# Google Cloud Machine Learning Engine . projects . jobs

## Instance Methods

Cancels a running job.

Creates a training or a batch prediction job.

Describes a job.

Lists the jobs in the project.

Retrieves the next page of results.

### Method Details

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

    Cancels a running job.

    Args:
      name: string, Required. The name of the job to cancel.

    Authorization: requires `Editor` role on the parent project. (required)
      body: object, The request body. (required)
        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);
          #     }
          #
          # The JSON representation for `Empty` is empty JSON object `{}`.
      }

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

    Creates a training or a batch prediction job.

    Args:
      parent: string, Required. The project name.

    Authorization: requires `Editor` role on the specified project. (required)
      body: object, The request body. (required)
        The object takes the form of:

    { # Represents a training or prediction job.
        "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
          "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
              # Only set for hyperparameter tuning jobs.
          "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.
              "hyperparameters": { # The hyperparameters given to this trial.
                "a_key": "A String",
              },
              "trialId": "A String", # The trial id for these results.
              "allMetrics": [ # All recorded object metrics for this trial.
                { # An observed value of a metric.
                  "trainingStep": "A String", # The global training step for this metric.
                  "objectiveValue": 3.14, # The objective value at this training step.
                },
              ],
              "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
                "trainingStep": "A String", # The global training step for this metric.
                "objectiveValue": 3.14, # The objective value at this training step.
              },
            },
          ],
          "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
        },
        "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
          "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 present when `scaleTier` is set to `CUSTOM` and
              # `workerCount` is greater than zero.
          "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training.  If not
              # set, Google Cloud ML will choose the latest stable version.
          "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
              # and parameter servers.
          "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
              # job's master worker.
              #
              # The following types are supported:
              #
              #
              #   standard
              #
              #   A basic machine configuration suitable for training simple models with
              #   small to moderate datasets.
              #
              #   large_model
              #
              #   A machine with a lot of memory, specially suited for parameter servers
              #   when your model is large (having many hidden layers or layers with very
              #   large numbers of nodes).
              #
              #   complex_model_s
              #
              #   A machine suitable for the master and workers of the cluster when your
              #   model requires more computation than the standard machine can handle
              #   satisfactorily.
              #
              #   complex_model_m
              #
              #   A machine with roughly twice the number of cores and roughly double the
              #   memory of complex_model_s.
              #
              #   complex_model_l
              #
              #   A machine with roughly twice the number of cores and roughly double the
              #   memory of complex_model_m.
              #
              #   standard_gpu
              #
              #   A machine equivalent to standard that
              #   also includes a
              #
              #   GPU that you can use in your trainer.
              #
              #   complex_model_m_gpu
              #
              #   A machine equivalent to
              #   complex_model_m that also includes
              #   four GPUs.
              #
              #
              #
              # You must set this value when `scaleTier` is set to `CUSTOM`.
          "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
            "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
                # the specified hyperparameters.
                #
                # Defaults to one.
            "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.
            "params": [ # Required. The set of parameters to tune.
              { # Represents a single hyperparameter to optimize.
                "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
                    # should be unset if type is `CATEGORICAL`. This value should be integers if
                    # type is `INTEGER`.
                "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,
                ],
                "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
                    # a HyperparameterSpec message. E.g., "learning_rate".
                "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.
                "type": "A String", # Required. The type of the parameter.
                "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`).
              },
            ],
            "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.
            "goal": "A String", # Required. The type of goal to use for tuning. Available types are
                # `MAXIMIZE` and `MINIMIZE`.
                #
                # Defaults to `MAXIMIZE`.
          },
          "region": "A String", # Required. The Google Compute Engine region to run the training job in.
          "args": [ # Optional. Command line arguments to pass to the program.
            "A String",
          ],
          "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
          "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.
          "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",
          ],
          "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`.
          "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 present when `scaleTier` is set to `CUSTOM` and
              # `parameter_server_count` is greater than zero.
          "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`.
        },
        "startTime": "A String", # Output only. When the job processing was started.
        "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
        "jobId": "A String", # Required. The user-specified id of the job.
        "state": "A String", # Output only. The detailed state of a job.
        "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
          "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]"`
          "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
              # prediction. If not set, Google Cloud ML 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.
          "inputPaths": [ # Required. The Google 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.
          "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
              # the model to use.
          "outputPath": "A String", # Required. The output Google Cloud Storage location.
          "dataFormat": "A String", # Required. The format of the input data files.
          "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]"`
          "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
        },
        "endTime": "A String", # Output only. When the job processing was completed.
        "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
          "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
          "nodeHours": 3.14, # Node hours used by the batch prediction job.
          "predictionCount": "A String", # The number of generated predictions.
          "errorCount": "A String", # The number of data instances which resulted in errors.
        },
        "createTime": "A String", # Output only. When the job was created.
      }

      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.
          "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
            "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
                # Only set for hyperparameter tuning jobs.
            "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.
                "hyperparameters": { # The hyperparameters given to this trial.
                  "a_key": "A String",
                },
                "trialId": "A String", # The trial id for these results.
                "allMetrics": [ # All recorded object metrics for this trial.
                  { # An observed value of a metric.
                    "trainingStep": "A String", # The global training step for this metric.
                    "objectiveValue": 3.14, # The objective value at this training step.
                  },
                ],
                "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
                  "trainingStep": "A String", # The global training step for this metric.
                  "objectiveValue": 3.14, # The objective value at this training step.
                },
              },
            ],
            "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
          },
          "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
            "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 present when `scaleTier` is set to `CUSTOM` and
                # `workerCount` is greater than zero.
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training.  If not
                # set, Google Cloud ML will choose the latest stable version.
            "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
                # and parameter servers.
            "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
                # job's master worker.
                #
                # The following types are supported:
                #
                #
                #   standard
                #
                #   A basic machine configuration suitable for training simple models with
                #   small to moderate datasets.
                #
                #   large_model
                #
                #   A machine with a lot of memory, specially suited for parameter servers
                #   when your model is large (having many hidden layers or layers with very
                #   large numbers of nodes).
                #
                #   complex_model_s
                #
                #   A machine suitable for the master and workers of the cluster when your
                #   model requires more computation than the standard machine can handle
                #   satisfactorily.
                #
                #   complex_model_m
                #
                #   A machine with roughly twice the number of cores and roughly double the
                #   memory of complex_model_s.
                #
                #   complex_model_l
                #
                #   A machine with roughly twice the number of cores and roughly double the
                #   memory of complex_model_m.
                #
                #   standard_gpu
                #
                #   A machine equivalent to standard that
                #   also includes a
                #
                #   GPU that you can use in your trainer.
                #
                #   complex_model_m_gpu
                #
                #   A machine equivalent to
                #   complex_model_m that also includes
                #   four GPUs.
                #
                #
                #
                # You must set this value when `scaleTier` is set to `CUSTOM`.
            "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
              "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
                  # the specified hyperparameters.
                  #
                  # Defaults to one.
              "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.
              "params": [ # Required. The set of parameters to tune.
                { # Represents a single hyperparameter to optimize.
                  "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
                      # should be unset if type is `CATEGORICAL`. This value should be integers if
                      # type is `INTEGER`.
                  "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,
                  ],
                  "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
                      # a HyperparameterSpec message. E.g., "learning_rate".
                  "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.
                  "type": "A String", # Required. The type of the parameter.
                  "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`).
                },
              ],
              "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.
              "goal": "A String", # Required. The type of goal to use for tuning. Available types are
                  # `MAXIMIZE` and `MINIMIZE`.
                  #
                  # Defaults to `MAXIMIZE`.
            },
            "region": "A String", # Required. The Google Compute Engine region to run the training job in.
            "args": [ # Optional. Command line arguments to pass to the program.
              "A String",
            ],
            "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
            "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.
            "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",
            ],
            "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`.
            "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 present when `scaleTier` is set to `CUSTOM` and
                # `parameter_server_count` is greater than zero.
            "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`.
          },
          "startTime": "A String", # Output only. When the job processing was started.
          "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
          "jobId": "A String", # Required. The user-specified id of the job.
          "state": "A String", # Output only. The detailed state of a job.
          "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
            "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]"`
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
                # prediction. If not set, Google Cloud ML 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.
            "inputPaths": [ # Required. The Google 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.
            "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
                # the model to use.
            "outputPath": "A String", # Required. The output Google Cloud Storage location.
            "dataFormat": "A String", # Required. The format of the input data files.
            "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]"`
            "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
          },
          "endTime": "A String", # Output only. When the job processing was completed.
          "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
            "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
            "nodeHours": 3.14, # Node hours used by the batch prediction job.
            "predictionCount": "A String", # The number of generated predictions.
            "errorCount": "A String", # The number of data instances which resulted in errors.
          },
          "createTime": "A String", # Output only. When the job was created.
        }

`get(name, x__xgafv=None)`

    Describes a job.

    Args:
      name: string, Required. The name of the job to get the description of.

    Authorization: requires `Viewer` role on the parent project. (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.
          "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
            "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
                # Only set for hyperparameter tuning jobs.
            "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.
                "hyperparameters": { # The hyperparameters given to this trial.
                  "a_key": "A String",
                },
                "trialId": "A String", # The trial id for these results.
                "allMetrics": [ # All recorded object metrics for this trial.
                  { # An observed value of a metric.
                    "trainingStep": "A String", # The global training step for this metric.
                    "objectiveValue": 3.14, # The objective value at this training step.
                  },
                ],
                "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
                  "trainingStep": "A String", # The global training step for this metric.
                  "objectiveValue": 3.14, # The objective value at this training step.
                },
              },
            ],
            "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
          },
          "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
            "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 present when `scaleTier` is set to `CUSTOM` and
                # `workerCount` is greater than zero.
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training.  If not
                # set, Google Cloud ML will choose the latest stable version.
            "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
                # and parameter servers.
            "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
                # job's master worker.
                #
                # The following types are supported:
                #
                #
                #   standard
                #
                #   A basic machine configuration suitable for training simple models with
                #   small to moderate datasets.
                #
                #   large_model
                #
                #   A machine with a lot of memory, specially suited for parameter servers
                #   when your model is large (having many hidden layers or layers with very
                #   large numbers of nodes).
                #
                #   complex_model_s
                #
                #   A machine suitable for the master and workers of the cluster when your
                #   model requires more computation than the standard machine can handle
                #   satisfactorily.
                #
                #   complex_model_m
                #
                #   A machine with roughly twice the number of cores and roughly double the
                #   memory of complex_model_s.
                #
                #   complex_model_l
                #
                #   A machine with roughly twice the number of cores and roughly double the
                #   memory of complex_model_m.
                #
                #   standard_gpu
                #
                #   A machine equivalent to standard that
                #   also includes a
                #
                #   GPU that you can use in your trainer.
                #
                #   complex_model_m_gpu
                #
                #   A machine equivalent to
                #   complex_model_m that also includes
                #   four GPUs.
                #
                #
                #
                # You must set this value when `scaleTier` is set to `CUSTOM`.
            "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
              "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
                  # the specified hyperparameters.
                  #
                  # Defaults to one.
              "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.
              "params": [ # Required. The set of parameters to tune.
                { # Represents a single hyperparameter to optimize.
                  "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
                      # should be unset if type is `CATEGORICAL`. This value should be integers if
                      # type is `INTEGER`.
                  "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,
                  ],
                  "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
                      # a HyperparameterSpec message. E.g., "learning_rate".
                  "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.
                  "type": "A String", # Required. The type of the parameter.
                  "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`).
                },
              ],
              "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.
              "goal": "A String", # Required. The type of goal to use for tuning. Available types are
                  # `MAXIMIZE` and `MINIMIZE`.
                  #
                  # Defaults to `MAXIMIZE`.
            },
            "region": "A String", # Required. The Google Compute Engine region to run the training job in.
            "args": [ # Optional. Command line arguments to pass to the program.
              "A String",
            ],
            "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
            "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.
            "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",
            ],
            "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`.
            "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 present when `scaleTier` is set to `CUSTOM` and
                # `parameter_server_count` is greater than zero.
            "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`.
          },
          "startTime": "A String", # Output only. When the job processing was started.
          "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
          "jobId": "A String", # Required. The user-specified id of the job.
          "state": "A String", # Output only. The detailed state of a job.
          "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
            "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]"`
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
                # prediction. If not set, Google Cloud ML 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.
            "inputPaths": [ # Required. The Google 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.
            "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
                # the model to use.
            "outputPath": "A String", # Required. The output Google Cloud Storage location.
            "dataFormat": "A String", # Required. The format of the input data files.
            "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]"`
            "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
          },
          "endTime": "A String", # Output only. When the job processing was completed.
          "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
            "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
            "nodeHours": 3.14, # Node hours used by the batch prediction job.
            "predictionCount": "A String", # The number of generated predictions.
            "errorCount": "A String", # The number of data instances which resulted in errors.
          },
          "createTime": "A String", # Output only. When the job was created.
        }

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

    Lists the jobs in the project.

    Args:
      parent: string, Required. The name of the project for which to list jobs.

    Authorization: requires `Viewer` role on the specified project. (required)
      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.
      filter: string, Optional. Specifies the subset of jobs to retrieve.
      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.
        "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
            # subsequent call.
        "jobs": [ # The list of jobs.
          { # Represents a training or prediction job.
              "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
                "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
                    # Only set for hyperparameter tuning jobs.
                "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.
                    "hyperparameters": { # The hyperparameters given to this trial.
                      "a_key": "A String",
                    },
                    "trialId": "A String", # The trial id for these results.
                    "allMetrics": [ # All recorded object metrics for this trial.
                      { # An observed value of a metric.
                        "trainingStep": "A String", # The global training step for this metric.
                        "objectiveValue": 3.14, # The objective value at this training step.
                      },
                    ],
                    "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
                      "trainingStep": "A String", # The global training step for this metric.
                      "objectiveValue": 3.14, # The objective value at this training step.
                    },
                  },
                ],
                "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
              },
              "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
                "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 present when `scaleTier` is set to `CUSTOM` and
                    # `workerCount` is greater than zero.
                "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training.  If not
                    # set, Google Cloud ML will choose the latest stable version.
                "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
                    # and parameter servers.
                "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
                    # job's master worker.
                    #
                    # The following types are supported:
                    #
                    #
                    #   standard
                    #
                    #   A basic machine configuration suitable for training simple models with
                    #   small to moderate datasets.
                    #
                    #   large_model
                    #
                    #   A machine with a lot of memory, specially suited for parameter servers
                    #   when your model is large (having many hidden layers or layers with very
                    #   large numbers of nodes).
                    #
                    #   complex_model_s
                    #
                    #   A machine suitable for the master and workers of the cluster when your
                    #   model requires more computation than the standard machine can handle
                    #   satisfactorily.
                    #
                    #   complex_model_m
                    #
                    #   A machine with roughly twice the number of cores and roughly double the
                    #   memory of complex_model_s.
                    #
                    #   complex_model_l
                    #
                    #   A machine with roughly twice the number of cores and roughly double the
                    #   memory of complex_model_m.
                    #
                    #   standard_gpu
                    #
                    #   A machine equivalent to standard that
                    #   also includes a
                    #
                    #   GPU that you can use in your trainer.
                    #
                    #   complex_model_m_gpu
                    #
                    #   A machine equivalent to
                    #   complex_model_m that also includes
                    #   four GPUs.
                    #
                    #
                    #
                    # You must set this value when `scaleTier` is set to `CUSTOM`.
                "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
                  "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
                      # the specified hyperparameters.
                      #
                      # Defaults to one.
                  "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.
                  "params": [ # Required. The set of parameters to tune.
                    { # Represents a single hyperparameter to optimize.
                      "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
                          # should be unset if type is `CATEGORICAL`. This value should be integers if
                          # type is `INTEGER`.
                      "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,
                      ],
                      "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
                          # a HyperparameterSpec message. E.g., "learning_rate".
                      "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.
                      "type": "A String", # Required. The type of the parameter.
                      "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`).
                    },
                  ],
                  "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.
                  "goal": "A String", # Required. The type of goal to use for tuning. Available types are
                      # `MAXIMIZE` and `MINIMIZE`.
                      #
                      # Defaults to `MAXIMIZE`.
                },
                "region": "A String", # Required. The Google Compute Engine region to run the training job in.
                "args": [ # Optional. Command line arguments to pass to the program.
                  "A String",
                ],
                "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
                "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.
                "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",
                ],
                "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`.
                "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 present when `scaleTier` is set to `CUSTOM` and
                    # `parameter_server_count` is greater than zero.
                "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`.
              },
              "startTime": "A String", # Output only. When the job processing was started.
              "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
              "jobId": "A String", # Required. The user-specified id of the job.
              "state": "A String", # Output only. The detailed state of a job.
              "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
                "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]"`
                "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
                    # prediction. If not set, Google Cloud ML 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.
                "inputPaths": [ # Required. The Google 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.
                "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
                    # the model to use.
                "outputPath": "A String", # Required. The output Google Cloud Storage location.
                "dataFormat": "A String", # Required. The format of the input data files.
                "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]"`
                "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
              },
              "endTime": "A String", # Output only. When the job processing was completed.
              "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
                "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
                "nodeHours": 3.14, # Node hours used by the batch prediction job.
                "predictionCount": "A String", # The number of generated predictions.
                "errorCount": "A String", # The number of data instances which resulted in errors.
              },
              "createTime": "A String", # Output only. When the job was created.
            },
        ],
      }

`list_next(previous_request, previous_response)`

    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.