Package vertexai (1.35.0)

Version 1.35.0

• 1.71.1 (latest)
• 1.71.0
• 1.70.0
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• 1.68.0
• 1.67.1
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• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
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• 1.30.1
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• 0.3.1

API documentation for vertexai package.

Packages

language_models

API documentation for language_models package.

preview

API documentation for preview package.

vision_models

API documentation for vision_models package.

Packages Functions

init

init(
    *,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    experiment: typing.Optional[str] = None,
    experiment_description: typing.Optional[str] = None,
    experiment_tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
    staging_bucket: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    service_account: typing.Optional[str] = None
)

Updates common initialization parameters with provided options.

Parameters
Name	Description
project	The default project to use when making API calls.
location	The default location to use when making API calls. If not set defaults to us-central-1.
experiment	Optional. The experiment name.
experiment_description	Optional. The description of the experiment.
experiment_tensorboard	Optional. The Vertex AI TensorBoard instance, Tensorboard resource name, or Tensorboard resource ID to use as a backing Tensorboard for the provided experiment. Example tensorboard resource name format: "projects/123/locations/us-central1/tensorboards/456" If experiment_tensorboard is provided and experiment is not, the provided experiment_tensorboard will be set as the global Tensorboard. Any subsequent calls to aiplatform.init() with experiment and without experiment_tensorboard will automatically assign the global Tensorboard to the experiment.
staging_bucket	The default staging bucket to use to stage artifacts when making API calls. In the form gs://...
credentials	The default custom credentials to use when making API calls. If not provided credentials will be ascertained from the environment.
encryption_spec_key_name	Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. 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 resource and all sub-resources will be secured by this key.
network	Optional. The full name of the Compute Engine network to which jobs and resources should be peered. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. If specified, all eligible jobs and resources created will be peered with this VPC.
service_account	Optional. The service account used to launch jobs and deploy models. Jobs that use service_account: BatchPredictionJob, CustomJob, PipelineJob, HyperparameterTuningJob, CustomTrainingJob, CustomPythonPackageTrainingJob, CustomContainerTrainingJob, ModelEvaluationJob.