Class Tensorboard (1.12.1)

Version 1.12.1

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Tensorboard(
    tensorboard_name: str,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

Managed tensorboard resource for Vertex AI.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.tensorboard.tensorboard_resource._TensorboardServiceResource > Tensorboard

Methods

Tensorboard

Tensorboard(
    tensorboard_name: str,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

Retrieves an existing managed tensorboard given a tensorboard name or ID.

Parameters

Name	Description
tensorboard_name	str Required. A fully-qualified tensorboard resource name or tensorboard ID. Example: "projects/123/locations/us-central1/tensorboards/456" or "456" when project and location are initialized or passed.
project	str Optional. Project to retrieve tensorboard from. If not set, project set in aiplatform.init will be used.
location	str Optional. Location to retrieve tensorboard from. If not set, location set in aiplatform.init will be used.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to retrieve this Tensorboard. Overrides credentials set in aiplatform.init.

create

create(
    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[google.auth.credentials.Credentials] = None,
    request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
    encryption_spec_key_name: Optional[str] = None,
    create_request_timeout: Optional[float] = None,
)

Creates a new tensorboard.

Example Usage:

tb = aiplatform.Tensorboard.create(
    display_name='my display name',
    description='my description',
    labels={
        'key1': 'value1',
        'key2': 'value2'
    }
)

Parameters

Name	Description
display_name	str Optional. 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.
create_request_timeout	float Optional. The timeout for the create request in seconds.

Returns

Type	Description
tensorboard (Tensorboard)	Instantiated representation of the managed tensorboard resource.

update

update(
    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,
)

Updates an existing tensorboard.

Example Usage:

tb = aiplatform.Tensorboard(tensorboard_name='123456')
tb.update(
    display_name='update my display name',
    description='update my description',
)

Parameters

Name	Description
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

Type	Description
Tensorboard	The managed tensorboard resource.