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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. |
| "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. |
| "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. |
| }, |
| }, |
| ], |
| "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. |
| }, |
| "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/<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 number of workers to be used for parallel processing. |
| # Defaults to 10 if not specified. |
| "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/<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. |
| "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. |
| "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. |
| }, |
| }, |
| ], |
| "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. |
| }, |
| "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/<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 number of workers to be used for parallel processing. |
| # Defaults to 10 if not specified. |
| "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/<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. |
| "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. |
| "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. |
| }, |
| }, |
| ], |
| "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. |
| }, |
| "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/<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 number of workers to be used for parallel processing. |
| # Defaults to 10 if not specified. |
| "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/<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. |
| "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. |
| "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. |
| }, |
| }, |
| ], |
| "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. |
| }, |
| "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/<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 number of workers to be used for parallel processing. |
| # Defaults to 10 if not specified. |
| "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/<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> |