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.

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.

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, theVertex AI Custom code service Agent for the CustomJob's project is used.

Optional. The full name of the Compute Enginenetwork to which the Job should be peered. For example,projects/12345/global/networks/myVPC.Format is of the formprojects/{project}/global/networks/{network}. Where {project} is a project number, as in12345, and {network} is a network name.

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

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

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'].

Optional. Configuration for PSC-I for 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 nameid 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 =<baseOutputDirectory>/model/
• AIP_CHECKPOINT_DIR =<baseOutputDirectory>/checkpoints/
• AIP_TENSORBOARD_LOG_DIR =<baseOutputDirectory>/logs/

For CustomJob backing a Trial of HyperparameterTuningJob:

• AIP_MODEL_DIR =<baseOutputDirectory>/<trial_id>/model/
• AIP_CHECKPOINT_DIR =<baseOutputDirectory>/<trial_id>/checkpoints/
• AIP_TENSORBOARD_LOG_DIR =<baseOutputDirectory>/<trial_id>/logs/

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. The name of a Vertex AITensorboard resource to which this CustomJob will upload Tensorboard logs. Format:projects/{project}/locations/{location}/tensorboards/{tensorboard}

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

If set totrue, you can access interactive shells at the URIs given byCustomJob.web_access_uris orTrial.web_access_uris (withinHyperparameterTuningJob.trials).

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

If set totrue, you can access the dashboard at the URIs given byCustomJob.web_access_uris orTrial.web_access_uris (withinHyperparameterTuningJob.trials).

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

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

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 orprojects/{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.

Optional. Immutable. The specification of a single machine.

Optional. The number of worker replicas to use for this worker pool.

Optional. List of NFS mount spec.

Optional. List of Lustre mounts.

Disk spec.

Required. The URI of a container image in the Artifact Registry that is to be run on each worker replica.

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

The arguments to be passed when starting the container.

Environment variables to be passed to the container. Maximum limit is 100.

Required. The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list ofpre-built containers for training. You must use an image from this list.

Required. The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

Required. The Python module name to run after installing the packages.

Command line arguments to be passed to the Python task.

Environment variables to be passed to the python module. Maximum limit is 100.

Required. IP address of the Lustre instance.

Required. The unique identifier of the Lustre volume.

Required. The name of the Lustre filesystem.

Required. Destination mount path. The Lustre file system will be mounted for the user under /mnt/lustre/

Optional. The maximum job running time. The default is 7 days.

A duration in seconds with up to nine fractional digits, ending with 's'. Example:"3.5s".

Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

Optional. This determines which type of scheduling strategy to use.

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overridesScheduling.restart_job_on_worker_restart to false.

Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.

A duration in seconds with up to nine fractional digits, ending with 's'. Example:"3.5s".