and are immutable.
project (str):
Optional. Project to create EntityType in. If not set, project
set in aiplatform.init will be used.
location (str):
Optional. Location to create EntityType in. If not set, location
set in aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to create EntityTypes. Overrides
credentials set in aiplatform.init.
request_metadata (Sequence[Tuple[str, str]]):
Optional. Strings which should be sent along with the request as metadata.
sync (bool):
Optional. Whether to execute this creation synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
Returns:
MatchingEngineIndexEndpoint - IndexEndpoint resource object
"""
gapic_index_endpoint = gca_matching_engine_index_endpoint.IndexEndpoint(
display_name=display_name,
description=description,
network=network,
)
if labels:
utils.validate_labels(labels)
gapic_index_endpoint.labels = labels
api_client = cls._instantiate_client(location=location,
credentials=credentials)
create_lro = api_client.create_index_endpoint(
parent=initializer.global_config.common_location_path(
project=project, location=location),
index_endpoint=gapic_index_endpoint,
metadata=request_metadata,
)
_LOGGER.log_create_with_lro(cls, create_lro)
created_index = create_lro.result()
_LOGGER.log_create_complete(cls, created_index, "index_endpoint")
index_obj = cls(
index_endpoint_name=created_index.name,
project=project,
location=location,
credentials=credentials,
)
return index_obj
