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+<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning</a> . <a href="ml_v1beta1.projects.html">projects</a> . <a href="ml_v1beta1.projects.jobs.html">jobs</a></h1>
+<h2>Instance Methods</h2>
+<p class="toc_element">
+ <code><a href="#cancel">cancel(name=None, body, x__xgafv=None)</a></code></p>
+<p class="firstline">Cancels a running job.</p>
+<p class="toc_element">
+ <code><a href="#create">create(parent=None, body, x__xgafv=None)</a></code></p>
+<p class="firstline">Creates a training or a batch prediction job.</p>
+<p class="toc_element">
+ <code><a href="#get">get(name=None, x__xgafv=None)</a></code></p>
+<p class="firstline">Describes a job.</p>
+<p class="toc_element">
+ <code><a href="#list">list(parent=None, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</a></code></p>
+<p class="firstline">Lists the jobs in the project.</p>
+<p class="toc_element">
+ <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
+<p class="firstline">Retrieves the next page of results.</p>
+<h3>Method Details</h3>
+<div class="method">
+ <code class="details" id="cancel">cancel(name=None, body, x__xgafv=None)</code>
+ <pre>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 `{}`.
+ }</pre>
+</div>
+
+<div class="method">
+ <code class="details" id="create">create(parent=None, body, x__xgafv=None)</code>
+ <pre>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. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "trials": [ # Results for individual Hyperparameter trials.
+ { # 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.
+ },
+ },
+ ],
+ },
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
+ # May contain wildcards.
+ "A String",
+ ],
+ "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
+ # Defaults to 10.
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
+ "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
+ },
+ "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.
+ "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:
+ #
+ # <dl>
+ # <dt>standard</dt>
+ # <dd>
+ # A basic machine configuration suitable for training simple models with
+ # small to moderate datasets.
+ # </dd>
+ # <dt>large_model</dt>
+ # <dd>
+ # 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).
+ # </dd>
+ # <dt>complex_model_s</dt>
+ # <dd>
+ # A machine suitable for the master and workers of the cluster when your
+ # model requires more computation than the standard machine can handle
+ # satisfactorily.
+ # </dd>
+ # <dt>complex_model_m</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_s</code>.
+ # </dd>
+ # <dt>complex_model_l</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_m</code>.
+ # </dd>
+ # </dl>
+ #
+ # 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.
+ "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`.
+ "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.
+ "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".
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
+ "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.
+ "packageUris": [ # Required. The Google Cloud Storage location of the packages with
+ # the training program and any additional dependencies.
+ "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`.
+ },
+ "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.
+ "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. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "trials": [ # Results for individual Hyperparameter trials.
+ { # 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.
+ },
+ },
+ ],
+ },
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
+ # May contain wildcards.
+ "A String",
+ ],
+ "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
+ # Defaults to 10.
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
+ "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
+ },
+ "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.
+ "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:
+ #
+ # <dl>
+ # <dt>standard</dt>
+ # <dd>
+ # A basic machine configuration suitable for training simple models with
+ # small to moderate datasets.
+ # </dd>
+ # <dt>large_model</dt>
+ # <dd>
+ # 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).
+ # </dd>
+ # <dt>complex_model_s</dt>
+ # <dd>
+ # A machine suitable for the master and workers of the cluster when your
+ # model requires more computation than the standard machine can handle
+ # satisfactorily.
+ # </dd>
+ # <dt>complex_model_m</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_s</code>.
+ # </dd>
+ # <dt>complex_model_l</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_m</code>.
+ # </dd>
+ # </dl>
+ #
+ # 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.
+ "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`.
+ "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.
+ "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".
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
+ "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.
+ "packageUris": [ # Required. The Google Cloud Storage location of the packages with
+ # the training program and any additional dependencies.
+ "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`.
+ },
+ "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.
+ "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.
+ }</pre>
+</div>
+
+<div class="method">
+ <code class="details" id="get">get(name=None, x__xgafv=None)</code>
+ <pre>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. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "trials": [ # Results for individual Hyperparameter trials.
+ { # 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.
+ },
+ },
+ ],
+ },
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
+ # May contain wildcards.
+ "A String",
+ ],
+ "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
+ # Defaults to 10.
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
+ "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
+ },
+ "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.
+ "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:
+ #
+ # <dl>
+ # <dt>standard</dt>
+ # <dd>
+ # A basic machine configuration suitable for training simple models with
+ # small to moderate datasets.
+ # </dd>
+ # <dt>large_model</dt>
+ # <dd>
+ # 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).
+ # </dd>
+ # <dt>complex_model_s</dt>
+ # <dd>
+ # A machine suitable for the master and workers of the cluster when your
+ # model requires more computation than the standard machine can handle
+ # satisfactorily.
+ # </dd>
+ # <dt>complex_model_m</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_s</code>.
+ # </dd>
+ # <dt>complex_model_l</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_m</code>.
+ # </dd>
+ # </dl>
+ #
+ # 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.
+ "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`.
+ "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.
+ "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".
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
+ "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.
+ "packageUris": [ # Required. The Google Cloud Storage location of the packages with
+ # the training program and any additional dependencies.
+ "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`.
+ },
+ "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.
+ "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.
+ }</pre>
+</div>
+
+<div class="method">
+ <code class="details" id="list">list(parent=None, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code>
+ <pre>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. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "trials": [ # Results for individual Hyperparameter trials.
+ { # 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.
+ },
+ },
+ ],
+ },
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
+ # May contain wildcards.
+ "A String",
+ ],
+ "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
+ # Defaults to 10.
+ "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:
+ #
+ # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
+ "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
+ },
+ "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.
+ "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:
+ #
+ # <dl>
+ # <dt>standard</dt>
+ # <dd>
+ # A basic machine configuration suitable for training simple models with
+ # small to moderate datasets.
+ # </dd>
+ # <dt>large_model</dt>
+ # <dd>
+ # 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).
+ # </dd>
+ # <dt>complex_model_s</dt>
+ # <dd>
+ # A machine suitable for the master and workers of the cluster when your
+ # model requires more computation than the standard machine can handle
+ # satisfactorily.
+ # </dd>
+ # <dt>complex_model_m</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_s</code>.
+ # </dd>
+ # <dt>complex_model_l</dt>
+ # <dd>
+ # A machine with roughly twice the number of cores and roughly double the
+ # memory of <code suppresswarning="true">complex_model_m</code>.
+ # </dd>
+ # </dl>
+ #
+ # 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.
+ "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`.
+ "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.
+ "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".
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
+ "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.
+ "packageUris": [ # Required. The Google Cloud Storage location of the packages with
+ # the training program and any additional dependencies.
+ "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`.
+ },
+ "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.
+ "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.
+ },
+ ],
+ }</pre>
+</div>
+
+<div class="method">
+ <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
+ <pre>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.
+ </pre>
+</div>
+
+</body></html>
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