Class CustomJob (1.10.0) 

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

Vertex AI Custom Job.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.jobs._Job > google.cloud.aiplatform.jobs._RunnableJob > CustomJob

Properties

network

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.

Methods

CustomJob

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

Cosntruct 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

Parameters
Name Description
display_name str

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.
worker_pool_specs Union[List[Dict], List[aiplatform.gapic.WorkerPoolSpec]]

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.
base_output_dir str

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

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

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

Optional.Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.
labels Dict[str, str]

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.
encryption_spec_key_name str

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.
staging_bucket str

Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.
Exceptions
Type Description
RuntimeError If staging bucket was not set using aiplatform.init and a staging
bucke was not passed in.:

from_local_script

from_local_script(
    display_name: str,
    script_path: str,
    container_uri: str,
    args: Optional[Sequence[str]] = None,
    requirements: Optional[Sequence[str]] = None,
    environment_variables: Optional[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: Optional[str] = None,
    reduction_server_container_uri: Optional[str] = None,
    base_output_dir: Optional[str] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    labels: Optional[Dict[str, str]] = None,
    encryption_spec_key_name: Optional[str] = None,
    staging_bucket: Optional[str] = None,
)

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()
Parameters
Name Description
display_name str

Required. The user-defined name of this CustomJob.
script_path str

Required. Local path to training script.
container_uri str

Required: Uri of the training container image to use for custom job.
args Optional[Sequence[str]]

Optional. Command line arguments to be passed to the Python task.
requirements Sequence[str]

Optional. List of python packages dependencies of script.
environment_variables Dict[str, str]

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' }
replica_count int

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.
machine_type str

Optional. The type of machine to use for training.
accelerator_type str

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

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

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).
boot_disk_size_gb int

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

The number of reduction server replicas, default is 0.
reduction_server_machine_type str

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

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
base_output_dir str

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

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

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

Optional. Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.
labels Dict[str, str]

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.
encryption_spec_key_name str

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.
staging_bucket str

Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.
Exceptions
Type Description
RuntimeError If staging bucket was not set using aiplatform.init and a staging
bucke was not passed in.:

run

run(
    service_account: Optional[str] = None,
    network: Optional[str] = None,
    timeout: Optional[int] = None,
    restart_job_on_worker_restart: bool = False,
    enable_web_access: bool = False,
    tensorboard: Optional[str] = None,
    sync: bool = True,
)

Run this configured CustomJob.

Parameters
Name Description
service_account str

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

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 job is not peered with any network.
timeout int

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

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.
enable_web_access bool

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
tensorboard str

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
sync bool

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