Blame - docs/dyn/ml_v1.projects.jobs.html - platform/external/python/google-api-python-client

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<code><a href="#cancel">cancel(name, body, x__xgafv=None)</a></code></p>

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<p class="firstline">Cancels a running job.</p>

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<code><a href="#create">create(parent, body, x__xgafv=None)</a></code></p>

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<p class="firstline">Creates a training or a batch prediction job.</p>

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<code><a href="#get">get(name, x__xgafv=None)</a></code></p>

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<p class="firstline">Describes a job.</p>

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<code><a href="#list">list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</a></code></p>

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<p class="firstline">Lists the jobs in the project.</p>

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<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>

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<p class="firstline">Retrieves the next page of results.</p>

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<h3>Method Details</h3>

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<code class="details" id="cancel">cancel(name, body, x__xgafv=None)</code>

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<pre>Cancels a running job.

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Args:

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

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Authorization: requires `Editor` role on the parent project. (required)

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body: object, The request body. (required)

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The object takes the form of:

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{ # Request message for the CancelJob method.

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}

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x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

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{ # A generic empty message that you can re-use to avoid defining duplicated

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# empty messages in your APIs. A typical example is to use it as the request

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# or the response type of an API method. For instance:

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#

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# service Foo {

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# rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);

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# }

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#

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# The JSON representation for `Empty` is empty JSON object `{}`.

}</pre>

</div>

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<code class="details" id="create">create(parent, body, x__xgafv=None)</code>

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<pre>Creates a training or a batch prediction job.

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Args:

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parent: string, Required. The project name.

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Authorization: requires `Editor` role on the specified project. (required)

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body: object, The request body. (required)

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The object takes the form of:

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{ # Represents a training or prediction job.

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"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.

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"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.

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"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.

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"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.

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# Only set for hyperparameter tuning jobs.

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"trials": [ # Results for individual Hyperparameter trials.

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# Only set for hyperparameter tuning jobs.

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{ # Represents the result of a single hyperparameter tuning trial from a

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# training job. The TrainingOutput object that is returned on successful

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# completion of a training job with hyperparameter tuning includes a list

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# of HyperparameterOutput objects, one for each successful trial.

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"hyperparameters": { # The hyperparameters given to this trial.

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"a_key": "A String",

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},

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"trialId": "A String", # The trial id for these results.

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"allMetrics": [ # All recorded object metrics for this trial.

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{ # An observed value of a metric.

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"trainingStep": "A String", # The global training step for this metric.

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"objectiveValue": 3.14, # The objective value at this training step.

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},

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],

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"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.

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"trainingStep": "A String", # The global training step for this metric.

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"objectiveValue": 3.14, # The objective value at this training step.

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},

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},

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],

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},

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"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.

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"modelName": "A String", # Use this field if you want to use the default version for the specified

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# model. The string must use the following format:

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#

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# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`

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"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch

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# prediction. If not set, Google Cloud ML will pick the runtime version used

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# during the CreateVersion request for this model version, or choose the

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# latest stable version when model version information is not available

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# such as when the model is specified by uri.

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"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.

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# May contain wildcards.

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"A String",

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],

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"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.

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# Defaults to 10 if not specified.

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"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for

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# the model to use.

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"outputPath": "A String", # Required. The output Google Cloud Storage location.

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"dataFormat": "A String", # Required. The format of the input data files.

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"versionName": "A String", # Use this field if you want to specify a version of the model to use. The

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# string is formatted the same way as `model_version`, with the addition

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# of the version information:

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#

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# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`

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"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.

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},

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"errorMessage": "A String", # Output only. The details of a failure or a cancellation.

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"jobId": "A String", # Required. The user-specified id of the job.

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"state": "A String", # Output only. The detailed state of a job.

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"startTime": "A String", # Output only. When the job processing was started.

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"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.

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"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

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# job's worker nodes.

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#

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# The supported values are the same as those described in the entry for

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# `masterType`.

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#

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# This value must be present when `scaleTier` is set to `CUSTOM` and

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# `workerCount` is greater than zero.

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"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not

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# set, Google Cloud ML will choose the latest stable version.

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"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers

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# and parameter servers.

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"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training

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# job's master worker.

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#

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# The following types are supported:

#

# <dl>

# <dt>standard</dt>

# <dd>

# A basic machine configuration suitable for training simple models with

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# small to moderate datasets.

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# </dd>

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# <dt>large_model</dt>

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# <dd>

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# A machine with a lot of memory, specially suited for parameter servers

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# when your model is large (having many hidden layers or layers with very

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# large numbers of nodes).

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# </dd>

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# <dt>complex_model_s</dt>

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# <dd>

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# A machine suitable for the master and workers of the cluster when your

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# model requires more computation than the standard machine can handle

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# satisfactorily.

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# </dd>

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# <dt>complex_model_m</dt>

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# <dd>

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# A machine with roughly twice the number of cores and roughly double the

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# memory of <code suppresswarning="true">complex_model_s</code>.

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# </dd>

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# <dt>complex_model_l</dt>

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# <dd>

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# A machine with roughly twice the number of cores and roughly double the

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# memory of <code suppresswarning="true">complex_model_m</code>.

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# </dd>

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# <dt>standard_gpu</dt>

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# <dd>

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# A machine equivalent to <code suppresswarning="true">standard</code> that

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# also includes a

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# <a href="/ml-engine/docs/how-tos/using-gpus">

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# GPU that you can use in your trainer</a>.

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# </dd>

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# <dt>complex_model_m_gpu</dt>

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# <dd>

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# A machine equivalent to

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# <code suppresswarning="true">coplex_model_m</code> that also includes

# four GPUs.

# </dd>

# </dl>

#

# You must set this value when `scaleTier` is set to `CUSTOM`.

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"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.

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"maxTrials": 42, # Optional. How many training trials should be attempted to optimize

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# the specified hyperparameters.

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#

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# Defaults to one.

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"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For

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# current versions of Tensorflow, this tag name should exactly match what is

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# shown in Tensorboard, including all scopes. For versions of Tensorflow

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# prior to 0.12, this should be only the tag passed to tf.Summary.

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# By default, "training/hptuning/metric" will be used.

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"params": [ # Required. The set of parameters to tune.

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{ # Represents a single hyperparameter to optimize.

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"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field

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# should be unset if type is `CATEGORICAL`. This value should be integers if

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# type is `INTEGER`.

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"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field

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# should be unset if type is `CATEGORICAL`. This value should be integers if

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# type is INTEGER.

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"discreteValues": [ # Required if type is `DISCRETE`.

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# A list of feasible points.

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# The list should be in strictly increasing order. For instance, this

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# parameter might have possible settings of 1.5, 2.5, and 4.0. This list

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# should not contain more than 1,000 values.

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3.14,

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],

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"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in

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# a HyperparameterSpec message. E.g., "learning_rate".

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"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.

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"A String",

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],

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"type": "A String", # Required. The type of the parameter.

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"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.

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# Leave unset for categorical parameters.

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# Some kind of scaling is strongly recommended for real or integral

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# parameters (e.g., `UNIT_LINEAR_SCALE`).

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},

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],

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"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.

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# You can reduce the time it takes to perform hyperparameter tuning by adding

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# trials in parallel. However, each trail only benefits from the information

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# gained in completed trials. That means that a trial does not get access to

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# the results of trials running at the same time, which could reduce the

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# quality of the overall optimization.

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#

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# Each trial will use the same scale tier and machine types.

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#

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# Defaults to one.

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"goal": "A String", # Required. The type of goal to use for tuning. Available types are

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# `MAXIMIZE` and `MINIMIZE`.

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#

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# Defaults to `MAXIMIZE`.

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},

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"region": "A String", # Required. The Google Compute Engine region to run the training job in.

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"args": [ # Optional. Command line arguments to pass to the program.

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"A String",

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],

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"pythonModule": "A String", # Required. The Python module name to run after installing the packages.

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"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs

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# and other data needed for training. This path is passed to your TensorFlow

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# program as the 'job_dir' command-line argument. The benefit of specifying

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# this field is that Cloud ML validates the path for use in training.

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"packageUris": [ # Required. The Google Cloud Storage location of the packages with

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# the training program and any additional dependencies.

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"A String",

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],

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"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each

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# replica in the cluster will be of the type specified in `worker_type`.

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#

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# This value can only be used when `scale_tier` is set to `CUSTOM`. If you

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# set this value, you must also set `worker_type`.

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"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

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# job's parameter server.

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#

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# The supported values are the same as those described in the entry for

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# `master_type`.

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#

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# This value must be present when `scaleTier` is set to `CUSTOM` and

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# `parameter_server_count` is greater than zero.

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"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training

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# job. Each replica in the cluster will be of the type specified in

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# `parameter_server_type`.

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#

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# This value can only be used when `scale_tier` is set to `CUSTOM`.If you

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# set this value, you must also set `parameter_server_type`.

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},

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"endTime": "A String", # Output only. When the job processing was completed.

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"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.

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"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.

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"nodeHours": 3.14, # Node hours used by the batch prediction job.

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"predictionCount": "A String", # The number of generated predictions.

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"errorCount": "A String", # The number of data instances which resulted in errors.

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},

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"createTime": "A String", # Output only. When the job was created.

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}

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x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

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{ # Represents a training or prediction job.

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"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.

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"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.

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"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.

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"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.

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# Only set for hyperparameter tuning jobs.

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"trials": [ # Results for individual Hyperparameter trials.

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# Only set for hyperparameter tuning jobs.

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{ # Represents the result of a single hyperparameter tuning trial from a

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# training job. The TrainingOutput object that is returned on successful

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# completion of a training job with hyperparameter tuning includes a list

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# of HyperparameterOutput objects, one for each successful trial.

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"hyperparameters": { # The hyperparameters given to this trial.

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"a_key": "A String",

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},

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"trialId": "A String", # The trial id for these results.

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"allMetrics": [ # All recorded object metrics for this trial.

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{ # An observed value of a metric.

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"trainingStep": "A String", # The global training step for this metric.

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"objectiveValue": 3.14, # The objective value at this training step.

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},

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],

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"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.

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"trainingStep": "A String", # The global training step for this metric.

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"objectiveValue": 3.14, # The objective value at this training step.

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},

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},

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],

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},

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"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.

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"modelName": "A String", # Use this field if you want to use the default version for the specified

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# model. The string must use the following format:

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#

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# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`

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"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch

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# prediction. If not set, Google Cloud ML will pick the runtime version used

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# during the CreateVersion request for this model version, or choose the

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# latest stable version when model version information is not available

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# such as when the model is specified by uri.

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"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.

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# May contain wildcards.

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"A String",

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],

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"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.

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# Defaults to 10 if not specified.

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"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for

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# the model to use.

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"outputPath": "A String", # Required. The output Google Cloud Storage location.

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"dataFormat": "A String", # Required. The format of the input data files.

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"versionName": "A String", # Use this field if you want to specify a version of the model to use. The

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# string is formatted the same way as `model_version`, with the addition

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# of the version information:

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#

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# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`

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"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.

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},

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"errorMessage": "A String", # Output only. The details of a failure or a cancellation.

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"jobId": "A String", # Required. The user-specified id of the job.

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"state": "A String", # Output only. The detailed state of a job.

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"startTime": "A String", # Output only. When the job processing was started.

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"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.

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"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

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# job's worker nodes.

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#

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# The supported values are the same as those described in the entry for

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# `masterType`.

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#

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# This value must be present when `scaleTier` is set to `CUSTOM` and

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# `workerCount` is greater than zero.

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"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not

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# set, Google Cloud ML will choose the latest stable version.

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"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers

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# and parameter servers.

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"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training

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# job's master worker.

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#

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# The following types are supported:

#

# <dl>

# <dt>standard</dt>

# <dd>

# A basic machine configuration suitable for training simple models with

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# small to moderate datasets.

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# </dd>

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# <dt>large_model</dt>

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# <dd>

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# A machine with a lot of memory, specially suited for parameter servers

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# when your model is large (having many hidden layers or layers with very

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# large numbers of nodes).

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# </dd>

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# <dt>complex_model_s</dt>

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# <dd>

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# A machine suitable for the master and workers of the cluster when your

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# model requires more computation than the standard machine can handle

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# satisfactorily.

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# </dd>

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# <dt>complex_model_m</dt>

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# <dd>

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# A machine with roughly twice the number of cores and roughly double the

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# memory of <code suppresswarning="true">complex_model_s</code>.

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# </dd>

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# <dt>complex_model_l</dt>

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# <dd>

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# A machine with roughly twice the number of cores and roughly double the

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# memory of <code suppresswarning="true">complex_model_m</code>.

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# </dd>

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# <dt>standard_gpu</dt>

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# <dd>

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# A machine equivalent to <code suppresswarning="true">standard</code> that

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# also includes a

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# <a href="/ml-engine/docs/how-tos/using-gpus">

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# GPU that you can use in your trainer</a>.

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# </dd>

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# <dt>complex_model_m_gpu</dt>

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# <dd>

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# A machine equivalent to

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# <code suppresswarning="true">coplex_model_m</code> that also includes

# four GPUs.

# </dd>

# </dl>

#

# You must set this value when `scaleTier` is set to `CUSTOM`.

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"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.

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"maxTrials": 42, # Optional. How many training trials should be attempted to optimize

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# the specified hyperparameters.

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#

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# Defaults to one.

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"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For

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# current versions of Tensorflow, this tag name should exactly match what is

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# shown in Tensorboard, including all scopes. For versions of Tensorflow

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# prior to 0.12, this should be only the tag passed to tf.Summary.

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# By default, "training/hptuning/metric" will be used.

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"params": [ # Required. The set of parameters to tune.

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{ # Represents a single hyperparameter to optimize.

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"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field

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# should be unset if type is `CATEGORICAL`. This value should be integers if

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# type is `INTEGER`.

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"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field

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# should be unset if type is `CATEGORICAL`. This value should be integers if

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# type is INTEGER.

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"discreteValues": [ # Required if type is `DISCRETE`.

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# A list of feasible points.

505

# The list should be in strictly increasing order. For instance, this

506

# parameter might have possible settings of 1.5, 2.5, and 4.0. This list

507

# should not contain more than 1,000 values.

508

3.14,

509

],

510

"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in

511

# a HyperparameterSpec message. E.g., "learning_rate".

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512

"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.

513

"A String",

514

],

Sai Cheemalapati

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515

"type": "A String", # Required. The type of the parameter.

516

"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.

517

# Leave unset for categorical parameters.

518

# Some kind of scaling is strongly recommended for real or integral

519

# parameters (e.g., `UNIT_LINEAR_SCALE`).

520

},

521

],

522

"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.

523

# You can reduce the time it takes to perform hyperparameter tuning by adding

524

# trials in parallel. However, each trail only benefits from the information

525

# gained in completed trials. That means that a trial does not get access to

526

# the results of trials running at the same time, which could reduce the

527

# quality of the overall optimization.

528

#

529

# Each trial will use the same scale tier and machine types.

530

#

531

# Defaults to one.

532

"goal": "A String", # Required. The type of goal to use for tuning. Available types are

533

# `MAXIMIZE` and `MINIMIZE`.

534

#

535

# Defaults to `MAXIMIZE`.

536

},

537

"region": "A String", # Required. The Google Compute Engine region to run the training job in.

538

"args": [ # Optional. Command line arguments to pass to the program.

539

"A String",

540

],

541

"pythonModule": "A String", # Required. The Python module name to run after installing the packages.

542

"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs

543

# and other data needed for training. This path is passed to your TensorFlow

544

# program as the 'job_dir' command-line argument. The benefit of specifying

545

# this field is that Cloud ML validates the path for use in training.

546

"packageUris": [ # Required. The Google Cloud Storage location of the packages with

547

# the training program and any additional dependencies.

548

"A String",

549

],

550

"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each

551

# replica in the cluster will be of the type specified in `worker_type`.

552

#

553

# This value can only be used when `scale_tier` is set to `CUSTOM`. If you

554

# set this value, you must also set `worker_type`.

555

"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

556

# job's parameter server.

557

#

558

# The supported values are the same as those described in the entry for

559

# `master_type`.

560

#

561

# This value must be present when `scaleTier` is set to `CUSTOM` and

562

# `parameter_server_count` is greater than zero.

563

"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training

564

# job. Each replica in the cluster will be of the type specified in

565

# `parameter_server_type`.

566

#

567

# This value can only be used when `scale_tier` is set to `CUSTOM`.If you

568

# set this value, you must also set `parameter_server_type`.

569

},

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570

"endTime": "A String", # Output only. When the job processing was completed.

571

"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.

572

"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.

573

"nodeHours": 3.14, # Node hours used by the batch prediction job.

574

"predictionCount": "A String", # The number of generated predictions.

575

"errorCount": "A String", # The number of data instances which resulted in errors.

576

},

577

"createTime": "A String", # Output only. When the job was created.

}</pre>

</div>

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

582

<code class="details" id="get">get(name, x__xgafv=None)</code>

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

583

<pre>Describes a job.

584

585

Args:

586

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

587

588

Authorization: requires `Viewer` role on the parent project. (required)

589

x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

596

597

{ # Represents a training or prediction job.

Sai Cheemalapati

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598

"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.

Thomas Coffee

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599

"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.

600

"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.

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601

"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.

602

# Only set for hyperparameter tuning jobs.

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603

"trials": [ # Results for individual Hyperparameter trials.

604

# Only set for hyperparameter tuning jobs.

605

{ # Represents the result of a single hyperparameter tuning trial from a

606

# training job. The TrainingOutput object that is returned on successful

607

# completion of a training job with hyperparameter tuning includes a list

608

# of HyperparameterOutput objects, one for each successful trial.

609

"hyperparameters": { # The hyperparameters given to this trial.

610

"a_key": "A String",

611

},

612

"trialId": "A String", # The trial id for these results.

613

"allMetrics": [ # All recorded object metrics for this trial.

614

{ # An observed value of a metric.

615

"trainingStep": "A String", # The global training step for this metric.

616

"objectiveValue": 3.14, # The objective value at this training step.

617

},

618

],

619

"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.

620

"trainingStep": "A String", # The global training step for this metric.

621

"objectiveValue": 3.14, # The objective value at this training step.

622

},

623

},

624

],

Sai Cheemalapati

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625

},

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626

"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.

627

"modelName": "A String", # Use this field if you want to use the default version for the specified

628

# model. The string must use the following format:

629

#

630

# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`

631

"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch

632

# prediction. If not set, Google Cloud ML will pick the runtime version used

633

# during the CreateVersion request for this model version, or choose the

634

# latest stable version when model version information is not available

635

# such as when the model is specified by uri.

636

"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.

637

# May contain wildcards.

638

"A String",

639

],

640

"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.

641

# Defaults to 10 if not specified.

642

"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for

643

# the model to use.

644

"outputPath": "A String", # Required. The output Google Cloud Storage location.

645

"dataFormat": "A String", # Required. The format of the input data files.

646

"versionName": "A String", # Use this field if you want to specify a version of the model to use. The

647

# string is formatted the same way as `model_version`, with the addition

648

# of the version information:

649

#

650

# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`

651

"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.

652

},

653

"errorMessage": "A String", # Output only. The details of a failure or a cancellation.

654

"jobId": "A String", # Required. The user-specified id of the job.

655

"state": "A String", # Output only. The detailed state of a job.

Sai Cheemalapati

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[diff] [blame]

656

"startTime": "A String", # Output only. When the job processing was started.

657

"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.

658

"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

659

# job's worker nodes.

660

#

661

# The supported values are the same as those described in the entry for

662

# `masterType`.

663

#

664

# This value must be present when `scaleTier` is set to `CUSTOM` and

665

# `workerCount` is greater than zero.

666

"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not

667

# set, Google Cloud ML will choose the latest stable version.

668

"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers

669

# and parameter servers.

670

"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training

671

# job's master worker.

672

#

673

# The following types are supported:

#

# <dl>

# <dt>standard</dt>

# <dd>

# A basic machine configuration suitable for training simple models with

679

# small to moderate datasets.

680

# </dd>

681

# <dt>large_model</dt>

682

# <dd>

683

# A machine with a lot of memory, specially suited for parameter servers

684

# when your model is large (having many hidden layers or layers with very

685

# large numbers of nodes).

686

# </dd>

687

# <dt>complex_model_s</dt>

688

# <dd>

689

# A machine suitable for the master and workers of the cluster when your

690

# model requires more computation than the standard machine can handle

691

# satisfactorily.

692

# </dd>

693

# <dt>complex_model_m</dt>

694

# <dd>

695

# A machine with roughly twice the number of cores and roughly double the

696

# memory of <code suppresswarning="true">complex_model_s</code>.

697

# </dd>

698

# <dt>complex_model_l</dt>

699

# <dd>

700

# A machine with roughly twice the number of cores and roughly double the

701

# memory of <code suppresswarning="true">complex_model_m</code>.

702

# </dd>

703

# <dt>standard_gpu</dt>

704

# <dd>

705

# A machine equivalent to <code suppresswarning="true">standard</code> that

706

# also includes a

Sai Cheemalapati

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[diff] [blame]

707

# <a href="/ml-engine/docs/how-tos/using-gpus">

Sai Cheemalapati

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[diff] [blame]

708

# GPU that you can use in your trainer</a>.

709

# </dd>

710

# <dt>complex_model_m_gpu</dt>

711

# <dd>

712

# A machine equivalent to

713

# <code suppresswarning="true">coplex_model_m</code> that also includes

# four GPUs.

# </dd>

# </dl>

#

# You must set this value when `scaleTier` is set to `CUSTOM`.

719

"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.

720

"maxTrials": 42, # Optional. How many training trials should be attempted to optimize

721

# the specified hyperparameters.

722

#

723

# Defaults to one.

724

"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For

725

# current versions of Tensorflow, this tag name should exactly match what is

726

# shown in Tensorboard, including all scopes. For versions of Tensorflow

727

# prior to 0.12, this should be only the tag passed to tf.Summary.

728

# By default, "training/hptuning/metric" will be used.

729

"params": [ # Required. The set of parameters to tune.

730

{ # Represents a single hyperparameter to optimize.

731

"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field

732

# should be unset if type is `CATEGORICAL`. This value should be integers if

733

# type is `INTEGER`.

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

734

"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field

735

# should be unset if type is `CATEGORICAL`. This value should be integers if

736

# type is INTEGER.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

737

"discreteValues": [ # Required if type is `DISCRETE`.

738

# A list of feasible points.

739

# The list should be in strictly increasing order. For instance, this

740

# parameter might have possible settings of 1.5, 2.5, and 4.0. This list

741

# should not contain more than 1,000 values.

742

3.14,

743

],

744

"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in

745

# a HyperparameterSpec message. E.g., "learning_rate".

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

746

"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.

747

"A String",

748

],

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

749

"type": "A String", # Required. The type of the parameter.

750

"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.

751

# Leave unset for categorical parameters.

752

# Some kind of scaling is strongly recommended for real or integral

753

# parameters (e.g., `UNIT_LINEAR_SCALE`).

754

},

755

],

756

"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.

757

# You can reduce the time it takes to perform hyperparameter tuning by adding

758

# trials in parallel. However, each trail only benefits from the information

759

# gained in completed trials. That means that a trial does not get access to

760

# the results of trials running at the same time, which could reduce the

761

# quality of the overall optimization.

762

#

763

# Each trial will use the same scale tier and machine types.

764

#

765

# Defaults to one.

766

"goal": "A String", # Required. The type of goal to use for tuning. Available types are

767

# `MAXIMIZE` and `MINIMIZE`.

768

#

769

# Defaults to `MAXIMIZE`.

770

},

771

"region": "A String", # Required. The Google Compute Engine region to run the training job in.

772

"args": [ # Optional. Command line arguments to pass to the program.

773

"A String",

774

],

775

"pythonModule": "A String", # Required. The Python module name to run after installing the packages.

776

"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs

777

# and other data needed for training. This path is passed to your TensorFlow

778

# program as the 'job_dir' command-line argument. The benefit of specifying

779

# this field is that Cloud ML validates the path for use in training.

780

"packageUris": [ # Required. The Google Cloud Storage location of the packages with

781

# the training program and any additional dependencies.

782

"A String",

783

],

784

"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each

785

# replica in the cluster will be of the type specified in `worker_type`.

786

#

787

# This value can only be used when `scale_tier` is set to `CUSTOM`. If you

788

# set this value, you must also set `worker_type`.

789

"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

790

# job's parameter server.

791

#

792

# The supported values are the same as those described in the entry for

793

# `master_type`.

794

#

795

# This value must be present when `scaleTier` is set to `CUSTOM` and

796

# `parameter_server_count` is greater than zero.

797

"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training

798

# job. Each replica in the cluster will be of the type specified in

799

# `parameter_server_type`.

800

#

801

# This value can only be used when `scale_tier` is set to `CUSTOM`.If you

802

# set this value, you must also set `parameter_server_type`.

803

},

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

804

"endTime": "A String", # Output only. When the job processing was completed.

805

"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.

806

"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.

807

"nodeHours": 3.14, # Node hours used by the batch prediction job.

808

"predictionCount": "A String", # The number of generated predictions.

809

"errorCount": "A String", # The number of data instances which resulted in errors.

810

},

811

"createTime": "A String", # Output only. When the job was created.

}</pre>

</div>

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

816

<code class="details" id="list">list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code>

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

817

<pre>Lists the jobs in the project.

818

819

Args:

820

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

821

822

Authorization: requires `Viewer` role on the specified project. (required)

823

pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there

824

are more remaining results than this number, the response message will

825

contain a valid value in the `next_page_token` field.

826

827

The default value is 20, and the maximum page size is 100.

828

filter: string, Optional. Specifies the subset of jobs to retrieve.

829

pageToken: string, Optional. A page token to request the next page of results.

830

831

You get the token from the `next_page_token` field of the response from

832

the previous call.

833

x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

840

841

{ # Response message for the ListJobs method.

842

"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a

843

# subsequent call.

844

"jobs": [ # The list of jobs.

845

{ # Represents a training or prediction job.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

846

"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

847

"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.

848

"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

849

"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.

850

# Only set for hyperparameter tuning jobs.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

851

"trials": [ # Results for individual Hyperparameter trials.

852

# Only set for hyperparameter tuning jobs.

853

{ # Represents the result of a single hyperparameter tuning trial from a

854

# training job. The TrainingOutput object that is returned on successful

855

# completion of a training job with hyperparameter tuning includes a list

856

# of HyperparameterOutput objects, one for each successful trial.

857

"hyperparameters": { # The hyperparameters given to this trial.

858

"a_key": "A String",

859

},

860

"trialId": "A String", # The trial id for these results.

861

"allMetrics": [ # All recorded object metrics for this trial.

862

{ # An observed value of a metric.

863

"trainingStep": "A String", # The global training step for this metric.

864

"objectiveValue": 3.14, # The objective value at this training step.

865

},

866

],

867

"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.

868

"trainingStep": "A String", # The global training step for this metric.

869

"objectiveValue": 3.14, # The objective value at this training step.

870

},

871

},

872

],

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

873

},

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

874

"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.

875

"modelName": "A String", # Use this field if you want to use the default version for the specified

876

# model. The string must use the following format:

877

#

878

# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`

879

"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch

880

# prediction. If not set, Google Cloud ML will pick the runtime version used

881

# during the CreateVersion request for this model version, or choose the

882

# latest stable version when model version information is not available

883

# such as when the model is specified by uri.

884

"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.

885

# May contain wildcards.

886

"A String",

887

],

888

"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.

889

# Defaults to 10 if not specified.

890

"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for

891

# the model to use.

892

"outputPath": "A String", # Required. The output Google Cloud Storage location.

893

"dataFormat": "A String", # Required. The format of the input data files.

894

"versionName": "A String", # Use this field if you want to specify a version of the model to use. The

895

# string is formatted the same way as `model_version`, with the addition

896

# of the version information:

897

#

898

# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`

899

"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.

900

},

901

"errorMessage": "A String", # Output only. The details of a failure or a cancellation.

902

"jobId": "A String", # Required. The user-specified id of the job.

903

"state": "A String", # Output only. The detailed state of a job.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

904

"startTime": "A String", # Output only. When the job processing was started.

905

"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.

906

"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

907

# job's worker nodes.

908

#

909

# The supported values are the same as those described in the entry for

910

# `masterType`.

911

#

912

# This value must be present when `scaleTier` is set to `CUSTOM` and

913

# `workerCount` is greater than zero.

914

"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not

915

# set, Google Cloud ML will choose the latest stable version.

916

"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers

917

# and parameter servers.

918

"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training

919

# job's master worker.

920

#

921

# The following types are supported:

#

# <dl>

# <dt>standard</dt>

# <dd>

# A basic machine configuration suitable for training simple models with

927

# small to moderate datasets.

928

# </dd>

929

# <dt>large_model</dt>

930

# <dd>

931

# A machine with a lot of memory, specially suited for parameter servers

932

# when your model is large (having many hidden layers or layers with very

933

# large numbers of nodes).

934

# </dd>

935

# <dt>complex_model_s</dt>

936

# <dd>

937

# A machine suitable for the master and workers of the cluster when your

938

# model requires more computation than the standard machine can handle

939

# satisfactorily.

940

# </dd>

941

# <dt>complex_model_m</dt>

942

# <dd>

943

# A machine with roughly twice the number of cores and roughly double the

944

# memory of <code suppresswarning="true">complex_model_s</code>.

945

# </dd>

946

# <dt>complex_model_l</dt>

947

# <dd>

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# A machine with roughly twice the number of cores and roughly double the

949

# memory of <code suppresswarning="true">complex_model_m</code>.

950

# </dd>

951

# <dt>standard_gpu</dt>

952

# <dd>

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# A machine equivalent to <code suppresswarning="true">standard</code> that

954

# also includes a

Sai Cheemalapati

2017-03-24 15:06:46 -0700

[diff] [blame]

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# <a href="/ml-engine/docs/how-tos/using-gpus">

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2017-03-13 12:12:03 -0400

[diff] [blame]

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# GPU that you can use in your trainer</a>.

957

# </dd>

958

# <dt>complex_model_m_gpu</dt>

959

# <dd>

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# A machine equivalent to

961

# <code suppresswarning="true">coplex_model_m</code> that also includes

# four GPUs.

# </dd>

# </dl>

#

# You must set this value when `scaleTier` is set to `CUSTOM`.

967

"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.

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"maxTrials": 42, # Optional. How many training trials should be attempted to optimize

969

# the specified hyperparameters.

970

#

971

# Defaults to one.

972

"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For

973

# current versions of Tensorflow, this tag name should exactly match what is

974

# shown in Tensorboard, including all scopes. For versions of Tensorflow

975

# prior to 0.12, this should be only the tag passed to tf.Summary.

976

# By default, "training/hptuning/metric" will be used.

977

"params": [ # Required. The set of parameters to tune.

978

{ # Represents a single hyperparameter to optimize.

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"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field

980

# should be unset if type is `CATEGORICAL`. This value should be integers if

981

# type is `INTEGER`.

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2017-03-27 10:39:26 -0700

[diff] [blame^]

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"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field

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# should be unset if type is `CATEGORICAL`. This value should be integers if

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# type is INTEGER.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame]

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"discreteValues": [ # Required if type is `DISCRETE`.

986

# A list of feasible points.

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# The list should be in strictly increasing order. For instance, this

988

# parameter might have possible settings of 1.5, 2.5, and 4.0. This list

989

# should not contain more than 1,000 values.

990

3.14,

991

],

992

"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in

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# a HyperparameterSpec message. E.g., "learning_rate".

Thomas Coffee

2017-03-27 10:39:26 -0700

[diff] [blame^]

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"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.

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"A String",

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],

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2017-03-13 12:12:03 -0400

[diff] [blame]

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"type": "A String", # Required. The type of the parameter.

998

"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.

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# Leave unset for categorical parameters.

1000

# Some kind of scaling is strongly recommended for real or integral

1001

# parameters (e.g., `UNIT_LINEAR_SCALE`).

1002

},

1003

],

1004

"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.

1005

# You can reduce the time it takes to perform hyperparameter tuning by adding

1006

# trials in parallel. However, each trail only benefits from the information

1007

# gained in completed trials. That means that a trial does not get access to

1008

# the results of trials running at the same time, which could reduce the

1009

# quality of the overall optimization.

1010

#

1011

# Each trial will use the same scale tier and machine types.

1012

#

1013

# Defaults to one.

1014

"goal": "A String", # Required. The type of goal to use for tuning. Available types are

1015

# `MAXIMIZE` and `MINIMIZE`.

1016

#

1017

# Defaults to `MAXIMIZE`.

1018

},

1019

"region": "A String", # Required. The Google Compute Engine region to run the training job in.

1020

"args": [ # Optional. Command line arguments to pass to the program.

1021

"A String",

1022

],

1023

"pythonModule": "A String", # Required. The Python module name to run after installing the packages.

1024

"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs

1025

# and other data needed for training. This path is passed to your TensorFlow

1026

# program as the 'job_dir' command-line argument. The benefit of specifying

1027

# this field is that Cloud ML validates the path for use in training.

1028

"packageUris": [ # Required. The Google Cloud Storage location of the packages with

1029

# the training program and any additional dependencies.

1030

"A String",

1031

],

1032

"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each

1033

# replica in the cluster will be of the type specified in `worker_type`.

1034

#

1035

# This value can only be used when `scale_tier` is set to `CUSTOM`. If you

1036

# set this value, you must also set `worker_type`.

1037

"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training

1038

# job's parameter server.

1039

#

1040

# The supported values are the same as those described in the entry for

1041

# `master_type`.

1042

#

1043

# This value must be present when `scaleTier` is set to `CUSTOM` and

1044

# `parameter_server_count` is greater than zero.

1045

"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training

1046

# job. Each replica in the cluster will be of the type specified in

1047

# `parameter_server_type`.

1048

#

1049

# This value can only be used when `scale_tier` is set to `CUSTOM`.If you

1050

# set this value, you must also set `parameter_server_type`.

1051

},

Sai Cheemalapati