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gcp_aiplatform_custom_job

ancestors

Type: UNORDERED_LIST_STRING

create_time

Type: TIMESTAMP
Provider name: createTime
Description: Output only. Time when the CustomJob was created.

encryption_spec

Type: STRUCT
Provider name: encryptionSpec
Description: Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

  • kms_key_name
    Type: STRING
    Provider name: kmsKeyName
    Description: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

end_time

Type: TIMESTAMP
Provider name: endTime
Description: Output only. Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

error

Type: STRUCT
Provider name: error
Description: Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

  • code
    Type: INT32
    Provider name: code
    Description: The status code, which should be an enum value of google.rpc.Code.
  • message
    Type: STRING
    Provider name: message
    Description: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

gcp_display_name

Type: STRING
Provider name: displayName
Description: Required. The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

job_spec

Type: STRUCT
Provider name: jobSpec
Description: Required. Job spec.

  • base_output_directory
    Type: STRUCT
    Provider name: baseOutputDirectory
    Description: 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 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 = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
    • output_uri_prefix
      Type: STRING
      Provider name: outputUriPrefix
      Description: Required. Google Cloud Storage URI to output directory. If the uri doesn’t end with ‘/’, a ‘/’ will be automatically appended. The directory is created if it doesn’t exist.
  • enable_dashboard_access
    Type: BOOLEAN
    Provider name: enableDashboardAccess
    Description: 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 or Trial.web_access_uris (within HyperparameterTuningJob.trials).
  • enable_web_access
    Type: BOOLEAN
    Provider name: enableWebAccess
    Description: 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 or Trial.web_access_uris (within HyperparameterTuningJob.trials).
  • experiment
    Type: STRING
    Provider name: experiment
    Description: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
  • experiment_run
    Type: STRING
    Provider name: experimentRun
    Description: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
  • models
    Type: UNORDERED_LIST_STRING
    Provider name: models
    Description: 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.
  • network
    Type: STRING
    Provider name: network
    Description: 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.
  • persistent_resource_id
    Type: STRING
    Provider name: persistentResourceId
    Description: 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.
  • protected_artifact_location_id
    Type: STRING
    Provider name: protectedArtifactLocationId
    Description: 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
  • reserved_ip_ranges
    Type: UNORDERED_LIST_STRING
    Provider name: reservedIpRanges
    Description: 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
    Type: STRUCT
    Provider name: scheduling
    Description: Scheduling options for a CustomJob.
    • disable_retries
      Type: BOOLEAN
      Provider name: disableRetries
      Description: Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
    • max_wait_duration
      Type: STRING
      Provider name: maxWaitDuration
      Description: 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.
    • restart_job_on_worker_restart
      Type: BOOLEAN
      Provider name: restartJobOnWorkerRestart
      Description: 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.
    • strategy
      Type: STRING
      Provider name: strategy
      Description: Optional. This determines which type of scheduling strategy to use.
      Possible values:
      • STRATEGY_UNSPECIFIED - Strategy will default to STANDARD.
      • ON_DEMAND - Deprecated. Regular on-demand provisioning strategy.
      • LOW_COST - Deprecated. Low cost by making potential use of spot resources.
      • STANDARD - Standard provisioning strategy uses regular on-demand resources.
      • SPOT - Spot provisioning strategy uses spot resources.
      • FLEX_START - Flex Start strategy uses DWS to queue for resources.
    • timeout
      Type: STRING
      Provider name: timeout
      Description: Optional. The maximum job running time. The default is 7 days.
  • service_account
    Type: STRING
    Provider name: serviceAccount
    Description: 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.
  • tensorboard
    Type: STRING
    Provider name: tensorboard
    Description: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
  • worker_pool_specs
    Type: UNORDERED_LIST_STRUCT
    Provider name: workerPoolSpecs
    Description: 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.
    • container_spec
      Type: STRUCT
      Provider name: containerSpec
      Description: The custom container task.
      • args
        Type: UNORDERED_LIST_STRING
        Provider name: args
        Description: The arguments to be passed when starting the container.
      • command
        Type: UNORDERED_LIST_STRING
        Provider name: command
        Description: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
      • env
        Type: UNORDERED_LIST_STRUCT
        Provider name: env
        Description: Environment variables to be passed to the container. Maximum limit is 100.
        • name
          Type: STRING
          Provider name: name
          Description: Required. Name of the environment variable. Must be a valid C identifier.
        • value
          Type: STRING
          Provider name: value
          Description: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
      • image_uri
        Type: STRING
        Provider name: imageUri
        Description: Required. The URI of a container image in the Container Registry that is to be run on each worker replica.
    • disk_spec
      Type: STRUCT
      Provider name: diskSpec
      Description: Disk spec.
      • boot_disk_size_gb
        Type: INT32
        Provider name: bootDiskSizeGb
        Description: Size in GB of the boot disk (default is 100GB).
      • boot_disk_type
        Type: STRING
        Provider name: bootDiskType
        Description: Type of the boot disk. For non-A3U machines, the default value is “pd-ssd”, for A3U machines, the default value is “hyperdisk-balanced”. Valid values: “pd-ssd” (Persistent Disk Solid State Drive), “pd-standard” (Persistent Disk Hard Disk Drive) or “hyperdisk-balanced”.
    • machine_spec
      Type: STRUCT
      Provider name: machineSpec
      Description: Optional. Immutable. The specification of a single machine.
      • accelerator_count
        Type: INT32
        Provider name: acceleratorCount
        Description: The number of accelerators to attach to the machine.
      • accelerator_type
        Type: STRING
        Provider name: acceleratorType
        Description: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
        Possible values:
        • ACCELERATOR_TYPE_UNSPECIFIED - Unspecified accelerator type, which means no accelerator.
        • NVIDIA_TESLA_K80 - Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol.
        • NVIDIA_TESLA_P100 - Nvidia Tesla P100 GPU.
        • NVIDIA_TESLA_V100 - Nvidia Tesla V100 GPU.
        • NVIDIA_TESLA_P4 - Nvidia Tesla P4 GPU.
        • NVIDIA_TESLA_T4 - Nvidia Tesla T4 GPU.
        • NVIDIA_TESLA_A100 - Nvidia Tesla A100 GPU.
        • NVIDIA_A100_80GB - Nvidia A100 80GB GPU.
        • NVIDIA_L4 - Nvidia L4 GPU.
        • NVIDIA_H100_80GB - Nvidia H100 80Gb GPU.
        • NVIDIA_H100_MEGA_80GB - Nvidia H100 Mega 80Gb GPU.
        • NVIDIA_H200_141GB - Nvidia H200 141Gb GPU.
        • TPU_V2 - TPU v2.
        • TPU_V3 - TPU v3.
        • TPU_V4_POD - TPU v4.
        • TPU_V5_LITEPOD - TPU v5.
      • machine_type
        Type: STRING
        Provider name: machineType
        Description: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
      • reservation_affinity
        Type: STRUCT
        Provider name: reservationAffinity
        Description: Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
        • key
          Type: STRING
          Provider name: key
          Description: Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
        • reservation_affinity_type
          Type: STRING
          Provider name: reservationAffinityType
          Description: Required. Specifies the reservation affinity type.
          Possible values:
          • TYPE_UNSPECIFIED - Default value. This should not be used.
          • NO_RESERVATION - Do not consume from any reserved capacity, only use on-demand.
          • ANY_RESERVATION - Consume any reservation available, falling back to on-demand.
          • SPECIFIC_RESERVATION - Consume from a specific reservation. When chosen, the reservation must be identified via the key and values fields.
        • values
          Type: UNORDERED_LIST_STRING
          Provider name: values
          Description: Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
      • tpu_topology
        Type: STRING
        Provider name: tpuTopology
        Description: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: “2x2x1”).
    • nfs_mounts
      Type: UNORDERED_LIST_STRUCT
      Provider name: nfsMounts
      Description: Optional. List of NFS mount spec.
      • mount_point
        Type: STRING
        Provider name: mountPoint
        Description: Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
      • path
        Type: STRING
        Provider name: path
        Description: Required. Source path exported from NFS server. Has to start with ‘/’, and combined with the ip address, it indicates the source mount path in the form of server:path
      • server
        Type: STRING
        Provider name: server
        Description: Required. IP address of the NFS server.
    • python_package_spec
      Type: STRUCT
      Provider name: pythonPackageSpec
      Description: The Python packaged task.
      • args
        Type: UNORDERED_LIST_STRING
        Provider name: args
        Description: Command line arguments to be passed to the Python task.
      • env
        Type: UNORDERED_LIST_STRUCT
        Provider name: env
        Description: Environment variables to be passed to the python module. Maximum limit is 100.
        • name
          Type: STRING
          Provider name: name
          Description: Required. Name of the environment variable. Must be a valid C identifier.
        • value
          Type: STRING
          Provider name: value
          Description: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
      • executor_image_uri
        Type: STRING
        Provider name: executorImageUri
        Description: 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 of pre-built containers for training. You must use an image from this list.
      • package_uris
        Type: UNORDERED_LIST_STRING
        Provider name: packageUris
        Description: 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.
      • python_module
        Type: STRING
        Provider name: pythonModule
        Description: Required. The Python module name to run after installing the packages.
    • replica_count
      Type: INT64
      Provider name: replicaCount
      Description: Optional. The number of worker replicas to use for this worker pool.

labels

Type: UNORDERED_LIST_STRING

name

Type: STRING
Provider name: name
Description: Output only. Resource name of a CustomJob.

organization_id

Type: STRING

parent

Type: STRING

project_id

Type: STRING

project_number

Type: STRING

resource_name

Type: STRING

satisfies_pzi

Type: BOOLEAN
Provider name: satisfiesPzi
Description: Output only. Reserved for future use.

satisfies_pzs

Type: BOOLEAN
Provider name: satisfiesPzs
Description: Output only. Reserved for future use.

start_time

Type: TIMESTAMP
Provider name: startTime
Description: Output only. Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

state

Type: STRING
Provider name: state
Description: Output only. The detailed state of the job.
Possible values:

  • JOB_STATE_UNSPECIFIED - The job state is unspecified.
  • JOB_STATE_QUEUED - The job has been just created or resumed and processing has not yet begun.
  • JOB_STATE_PENDING - The service is preparing to run the job.
  • JOB_STATE_RUNNING - The job is in progress.
  • JOB_STATE_SUCCEEDED - The job completed successfully.
  • JOB_STATE_FAILED - The job failed.
  • JOB_STATE_CANCELLING - The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
  • JOB_STATE_CANCELLED - The job has been cancelled.
  • JOB_STATE_PAUSED - The job has been stopped, and can be resumed.
  • JOB_STATE_EXPIRED - The job has expired.
  • JOB_STATE_UPDATING - The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
  • JOB_STATE_PARTIALLY_SUCCEEDED - The job is partially succeeded, some results may be missing due to errors.

tags

Type: UNORDERED_LIST_STRING

update_time

Type: TIMESTAMP
Provider name: updateTime
Description: Output only. Time when the CustomJob was most recently updated.