Cloud AI Platform v1 API - Class CustomJobSpec (2.12.0)

Reference documentation and code samples for the Cloud AI Platform v1 API class CustomJobSpec.

Represents the spec of a CustomJob.

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name [id][google.cloud.aiplatform.v1.Trial.id] under its parent HyperparameterTuningJob's baseOutputDirectory.

The following Vertex AI environment variables will be passed to containers or python modules when this field is set:

For CustomJob:

• AIP_MODEL_DIR = <base_output_directory>/model/
• AIP_CHECKPOINT_DIR = <base_output_directory>/checkpoints/

• AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/logs/

  For CustomJob backing a Trial of HyperparameterTuningJob:

• AIP_MODEL_DIR = <base_output_directory>/<trial_id>/model/

• AIP_CHECKPOINT_DIR = <base_output_directory>/<trial_id>/checkpoints/
• AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/<trial_id>/logs/

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container.

If set to true, you can access the dashboard at the URIs given by [CustomJob.web_access_uris][google.cloud.aiplatform.v1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1.HyperparameterTuningJob.trials]).

Optional. Whether you want Vertex AI to enable interactive shell access to training containers.

If set to true, you can access interactive shells at the URIs given by [CustomJob.web_access_uris][google.cloud.aiplatform.v1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1.HyperparameterTuningJob.trials]).

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name.

To specify this field, you must have already configured VPC Network Peering for Vertex AI.

If this field is left unspecified, the job is not peered with any network.

NetworkName-typed view over the Network resource name property.

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job.

If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network.

Example: ['vertex-ai-ip-range'].

Scheduling options for a CustomJob.

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

TensorboardName-typed view over the Tensorboard resource name property.

Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.