Cloud AI Platform v1beta1 API - Class CustomJobSpec (1.0.0-beta12)

Reference documentation and code samples for the Cloud AI Platform v1beta1 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.v1beta1.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.v1beta1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.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.v1beta1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.HyperparameterTuningJob.trials]).

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

ContextName-typed view over the Experiment resource name property.

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

ContextName-typed view over the ExperimentRun resource name property.

Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model}

In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.

ModelName-typed view over the Models resource name property.

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. The ID of the PersistentResource in the same Project and Location which to run

If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

PersistentResourceName-typed view over the PersistentResourceId resource name property.

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

Optional. Configuration for PSC-I for CustomJob.

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.v1beta1.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.