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Showing content from https://cloud.google.com/python/docs/reference/aiplatform/1.88.0/google.cloud.aiplatform.CustomJob below:

Class CustomJob (1.88.0) | Python client library

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CustomJob(
    display_name: str,
    worker_pool_specs: typing.Union[
        typing.List[typing.Dict],
        typing.List[google.cloud.aiplatform_v1.types.custom_job.WorkerPoolSpec],
    ],
    base_output_dir: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    persistent_resource_id: typing.Optional[str] = None,
)

Vertex AI Custom Job.

Time this resource was created.

Display name of this resource.

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

Time when the Job resource entered the JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, or JOB_STATE_CANCELLED state.

Detailed error info for this Job resource. Only populated when the Job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

The underlying resource proto representation.

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

Name of this resource.

The full name of the Google Compute Engine network to which this CustomJob should be peered.

Takes the format projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name.

Private services access must already be configured for the network. If left unspecified, the CustomJob is not peered with any network.

Exposes features available in preview for this class.

Full qualified resource name.

Time when the Job resource entered the JOB_STATE_RUNNING for the first time.

Fetch Job again and return the current JobState.

Time this resource was last updated.

Fetch the runnable job again and return the latest web access uris.

CustomJob(
    display_name: str,
    worker_pool_specs: typing.Union[
        typing.List[typing.Dict],
        typing.List[google.cloud.aiplatform_v1.types.custom_job.WorkerPoolSpec],
    ],
    base_output_dir: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    persistent_resource_id: typing.Optional[str] = None,
)

Constructs a Custom Job with Worker Pool Specs.

Example usage:
worker_pool_specs = [
        {
            "machine_spec": {
                "machine_type": "n1-standard-4",
                "accelerator_type": "NVIDIA_TESLA_K80",
                "accelerator_count": 1,
            },
            "replica_count": 1,
            "container_spec": {
                "image_uri": container_image_uri,
                "command": [],
                "args": [],
            },
        }
    ]

my_job = aiplatform.CustomJob(
    display_name='my_job',
    worker_pool_specs=worker_pool_specs,
    labels={'my_key': 'my_value'},
)

my_job.run()

For more information on configuring worker pool specs please visit: https://cloud.google.com/ai-platform-unified/docs/training/create-custom-job

Required. The user-defined name of the HyperparameterTuningJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

Required. The spec of the worker pools including machine type and Docker image. Can provided as a list of dictionaries or list of WorkerPoolSpec proto messages.

Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

Optional.Project to run the custom job in. Overrides project set in aiplatform.init.

Optional.Location to run the custom job in. Overrides location set in aiplatform.init.

Optional.Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.

Optional. The labels with user-defined metadata to organize CustomJobs. 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. See https://goo.gl/xmQnxf for more information and examples of labels.

Optional.Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.

Optional. The ID of the PersistentResource in the same Project and Location. 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.

Cancels this Job.

Success of cancellation is not guaranteed. Use Job.state property to verify if cancellation was successful.

delete(sync: bool = True) -> None

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

Method indicating whether a job has completed.

from_local_script(
    display_name: str,
    script_path: str,
    container_uri: str,
    enable_autolog: bool = False,
    args: typing.Optional[typing.Sequence[str]] = None,
    requirements: typing.Optional[typing.Sequence[str]] = None,
    environment_variables: typing.Optional[typing.Dict[str, str]] = None,
    replica_count: int = 1,
    machine_type: str = "n1-standard-4",
    accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
    accelerator_count: int = 0,
    boot_disk_type: str = "pd-ssd",
    boot_disk_size_gb: int = 100,
    reduction_server_replica_count: int = 0,
    reduction_server_machine_type: typing.Optional[str] = None,
    reduction_server_container_uri: typing.Optional[str] = None,
    base_output_dir: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    staging_bucket: typing.Optional[str] = None,
    persistent_resource_id: typing.Optional[str] = None,
    tpu_topology: typing.Optional[str] = None,
) -> google.cloud.aiplatform.jobs.CustomJob

Configures a custom job from a local script.

Example usage:

job = aiplatform.CustomJob.from_local_script(
    display_name="my-custom-job",
    script_path="training_script.py",
    container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest",
    requirements=["gcsfs==0.7.1"],
    replica_count=1,
    args=['--dataset', 'gs://my-bucket/my-dataset',
    '--model_output_uri', 'gs://my-bucket/model']
    labels={'my_key': 'my_value'},
)

job.run()

Required. The user-defined name of this CustomJob.

Required. Local path to training script.

Required. Uri of the training container image to use for custom job. Support images in Artifact Registry, Container Registry, or Docker Hub. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training https://cloud.google.com/vertex-ai/docs/training/pre-built-containers. If not using image from this list, please make sure python3 and pip3 are installed in your container.

Optional. If True, the Vertex Experiments autologging feature will be enabled in the CustomJob. Note that this will wrap your training script with some autologging-related code.

Optional. Command line arguments to be passed to the Python task.

Optional. List of python packages dependencies of script.

Optional. Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. environment_variables = { 'MY_KEY': 'MY_VALUE' }

Optional. The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

Optional. The type of machine to use for training.

Optional. Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

Optional. The number of accelerators to attach to a worker replica.

Optional. Type of the boot disk, default is pd-ssd. Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).

Optional. Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

The number of reduction server replicas, default is 0.

Optional. The type of machine to use for reduction server.

Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

Optional. Project to run the custom job in. Overrides project set in aiplatform.init.

Optional. Location to run the custom job in. Overrides location set in aiplatform.init.

Optional. Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.

Optional. The labels with user-defined metadata to organize CustomJobs. 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. See https://goo.gl/xmQnxf for more information and examples of labels.

Optional. Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.

Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

get(
    resource_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> google.cloud.aiplatform.jobs._RunnableJob

Get a Vertex AI Job for the given resource_name.

Required. A fully-qualified resource name or ID.

Optional. project to retrieve dataset from. If not set, project set in aiplatform.init will be used.

Optional. location to retrieve dataset from. If not set, location set in aiplatform.init will be used.

Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init.

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Job Resource.

Example Usage:

aiplatform.BatchPredictionJobs.list( filter='state="JOB_STATE_SUCCEEDED" AND display_name="my_job"', )

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name, create_time, update_time

Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.

Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

run(
    service_account: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    timeout: typing.Optional[int] = None,
    restart_job_on_worker_restart: bool = False,
    enable_web_access: bool = False,
    experiment: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
        ]
    ] = None,
    experiment_run: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str
        ]
    ] = None,
    tensorboard: typing.Optional[str] = None,
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
    disable_retries: bool = False,
    persistent_resource_id: typing.Optional[str] = None,
    scheduling_strategy: typing.Optional[
        google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy
    ] = None,
    max_wait_duration: typing.Optional[int] = None,
) -> None

Run this configured CustomJob.

Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

The maximum job running time in seconds. The default is 7 days.

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.

Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics. service_account is required with provided experiment. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when experiment is set. If 'experiment' is set but 'experiment_run` is not, an ExperimentRun resource will still be auto-generated.

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} The training script should write Tensorboard to following Vertex AI environment variable: AIP_TENSORBOARD_LOG_DIR service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future.

Optional. The timeout for the create request in seconds.

Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

Optional. Indicates the job scheduling strategy.

This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 1 day.

submit(
    *,
    service_account: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    timeout: typing.Optional[int] = None,
    restart_job_on_worker_restart: bool = False,
    enable_web_access: bool = False,
    experiment: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
        ]
    ] = None,
    experiment_run: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str
        ]
    ] = None,
    tensorboard: typing.Optional[str] = None,
    create_request_timeout: typing.Optional[float] = None,
    disable_retries: bool = False,
    persistent_resource_id: typing.Optional[str] = None,
    scheduling_strategy: typing.Optional[
        google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy
    ] = None,
    max_wait_duration: typing.Optional[int] = None
) -> None

Submit the configured CustomJob.

Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network.

The maximum job running time in seconds. The default is 7 days.

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.

Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics. service_account is required with provided experiment. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when experiment is set. If 'experiment' is set but 'experiment_run` is not, an ExperimentRun resource will still be auto-generated.

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} The training script should write Tensorboard to following Vertex AI environment variable: AIP_TENSORBOARD_LOG_DIR service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Optional. The timeout for the create request in seconds.

Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

Optional. Indicates the job scheduling strategy.

This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 1 day.

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

Helper method that blocks until all futures are complete.

wait_for_completion() -> None

Waits for job to complete.

wait_for_resource_creation() -> None

Waits until resource has been created.

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Last updated 2025-08-14 UTC.

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