AI Platform Training & Prediction API . projects . jobs

cancel(name, body=None, x__xgafv=None)

Cancels a running job.

Args:
  name: string, Required. The name of the job to cancel. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for the CancelJob method.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}

close()

Close httplib2 connections.

create(parent, body=None, x__xgafv=None)

Creates a training or a batch prediction job.

Args:
  parent: string, Required. The project name. (required)
  body: object, The request body.
    The object takes the form of:

{ # Represents a training or prediction job.
  "createTime": "A String", # Output only. When the job was created.
  "endTime": "A String", # Output only. When the job processing was completed.
  "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
  "jobId": "A String", # Required. The user-specified id of the job.
  "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    "a_key": "A String",
  },
  "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
    "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
    "dataFormat": "A String", # Required. The format of the input data files.
    "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
      "A String",
    ],
    "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
    "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
    "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
    "outputPath": "A String", # Required. The output Google Cloud Storage location.
    "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
    "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
    "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
    "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
  },
  "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
    "errorCount": "A String", # The number of data instances which resulted in errors.
    "nodeHours": 3.14, # Node hours used by the batch prediction job.
    "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
    "predictionCount": "A String", # The number of generated predictions.
  },
  "startTime": "A String", # Output only. When the job processing was started.
  "state": "A String", # Output only. The detailed state of a job.
  "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
    "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
      "A String",
    ],
    "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
    "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
      "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
    },
    "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
      "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
      "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
      "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
      "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
      "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
      "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
      "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
      "params": [ # Required. The set of parameters to tune.
        { # Represents a single hyperparameter to optimize.
          "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
            "A String",
          ],
          "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
            3.14,
          ],
          "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
          "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
          "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
          "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
          "type": "A String", # Required. The type of the parameter.
        },
      ],
      "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
    },
    "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
    "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
      "A String",
    ],
    "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
    "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
    "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
      "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
      "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
      "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
    },
    "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
  },
  "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
    "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
      "framework": "A String", # Framework on which the built-in algorithm was trained.
      "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
      "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
      "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
    },
    "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
    "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
    "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
    "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
    "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
    "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
      { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
        "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
          { # An observed value of a metric.
            "objectiveValue": 3.14, # The objective value at this training step.
            "trainingStep": "A String", # The global training step for this metric.
          },
        ],
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "endTime": "A String", # Output only. End time for the trial.
        "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
          "objectiveValue": 3.14, # The objective value at this training step.
          "trainingStep": "A String", # The global training step for this metric.
        },
        "hyperparameters": { # The hyperparameters given to this trial.
          "a_key": "A String",
        },
        "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
        "startTime": "A String", # Output only. Start time for the trial.
        "state": "A String", # Output only. The detailed state of the trial.
        "trialId": "A String", # The trial id for these results.
        "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    ],
    "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
      "a_key": "A String",
    },
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a training or prediction job.
  "createTime": "A String", # Output only. When the job was created.
  "endTime": "A String", # Output only. When the job processing was completed.
  "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
  "jobId": "A String", # Required. The user-specified id of the job.
  "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    "a_key": "A String",
  },
  "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
    "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
    "dataFormat": "A String", # Required. The format of the input data files.
    "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
      "A String",
    ],
    "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
    "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
    "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
    "outputPath": "A String", # Required. The output Google Cloud Storage location.
    "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
    "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
    "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
    "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
  },
  "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
    "errorCount": "A String", # The number of data instances which resulted in errors.
    "nodeHours": 3.14, # Node hours used by the batch prediction job.
    "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
    "predictionCount": "A String", # The number of generated predictions.
  },
  "startTime": "A String", # Output only. When the job processing was started.
  "state": "A String", # Output only. The detailed state of a job.
  "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
    "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
      "A String",
    ],
    "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
    "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
      "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
    },
    "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
      "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
      "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
      "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
      "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
      "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
      "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
      "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
      "params": [ # Required. The set of parameters to tune.
        { # Represents a single hyperparameter to optimize.
          "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
            "A String",
          ],
          "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
            3.14,
          ],
          "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
          "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
          "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
          "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
          "type": "A String", # Required. The type of the parameter.
        },
      ],
      "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
    },
    "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
    "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
      "A String",
    ],
    "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
    "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
    "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
      "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
      "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
      "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
    },
    "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
  },
  "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
    "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
      "framework": "A String", # Framework on which the built-in algorithm was trained.
      "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
      "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
      "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
    },
    "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
    "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
    "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
    "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
    "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
    "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
      { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
        "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
          { # An observed value of a metric.
            "objectiveValue": 3.14, # The objective value at this training step.
            "trainingStep": "A String", # The global training step for this metric.
          },
        ],
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "endTime": "A String", # Output only. End time for the trial.
        "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
          "objectiveValue": 3.14, # The objective value at this training step.
          "trainingStep": "A String", # The global training step for this metric.
        },
        "hyperparameters": { # The hyperparameters given to this trial.
          "a_key": "A String",
        },
        "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
        "startTime": "A String", # Output only. Start time for the trial.
        "state": "A String", # Output only. The detailed state of the trial.
        "trialId": "A String", # The trial id for these results.
        "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    ],
    "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
      "a_key": "A String",
    },
  },
}

get(name, x__xgafv=None)

Describes a job.

Args:
  name: string, Required. The name of the job to get the description of. (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a training or prediction job.
  "createTime": "A String", # Output only. When the job was created.
  "endTime": "A String", # Output only. When the job processing was completed.
  "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
  "jobId": "A String", # Required. The user-specified id of the job.
  "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    "a_key": "A String",
  },
  "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
    "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
    "dataFormat": "A String", # Required. The format of the input data files.
    "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
      "A String",
    ],
    "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
    "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
    "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
    "outputPath": "A String", # Required. The output Google Cloud Storage location.
    "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
    "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
    "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
    "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
  },
  "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
    "errorCount": "A String", # The number of data instances which resulted in errors.
    "nodeHours": 3.14, # Node hours used by the batch prediction job.
    "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
    "predictionCount": "A String", # The number of generated predictions.
  },
  "startTime": "A String", # Output only. When the job processing was started.
  "state": "A String", # Output only. The detailed state of a job.
  "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
    "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
      "A String",
    ],
    "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
    "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
      "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
    },
    "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
      "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
      "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
      "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
      "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
      "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
      "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
      "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
      "params": [ # Required. The set of parameters to tune.
        { # Represents a single hyperparameter to optimize.
          "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
            "A String",
          ],
          "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
            3.14,
          ],
          "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
          "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
          "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
          "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
          "type": "A String", # Required. The type of the parameter.
        },
      ],
      "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
    },
    "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
    "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
      "A String",
    ],
    "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
    "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
    "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
      "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
      "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
      "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
    },
    "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
  },
  "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
    "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
      "framework": "A String", # Framework on which the built-in algorithm was trained.
      "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
      "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
      "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
    },
    "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
    "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
    "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
    "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
    "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
    "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
      { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
        "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
          { # An observed value of a metric.
            "objectiveValue": 3.14, # The objective value at this training step.
            "trainingStep": "A String", # The global training step for this metric.
          },
        ],
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "endTime": "A String", # Output only. End time for the trial.
        "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
          "objectiveValue": 3.14, # The objective value at this training step.
          "trainingStep": "A String", # The global training step for this metric.
        },
        "hyperparameters": { # The hyperparameters given to this trial.
          "a_key": "A String",
        },
        "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
        "startTime": "A String", # Output only. Start time for the trial.
        "state": "A String", # Output only. The detailed state of the trial.
        "trialId": "A String", # The trial id for these results.
        "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    ],
    "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
      "a_key": "A String",
    },
  },
}

getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

Args:
  resource: string, REQUIRED: The resource for which the policy is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  options_requestedPolicyVersion: integer, Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members`, or principals, to a single `role`. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions; each `role` can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a `binding` can also specify a `condition`, which is a logical expression that allows access to a resource only if the expression evaluates to `true`. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). **JSON example:** ``` { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } ``` **YAML example:** ``` bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the [IAM documentation](https://cloud.google.com/iam/docs/).
  "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
    { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs. If there are AuditConfigs for both `allServices` and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted. Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts `jose@example.com` from DATA_READ logging, and `aliya@example.com` from DATA_WRITE logging.
      "auditLogConfigs": [ # The configuration for logging of each type of permission.
        { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
          "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
            "A String",
          ],
          "logType": "A String", # The log type that this config enables.
        },
      ],
      "service": "A String", # Specifies a service that will be enabled for audit logging. For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. `allServices` is a special value that covers all services.
    },
  ],
  "bindings": [ # Associates a list of `members`, or principals, with a `role`. Optionally, may specify a `condition` that determines how and when the `bindings` are applied. Each of the `bindings` must contain at least one principal. The `bindings` in a `Policy` can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the `bindings` grant 50 different roles to `user:alice@example.com`, and not to any other principal, then you can add another 1,450 principals to the `bindings` in the `Policy`.
    { # Associates `members`, or principals, with a `role`.
      "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
        "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
        "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
        "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
        "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
      },
      "members": [ # Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other-app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, `my-project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All identities in a workload identity pool with a certain attribute. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*`: All identities in a workload identity pool. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. * `deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.
        "A String",
      ],
      "role": "A String", # Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. For an overview of the IAM roles and permissions, see the [IAM documentation](https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see [here](https://cloud.google.com/iam/docs/understanding-roles).
    },
  ],
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An `etag` is returned in the response to `getIamPolicy`, and systems are expected to put that etag in the request to `setIamPolicy` to ensure that their change will be applied to the same version of the policy. **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost.
  "version": 42, # Specifies the format of the policy. Valid values are `0`, `1`, and `3`. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version `3`. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
}

list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)

Lists the jobs in the project. If there are no jobs that match the request parameters, the list request returns an empty response body: {}.

Args:
  parent: string, Required. The name of the project for which to list jobs. (required)
  filter: string, Optional. Specifies the subset of jobs to retrieve. You can filter on the value of one or more attributes of the job object. For example, retrieve jobs with a job identifier that starts with 'census': gcloud ai-platform jobs list --filter='jobId:census*' List all failed jobs with names that start with 'rnn': gcloud ai-platform jobs list --filter='jobId:rnn* AND state:FAILED' For more examples, see the guide to monitoring jobs.
  pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there are more remaining results than this number, the response message will contain a valid value in the `next_page_token` field. The default value is 20, and the maximum page size is 100.
  pageToken: string, Optional. A page token to request the next page of results. You get the token from the `next_page_token` field of the response from the previous call.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for the ListJobs method.
  "jobs": [ # The list of jobs.
    { # Represents a training or prediction job.
      "createTime": "A String", # Output only. When the job was created.
      "endTime": "A String", # Output only. When the job processing was completed.
      "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
      "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
      "jobId": "A String", # Required. The user-specified id of the job.
      "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
      "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
        "a_key": "A String",
      },
      "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
        "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
        "dataFormat": "A String", # Required. The format of the input data files.
        "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
          "A String",
        ],
        "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
        "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
        "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
        "outputPath": "A String", # Required. The output Google Cloud Storage location.
        "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
        "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
        "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
        "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
        "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
      },
      "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
        "errorCount": "A String", # The number of data instances which resulted in errors.
        "nodeHours": 3.14, # Node hours used by the batch prediction job.
        "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
        "predictionCount": "A String", # The number of generated predictions.
      },
      "startTime": "A String", # Output only. When the job processing was started.
      "state": "A String", # Output only. The detailed state of a job.
      "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
        "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
          "A String",
        ],
        "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
        "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
          "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
        },
        "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
            "count": "A String", # The number of accelerators to attach to each machine running the job.
            "type": "A String", # The type of accelerator to use.
          },
          "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
            "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
            "bootDiskType": "A String", # 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).
          },
          "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
        },
        "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
        "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
        "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
          "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
          "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
          "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
          "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
          "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
          "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
          "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
          "params": [ # Required. The set of parameters to tune.
            { # Represents a single hyperparameter to optimize.
              "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
                "A String",
              ],
              "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
                3.14,
              ],
              "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
              "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
              "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
              "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
              "type": "A String", # Required. The type of the parameter.
            },
          ],
          "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
        },
        "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
        "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
            "count": "A String", # The number of accelerators to attach to each machine running the job.
            "type": "A String", # The type of accelerator to use.
          },
          "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
            "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
            "bootDiskType": "A String", # 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).
          },
          "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
        },
        "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
        "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
        "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
          "A String",
        ],
        "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
            "count": "A String", # The number of accelerators to attach to each machine running the job.
            "type": "A String", # The type of accelerator to use.
          },
          "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
            "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
            "bootDiskType": "A String", # 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).
          },
          "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
        },
        "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
        "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
        "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
        "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
        "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
        "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
        "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
        "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
          "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
          "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
          "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
        },
        "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
        "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
        "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
            "count": "A String", # The number of accelerators to attach to each machine running the job.
            "type": "A String", # The type of accelerator to use.
          },
          "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
            "A String",
          ],
          "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
            "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
            "bootDiskType": "A String", # 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).
          },
          "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
          "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
        },
        "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
        "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
      },
      "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
        "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
        "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
        "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
        "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
        "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
          { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
            "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
              { # An observed value of a metric.
                "objectiveValue": 3.14, # The objective value at this training step.
                "trainingStep": "A String", # The global training step for this metric.
              },
            ],
            "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
              "framework": "A String", # Framework on which the built-in algorithm was trained.
              "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
              "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
              "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
            },
            "endTime": "A String", # Output only. End time for the trial.
            "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
              "objectiveValue": 3.14, # The objective value at this training step.
              "trainingStep": "A String", # The global training step for this metric.
            },
            "hyperparameters": { # The hyperparameters given to this trial.
              "a_key": "A String",
            },
            "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
            "startTime": "A String", # Output only. Start time for the trial.
            "state": "A String", # Output only. The detailed state of the trial.
            "trialId": "A String", # The trial id for these results.
            "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
              "a_key": "A String",
            },
          },
        ],
        "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    },
  ],
  "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a subsequent call.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.
        

patch(name, body=None, updateMask=None, x__xgafv=None)

Updates a specific job resource. Currently the only supported fields to update are `labels`.

Args:
  name: string, Required. The job name. (required)
  body: object, The request body.
    The object takes the form of:

{ # Represents a training or prediction job.
  "createTime": "A String", # Output only. When the job was created.
  "endTime": "A String", # Output only. When the job processing was completed.
  "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
  "jobId": "A String", # Required. The user-specified id of the job.
  "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    "a_key": "A String",
  },
  "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
    "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
    "dataFormat": "A String", # Required. The format of the input data files.
    "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
      "A String",
    ],
    "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
    "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
    "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
    "outputPath": "A String", # Required. The output Google Cloud Storage location.
    "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
    "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
    "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
    "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
  },
  "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
    "errorCount": "A String", # The number of data instances which resulted in errors.
    "nodeHours": 3.14, # Node hours used by the batch prediction job.
    "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
    "predictionCount": "A String", # The number of generated predictions.
  },
  "startTime": "A String", # Output only. When the job processing was started.
  "state": "A String", # Output only. The detailed state of a job.
  "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
    "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
      "A String",
    ],
    "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
    "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
      "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
    },
    "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
      "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
      "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
      "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
      "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
      "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
      "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
      "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
      "params": [ # Required. The set of parameters to tune.
        { # Represents a single hyperparameter to optimize.
          "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
            "A String",
          ],
          "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
            3.14,
          ],
          "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
          "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
          "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
          "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
          "type": "A String", # Required. The type of the parameter.
        },
      ],
      "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
    },
    "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
    "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
      "A String",
    ],
    "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
    "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
    "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
      "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
      "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
      "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
    },
    "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
  },
  "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
    "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
      "framework": "A String", # Framework on which the built-in algorithm was trained.
      "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
      "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
      "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
    },
    "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
    "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
    "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
    "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
    "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
    "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
      { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
        "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
          { # An observed value of a metric.
            "objectiveValue": 3.14, # The objective value at this training step.
            "trainingStep": "A String", # The global training step for this metric.
          },
        ],
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "endTime": "A String", # Output only. End time for the trial.
        "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
          "objectiveValue": 3.14, # The objective value at this training step.
          "trainingStep": "A String", # The global training step for this metric.
        },
        "hyperparameters": { # The hyperparameters given to this trial.
          "a_key": "A String",
        },
        "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
        "startTime": "A String", # Output only. Start time for the trial.
        "state": "A String", # Output only. The detailed state of the trial.
        "trialId": "A String", # The trial id for these results.
        "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    ],
    "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
      "a_key": "A String",
    },
  },
}

  updateMask: string, Required. Specifies the path, relative to `Job`, of the field to update. To adopt etag mechanism, include `etag` field in the mask, and include the `etag` value in your job resource. For example, to change the labels of a job, the `update_mask` parameter would be specified as `labels`, `etag`, and the `PATCH` request body would specify the new value, as follows: { "labels": { "owner": "Google", "color": "Blue" } "etag": "33a64df551425fcc55e4d42a148795d9f25f89d4" } If `etag` matches the one on the server, the labels of the job will be replaced with the given ones, and the server end `etag` will be recalculated. Currently the only supported update masks are `labels` and `etag`.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a training or prediction job.
  "createTime": "A String", # Output only. When the job was created.
  "endTime": "A String", # Output only. When the job processing was completed.
  "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform job updates in order to avoid race conditions: An `etag` is returned in the response to `GetJob`, and systems are expected to put that etag in the request to `UpdateJob` to ensure that their change will be applied to the same version of the job.
  "jobId": "A String", # Required. The user-specified id of the job.
  "jobPosition": "A String", # Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  "labels": { # Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    "a_key": "A String",
  },
  "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
    "batchSize": "A String", # Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
    "dataFormat": "A String", # Required. The format of the input data files.
    "inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain wildcards.
      "A String",
    ],
    "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
    "modelName": "A String", # Use this field if you want to use the default version for the specified model. The string must use the following format: `"projects/YOUR_PROJECT/models/YOUR_MODEL"`
    "outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
    "outputPath": "A String", # Required. The output Google Cloud Storage location.
    "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
    "signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to [SavedModel](https://tensorflow.github.io/serving/serving_basic.html) for information about how to use signatures. Defaults to [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants) , which is "serving_default".
    "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for the model to use.
    "versionName": "A String", # Use this field if you want to specify a version of the model to use. The string is formatted the same way as `model_version`, with the addition of the version information: `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
  },
  "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
    "errorCount": "A String", # The number of data instances which resulted in errors.
    "nodeHours": 3.14, # Node hours used by the batch prediction job.
    "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
    "predictionCount": "A String", # The number of generated predictions.
  },
  "startTime": "A String", # Output only. When the job processing was started.
  "state": "A String", # Output only. The detailed state of a job.
  "trainingInput": { # Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to [submitting a training job](/ai-platform/training/docs/training-jobs). # Input parameters to create a training job.
    "args": [ # Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's `ENTRYPOINT` command.
      "A String",
    ],
    "enableWebAccess": True or False, # Optional. Whether you want AI Platform Training to enable [interactive shell access](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) to training containers. If set to `true`, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
    "encryptionConfig": { # Represents a custom encryption key configuration that can be applied to a resource. # Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. [Learn how and when to use CMEK with AI Platform Training](/ai-platform/training/docs/cmek).
      "kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
    },
    "evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators. You should only set `evaluatorConfig.acceleratorConfig` if `evaluatorType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `evaluatorConfig.imageUri` only if you build a custom image for your evaluator. If `evaluatorConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in `evaluator_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `evaluator_type`. The default value is zero.
    "evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater than zero.
    "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
      "algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
      "enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
      "goal": "A String", # Required. The type of goal to use for tuning. Available types are `MAXIMIZE` and `MINIMIZE`. Defaults to `MAXIMIZE`.
      "hyperparameterMetricTag": "A String", # Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
      "maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
      "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
      "maxTrials": 42, # Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
      "params": [ # Required. The set of parameters to tune.
        { # Represents a single hyperparameter to optimize.
          "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
            "A String",
          ],
          "discreteValues": [ # Required if type is `DISCRETE`. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
            3.14,
          ],
          "maxValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is `INTEGER`.
          "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field should be unset if type is `CATEGORICAL`. This value should be integers if type is INTEGER.
          "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
          "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., `UNIT_LINEAR_SCALE`).
          "type": "A String", # Required. The type of the parameter.
        },
      ],
      "resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.
    },
    "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
    "masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker. You should only set `masterConfig.acceleratorConfig` if `masterType` is set to a Compute Engine machine type. Learn about [restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `masterConfig.imageUri` only if you build a custom image. Only one of `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. See the [list of compatible Compute Engine machine types](/ai-platform/training/docs/machine-types#compute-engine-machine-types). Alternatively, you can use the certain legacy machine types in this field. See the [list of legacy machine types](/ai-platform/training/docs/machine-types#legacy-machine-types). Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPUs](/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
    "network": "A String", # Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to which the Job is peered. For example, `projects/12345/global/networks/myVPC`. The format of this field is `projects/{project}/global/networks/{network}`, where {project} is a project number (like `12345`) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. [Learn about using VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
    "packageUris": [ # Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
      "A String",
    ],
    "parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers. You should only set `parameterServerConfig.acceleratorConfig` if `parameterServerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `parameterServerConfig.imageUri` only if you build a custom image for your parameter server. If `parameterServerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in `parameter_server_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `parameter_server_type`. The default value is zero.
    "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for `master_type`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when `scaleTier` is set to `CUSTOM` and `parameter_server_count` is greater than zero.
    "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
    "pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify this field or specify `masterConfig.imageUri`. The following Python versions are available: * Python '3.7' is available when `runtime_version` is set to '1.15' or later. * Python '3.5' is available when `runtime_version` is set to a version from '1.4' to '1.14'. * Python '2.7' is available when `runtime_version` is set to '1.15' or earlier. Read more about the Python versions available for [each runtime version](/ml-engine/docs/runtime-version-list).
    "region": "A String", # Required. The region to run the training job in. See the [available regions](/ai-platform/training/docs/regions) for AI Platform Training.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must either specify this field or specify `masterConfig.imageUri`. For more information, see the [runtime version list](/ai-platform/training/docs/runtime-version-list) and learn [how to manage runtime versions](/ai-platform/training/docs/versioning).
    "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers and parameter servers.
    "scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
      "maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, this field defaults to `604800s` (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the `RUNNING` state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxRunningTime: 7200s ```
      "maxWaitTime": "A String", # Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by `s`. If not specified, there is no limit to the wait time. The minimum for this field is `1800s` (30 minutes). If the training job has not entered the `RUNNING` state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts), this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the `gcloud` tool, you can [specify this field in a `config.yaml` file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters). For example: ```yaml trainingInput: scheduling: maxWaitTime: 3600s ```
      "priority": 42, # Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.
    },
    "serviceAccount": "A String", # Optional. The email address of a service account to use when running the training appplication. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the `roles/iam.serviceAccountAdmin` role for the specified service account. [Learn more about configuring a service account.](/ai-platform/training/docs/custom-service-account) If not specified, the AI Platform Training Google-managed service account is used by default.
    "useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment variable when training with a custom container. Defaults to `false`. [Learn more about this field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master) This field has no effect for training jobs that don't use a custom container.
    "workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers. You should only set `workerConfig.acceleratorConfig` if `workerType` is set to a Compute Engine machine type. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu) Set `workerConfig.imageUri` only if you build a custom image for your worker. If `workerConfig.imageUri` has not been set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "acceleratorConfig": { # Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for online prediction](/ml-engine/docs/machine-types-online-prediction#gpus). # Represents the type and number of accelerators used by the replica. [Learn about restrictions on accelerator configurations for training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
        "count": "A String", # The number of accelerators to attach to each machine running the job.
        "type": "A String", # The type of accelerator to use.
      },
      "containerArgs": [ # Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "containerCommand": [ # The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
        "A String",
      ],
      "diskConfig": { # Represents the config of disk options. # Represents the configuration of disk options.
        "bootDiskSizeGb": 42, # Size in GB of the boot disk (default is 100GB).
        "bootDiskType": "A String", # 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).
      },
      "imageUri": "A String", # The Docker image to run on the replica. This image must be in Container Registry. Learn more about [configuring custom containers](/ai-platform/training/docs/distributed-training-containers).
      "tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a [runtime version that currently supports training with TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different [patch version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20). In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow `1.x.y`, specify `1.x`.
    },
    "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in `worker_type`. This value can only be used when `scale_tier` is set to `CUSTOM`. If you set this value, you must also set `worker_type`. The default value is zero.
    "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for `masterType`. This value must be consistent with the category of machine type that `masterType` uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use `cloud_tpu` for this value, see special instructions for [configuring a custom TPU machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine). This value must be present when `scaleTier` is set to `CUSTOM` and `workerCount` is greater than zero.
  },
  "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
    "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
      "framework": "A String", # Framework on which the built-in algorithm was trained.
      "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
      "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
      "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
    },
    "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
    "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
    "hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag) for more information. Only set for hyperparameter tuning jobs.
    "isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
    "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
    "trials": [ # Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
      { # Represents the result of a single hyperparameter tuning trial from a training job. The TrainingOutput object that is returned on successful completion of a training job with hyperparameter tuning includes a list of HyperparameterOutput objects, one for each successful trial.
        "allMetrics": [ # All recorded object metrics for this trial. This field is not currently populated.
          { # An observed value of a metric.
            "objectiveValue": 3.14, # The objective value at this training step.
            "trainingStep": "A String", # The global training step for this metric.
          },
        ],
        "builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
          "framework": "A String", # Framework on which the built-in algorithm was trained.
          "modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
          "pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
          "runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was trained.
        },
        "endTime": "A String", # Output only. End time for the trial.
        "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
          "objectiveValue": 3.14, # The objective value at this training step.
          "trainingStep": "A String", # The global training step for this metric.
        },
        "hyperparameters": { # The hyperparameters given to this trial.
          "a_key": "A String",
        },
        "isTrialStoppedEarly": True or False, # True if the trial is stopped early.
        "startTime": "A String", # Output only. Start time for the trial.
        "state": "A String", # Output only. The detailed state of the trial.
        "trialId": "A String", # The trial id for these results.
        "webAccessUris": { # URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
          "a_key": "A String",
        },
      },
    ],
    "webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell) (one URI for each training node). Only available if training_input.enable_web_access is `true`. The keys are names of each node in the training job; for example, `master-replica-0` for the master node, `worker-replica-0` for the first worker, and `ps-replica-0` for the first parameter server. The values are the URIs for each node's interactive shell.
      "a_key": "A String",
    },
  },
}

setIamPolicy(resource, body=None, x__xgafv=None)

Sets the access control policy on the specified resource. Replaces any existing policy. Can return `NOT_FOUND`, `INVALID_ARGUMENT`, and `PERMISSION_DENIED` errors.

Args:
  resource: string, REQUIRED: The resource for which the policy is being specified. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for `SetIamPolicy` method.
  "policy": { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members`, or principals, to a single `role`. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions; each `role` can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a `binding` can also specify a `condition`, which is a logical expression that allows access to a resource only if the expression evaluates to `true`. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). **JSON example:** ``` { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } ``` **YAML example:** ``` bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the [IAM documentation](https://cloud.google.com/iam/docs/). # REQUIRED: The complete policy to be applied to the `resource`. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.
    "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
      { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs. If there are AuditConfigs for both `allServices` and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted. Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts `jose@example.com` from DATA_READ logging, and `aliya@example.com` from DATA_WRITE logging.
        "auditLogConfigs": [ # The configuration for logging of each type of permission.
          { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
            "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
              "A String",
            ],
            "logType": "A String", # The log type that this config enables.
          },
        ],
        "service": "A String", # Specifies a service that will be enabled for audit logging. For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. `allServices` is a special value that covers all services.
      },
    ],
    "bindings": [ # Associates a list of `members`, or principals, with a `role`. Optionally, may specify a `condition` that determines how and when the `bindings` are applied. Each of the `bindings` must contain at least one principal. The `bindings` in a `Policy` can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the `bindings` grant 50 different roles to `user:alice@example.com`, and not to any other principal, then you can add another 1,450 principals to the `bindings` in the `Policy`.
      { # Associates `members`, or principals, with a `role`.
        "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
          "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
          "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
          "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
          "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
        },
        "members": [ # Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other-app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, `my-project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All identities in a workload identity pool with a certain attribute. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*`: All identities in a workload identity pool. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. * `deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.
          "A String",
        ],
        "role": "A String", # Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. For an overview of the IAM roles and permissions, see the [IAM documentation](https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see [here](https://cloud.google.com/iam/docs/understanding-roles).
      },
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An `etag` is returned in the response to `getIamPolicy`, and systems are expected to put that etag in the request to `setIamPolicy` to ensure that their change will be applied to the same version of the policy. **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost.
    "version": 42, # Specifies the format of the policy. Valid values are `0`, `1`, and `3`. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version `3`. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
  },
  "updateMask": "A String", # OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only the fields in the mask will be modified. If no mask is provided, the following default mask is used: `paths: "bindings, etag"`
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members`, or principals, to a single `role`. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions; each `role` can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a `binding` can also specify a `condition`, which is a logical expression that allows access to a resource only if the expression evaluates to `true`. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). **JSON example:** ``` { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } ``` **YAML example:** ``` bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the [IAM documentation](https://cloud.google.com/iam/docs/).
  "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
    { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs. If there are AuditConfigs for both `allServices` and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted. Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts `jose@example.com` from DATA_READ logging, and `aliya@example.com` from DATA_WRITE logging.
      "auditLogConfigs": [ # The configuration for logging of each type of permission.
        { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
          "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
            "A String",
          ],
          "logType": "A String", # The log type that this config enables.
        },
      ],
      "service": "A String", # Specifies a service that will be enabled for audit logging. For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. `allServices` is a special value that covers all services.
    },
  ],
  "bindings": [ # Associates a list of `members`, or principals, with a `role`. Optionally, may specify a `condition` that determines how and when the `bindings` are applied. Each of the `bindings` must contain at least one principal. The `bindings` in a `Policy` can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the `bindings` grant 50 different roles to `user:alice@example.com`, and not to any other principal, then you can add another 1,450 principals to the `bindings` in the `Policy`.
    { # Associates `members`, or principals, with a `role`.
      "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
        "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
        "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
        "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
        "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
      },
      "members": [ # Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other-app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, `my-project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workforce identity pool. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}`: All workforce identities in a group. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All workforce identities with a specific attribute value. * `principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*`: All identities in a workforce identity pool. * `principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}`: A single identity in a workload identity pool. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}`: A workload identity pool group. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}`: All identities in a workload identity pool with a certain attribute. * `principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*`: All identities in a workload identity pool. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding. * `deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}`: Deleted single identity in a workforce identity pool. For example, `deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value`.
        "A String",
      ],
      "role": "A String", # Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`. For an overview of the IAM roles and permissions, see the [IAM documentation](https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see [here](https://cloud.google.com/iam/docs/understanding-roles).
    },
  ],
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the `etag` in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An `etag` is returned in the response to `getIamPolicy`, and systems are expected to put that etag in the request to `setIamPolicy` to ensure that their change will be applied to the same version of the policy. **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost.
  "version": 42, # Specifies the format of the policy. Valid values are `0`, `1`, and `3`. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version `3`. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions **Important:** If you use IAM Conditions, you must include the `etag` field whenever you call `setIamPolicy`. If you omit this field, then IAM allows you to overwrite a version `3` policy with a version `1` policy, and all of the conditions in the version `3` policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
}

testIamPermissions(resource, body=None, x__xgafv=None)

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a `NOT_FOUND` error. Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

Args:
  resource: string, REQUIRED: The resource for which the policy detail is being requested. See [Resource names](https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for `TestIamPermissions` method.
  "permissions": [ # The set of permissions to check for the `resource`. Permissions with wildcards (such as `*` or `storage.*`) are not allowed. For more information see [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions).
    "A String",
  ],
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for `TestIamPermissions` method.
  "permissions": [ # A subset of `TestPermissionsRequest.permissions` that the caller is allowed.
    "A String",
  ],
}