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1083 lines
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1083 lines
58 KiB
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<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning Engine</a> . <a href="ml_v1beta1.projects.html">projects</a> . <a href="ml_v1beta1.projects.jobs.html">jobs</a></h1>
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<h2>Instance Methods</h2>
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<p class="toc_element">
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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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<p class="toc_element">
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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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<p class="toc_element">
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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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<p class="toc_element">
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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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<p class="toc_element">
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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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<div class="method">
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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.
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Allowed values
|
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1 - v1 error format
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2 - v2 error format
|
|
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Returns:
|
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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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# The JSON representation for `Empty` is empty JSON object `{}`.
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}</pre>
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</div>
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<div class="method">
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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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"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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"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
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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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"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
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},
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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:
|
|
#
|
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# <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>
|
|
# <dt>standard_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to <code suppresswarning="true">standard</code> that
|
|
# also includes a
|
|
# <a href="/ml-engine/docs/how-tos/using-gpus">
|
|
# GPU that you can use in your trainer</a>.
|
|
# </dd>
|
|
# <dt>complex_model_m_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to
|
|
# <code suppresswarning="true">complex_model_m</code> that also includes
|
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# four GPUs.
|
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# </dd>
|
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# </dl>
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#
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# 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.
|
|
"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
|
|
# 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.
|
|
{ # Represents a single hyperparameter to optimize.
|
|
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is `INTEGER`.
|
|
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
|
|
"A String",
|
|
],
|
|
"discreteValues": [ # Required if type is `DISCRETE`.
|
|
# A list of feasible points.
|
|
# The list should be in strictly increasing order. For instance, this
|
|
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
|
|
# should not contain more than 1,000 values.
|
|
3.14,
|
|
],
|
|
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
|
|
# a HyperparameterSpec message. E.g., "learning_rate".
|
|
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is INTEGER.
|
|
"type": "A String", # Required. The type of the parameter.
|
|
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
|
|
# Leave unset for categorical parameters.
|
|
# Some kind of scaling is strongly recommended for real or integral
|
|
# parameters (e.g., `UNIT_LINEAR_SCALE`).
|
|
},
|
|
],
|
|
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
|
|
# You can reduce the time it takes to perform hyperparameter tuning by adding
|
|
# trials in parallel. However, each trail only benefits from the information
|
|
# gained in completed trials. That means that a trial does not get access to
|
|
# the results of trials running at the same time, which could reduce the
|
|
# quality of the overall optimization.
|
|
#
|
|
# Each trial will use the same scale tier and machine types.
|
|
#
|
|
# Defaults to one.
|
|
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
|
|
# `MAXIMIZE` and `MINIMIZE`.
|
|
#
|
|
# Defaults to `MAXIMIZE`.
|
|
},
|
|
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
|
|
"args": [ # Optional. Command line arguments to pass to the program.
|
|
"A String",
|
|
],
|
|
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
|
|
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
|
|
# and other data needed for training. This path is passed to your TensorFlow
|
|
# program as the 'job_dir' command-line argument. The benefit of specifying
|
|
# this field is that Cloud ML validates the path for use in training.
|
|
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
|
|
# the training program and any additional dependencies.
|
|
# The maximum number of package URIs is 100.
|
|
"A String",
|
|
],
|
|
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
|
|
# replica in the cluster will be of the type specified in `worker_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
|
|
# set this value, you must also set `worker_type`.
|
|
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's parameter server.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `master_type`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `parameter_server_count` is greater than zero.
|
|
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
|
|
# job. Each replica in the cluster will be of the type specified in
|
|
# `parameter_server_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
|
|
# set this value, you must also set `parameter_server_type`.
|
|
},
|
|
"startTime": "A String", # Output only. When the job processing was started.
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"jobId": "A String", # Required. The user-specified id of the job.
|
|
"state": "A String", # Output only. The detailed state of a job.
|
|
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
|
|
"modelName": "A String", # Use this field if you want to use the default version for the specified
|
|
# model. The string must use the following format:
|
|
#
|
|
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
|
|
# prediction. If not set, Google Cloud ML will pick the runtime version used
|
|
# during the CreateVersion request for this model version, or choose the
|
|
# latest stable version when model version information is not available
|
|
# such as when the model is specified by uri.
|
|
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
|
|
# May contain wildcards.
|
|
"A String",
|
|
],
|
|
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
|
|
# Defaults to 10 if not specified.
|
|
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
|
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# the model to use.
|
|
"outputPath": "A String", # Required. The output Google Cloud Storage location.
|
|
"dataFormat": "A String", # Required. The format of the input data files.
|
|
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
|
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# string is formatted the same way as `model_version`, with the addition
|
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# 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.
|
|
},
|
|
"endTime": "A String", # Output only. When the job processing was completed.
|
|
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
|
|
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
|
|
"nodeHours": 3.14, # Node hours used by the batch prediction job.
|
|
"predictionCount": "A String", # The number of generated predictions.
|
|
"errorCount": "A String", # The number of data instances which resulted in errors.
|
|
},
|
|
"createTime": "A String", # Output only. When the job was created.
|
|
}
|
|
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # Represents a training or prediction job.
|
|
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
|
|
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
|
|
# Only set for hyperparameter tuning jobs.
|
|
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
|
|
"trials": [ # Results for individual Hyperparameter trials.
|
|
# Only set for hyperparameter tuning jobs.
|
|
{ # Represents the result of a single hyperparameter tuning trial from a
|
|
# training job. The TrainingOutput object that is returned on successful
|
|
# completion of a training job with hyperparameter tuning includes a list
|
|
# of HyperparameterOutput objects, one for each successful trial.
|
|
"hyperparameters": { # The hyperparameters given to this trial.
|
|
"a_key": "A String",
|
|
},
|
|
"trialId": "A String", # The trial id for these results.
|
|
"allMetrics": [ # All recorded object metrics for this trial.
|
|
{ # An observed value of a metric.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
],
|
|
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
},
|
|
],
|
|
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
|
|
},
|
|
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
|
|
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's worker nodes.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `masterType`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `workerCount` is greater than zero.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
|
|
# set, Google Cloud ML will choose the latest stable version.
|
|
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
|
|
# and parameter servers.
|
|
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's master worker.
|
|
#
|
|
# The following types are supported:
|
|
#
|
|
# <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>
|
|
# <dt>standard_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to <code suppresswarning="true">standard</code> that
|
|
# also includes a
|
|
# <a href="/ml-engine/docs/how-tos/using-gpus">
|
|
# GPU that you can use in your trainer</a>.
|
|
# </dd>
|
|
# <dt>complex_model_m_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to
|
|
# <code suppresswarning="true">complex_model_m</code> that also includes
|
|
# four GPUs.
|
|
# </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.
|
|
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
|
|
# current versions of Tensorflow, this tag name should exactly match what is
|
|
# shown in Tensorboard, including all scopes. For versions of Tensorflow
|
|
# prior to 0.12, this should be only the tag passed to tf.Summary.
|
|
# By default, "training/hptuning/metric" will be used.
|
|
"params": [ # Required. The set of parameters to tune.
|
|
{ # Represents a single hyperparameter to optimize.
|
|
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is `INTEGER`.
|
|
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
|
|
"A String",
|
|
],
|
|
"discreteValues": [ # Required if type is `DISCRETE`.
|
|
# A list of feasible points.
|
|
# The list should be in strictly increasing order. For instance, this
|
|
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
|
|
# should not contain more than 1,000 values.
|
|
3.14,
|
|
],
|
|
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
|
|
# a HyperparameterSpec message. E.g., "learning_rate".
|
|
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is INTEGER.
|
|
"type": "A String", # Required. The type of the parameter.
|
|
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
|
|
# Leave unset for categorical parameters.
|
|
# Some kind of scaling is strongly recommended for real or integral
|
|
# parameters (e.g., `UNIT_LINEAR_SCALE`).
|
|
},
|
|
],
|
|
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
|
|
# You can reduce the time it takes to perform hyperparameter tuning by adding
|
|
# trials in parallel. However, each trail only benefits from the information
|
|
# gained in completed trials. That means that a trial does not get access to
|
|
# the results of trials running at the same time, which could reduce the
|
|
# quality of the overall optimization.
|
|
#
|
|
# Each trial will use the same scale tier and machine types.
|
|
#
|
|
# Defaults to one.
|
|
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
|
|
# `MAXIMIZE` and `MINIMIZE`.
|
|
#
|
|
# Defaults to `MAXIMIZE`.
|
|
},
|
|
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
|
|
"args": [ # Optional. Command line arguments to pass to the program.
|
|
"A String",
|
|
],
|
|
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
|
|
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
|
|
# and other data needed for training. This path is passed to your TensorFlow
|
|
# program as the 'job_dir' command-line argument. The benefit of specifying
|
|
# this field is that Cloud ML validates the path for use in training.
|
|
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
|
|
# the training program and any additional dependencies.
|
|
# The maximum number of package URIs is 100.
|
|
"A String",
|
|
],
|
|
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
|
|
# replica in the cluster will be of the type specified in `worker_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
|
|
# set this value, you must also set `worker_type`.
|
|
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's parameter server.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `master_type`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `parameter_server_count` is greater than zero.
|
|
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
|
|
# job. Each replica in the cluster will be of the type specified in
|
|
# `parameter_server_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
|
|
# set this value, you must also set `parameter_server_type`.
|
|
},
|
|
"startTime": "A String", # Output only. When the job processing was started.
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"jobId": "A String", # Required. The user-specified id of the job.
|
|
"state": "A String", # Output only. The detailed state of a job.
|
|
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
|
|
"modelName": "A String", # Use this field if you want to use the default version for the specified
|
|
# model. The string must use the following format:
|
|
#
|
|
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
|
|
# prediction. If not set, Google Cloud ML will pick the runtime version used
|
|
# during the CreateVersion request for this model version, or choose the
|
|
# latest stable version when model version information is not available
|
|
# such as when the model is specified by uri.
|
|
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
|
|
# May contain wildcards.
|
|
"A String",
|
|
],
|
|
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
|
|
# Defaults to 10 if not specified.
|
|
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
|
|
# the model to use.
|
|
"outputPath": "A String", # Required. The output Google Cloud Storage location.
|
|
"dataFormat": "A String", # Required. The format of the input data files.
|
|
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
|
|
# string is formatted the same way as `model_version`, with the addition
|
|
# of the version information:
|
|
#
|
|
# `"projects/<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.
|
|
},
|
|
"endTime": "A String", # Output only. When the job processing was completed.
|
|
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
|
|
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
|
|
"nodeHours": 3.14, # Node hours used by the batch prediction job.
|
|
"predictionCount": "A String", # The number of generated predictions.
|
|
"errorCount": "A String", # The number of data instances which resulted in errors.
|
|
},
|
|
"createTime": "A String", # Output only. When the job was created.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="get">get(name, 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. Output only. # The current training job result.
|
|
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
|
|
# Only set for hyperparameter tuning jobs.
|
|
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
|
|
"trials": [ # Results for individual Hyperparameter trials.
|
|
# Only set for hyperparameter tuning jobs.
|
|
{ # Represents the result of a single hyperparameter tuning trial from a
|
|
# training job. The TrainingOutput object that is returned on successful
|
|
# completion of a training job with hyperparameter tuning includes a list
|
|
# of HyperparameterOutput objects, one for each successful trial.
|
|
"hyperparameters": { # The hyperparameters given to this trial.
|
|
"a_key": "A String",
|
|
},
|
|
"trialId": "A String", # The trial id for these results.
|
|
"allMetrics": [ # All recorded object metrics for this trial.
|
|
{ # An observed value of a metric.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
],
|
|
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
},
|
|
],
|
|
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
|
|
},
|
|
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
|
|
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's worker nodes.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `masterType`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `workerCount` is greater than zero.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
|
|
# set, Google Cloud ML will choose the latest stable version.
|
|
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
|
|
# and parameter servers.
|
|
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's master worker.
|
|
#
|
|
# The following types are supported:
|
|
#
|
|
# <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>
|
|
# <dt>standard_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to <code suppresswarning="true">standard</code> that
|
|
# also includes a
|
|
# <a href="/ml-engine/docs/how-tos/using-gpus">
|
|
# GPU that you can use in your trainer</a>.
|
|
# </dd>
|
|
# <dt>complex_model_m_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to
|
|
# <code suppresswarning="true">complex_model_m</code> that also includes
|
|
# four GPUs.
|
|
# </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.
|
|
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
|
|
# current versions of Tensorflow, this tag name should exactly match what is
|
|
# shown in Tensorboard, including all scopes. For versions of Tensorflow
|
|
# prior to 0.12, this should be only the tag passed to tf.Summary.
|
|
# By default, "training/hptuning/metric" will be used.
|
|
"params": [ # Required. The set of parameters to tune.
|
|
{ # Represents a single hyperparameter to optimize.
|
|
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is `INTEGER`.
|
|
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
|
|
"A String",
|
|
],
|
|
"discreteValues": [ # Required if type is `DISCRETE`.
|
|
# A list of feasible points.
|
|
# The list should be in strictly increasing order. For instance, this
|
|
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
|
|
# should not contain more than 1,000 values.
|
|
3.14,
|
|
],
|
|
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
|
|
# a HyperparameterSpec message. E.g., "learning_rate".
|
|
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is INTEGER.
|
|
"type": "A String", # Required. The type of the parameter.
|
|
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
|
|
# Leave unset for categorical parameters.
|
|
# Some kind of scaling is strongly recommended for real or integral
|
|
# parameters (e.g., `UNIT_LINEAR_SCALE`).
|
|
},
|
|
],
|
|
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
|
|
# You can reduce the time it takes to perform hyperparameter tuning by adding
|
|
# trials in parallel. However, each trail only benefits from the information
|
|
# gained in completed trials. That means that a trial does not get access to
|
|
# the results of trials running at the same time, which could reduce the
|
|
# quality of the overall optimization.
|
|
#
|
|
# Each trial will use the same scale tier and machine types.
|
|
#
|
|
# Defaults to one.
|
|
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
|
|
# `MAXIMIZE` and `MINIMIZE`.
|
|
#
|
|
# Defaults to `MAXIMIZE`.
|
|
},
|
|
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
|
|
"args": [ # Optional. Command line arguments to pass to the program.
|
|
"A String",
|
|
],
|
|
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
|
|
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
|
|
# and other data needed for training. This path is passed to your TensorFlow
|
|
# program as the 'job_dir' command-line argument. The benefit of specifying
|
|
# this field is that Cloud ML validates the path for use in training.
|
|
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
|
|
# the training program and any additional dependencies.
|
|
# The maximum number of package URIs is 100.
|
|
"A String",
|
|
],
|
|
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
|
|
# replica in the cluster will be of the type specified in `worker_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
|
|
# set this value, you must also set `worker_type`.
|
|
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's parameter server.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `master_type`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `parameter_server_count` is greater than zero.
|
|
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
|
|
# job. Each replica in the cluster will be of the type specified in
|
|
# `parameter_server_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
|
|
# set this value, you must also set `parameter_server_type`.
|
|
},
|
|
"startTime": "A String", # Output only. When the job processing was started.
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"jobId": "A String", # Required. The user-specified id of the job.
|
|
"state": "A String", # Output only. The detailed state of a job.
|
|
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
|
|
"modelName": "A String", # Use this field if you want to use the default version for the specified
|
|
# model. The string must use the following format:
|
|
#
|
|
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
|
|
# prediction. If not set, Google Cloud ML will pick the runtime version used
|
|
# during the CreateVersion request for this model version, or choose the
|
|
# latest stable version when model version information is not available
|
|
# such as when the model is specified by uri.
|
|
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
|
|
# May contain wildcards.
|
|
"A String",
|
|
],
|
|
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
|
|
# Defaults to 10 if not specified.
|
|
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
|
|
# the model to use.
|
|
"outputPath": "A String", # Required. The output Google Cloud Storage location.
|
|
"dataFormat": "A String", # Required. The format of the input data files.
|
|
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
|
|
# string is formatted the same way as `model_version`, with the addition
|
|
# of the version information:
|
|
#
|
|
# `"projects/<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.
|
|
},
|
|
"endTime": "A String", # Output only. When the job processing was completed.
|
|
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
|
|
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
|
|
"nodeHours": 3.14, # Node hours used by the batch prediction job.
|
|
"predictionCount": "A String", # The number of generated predictions.
|
|
"errorCount": "A String", # The number of data instances which resulted in errors.
|
|
},
|
|
"createTime": "A String", # Output only. When the job was created.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="list">list(parent, 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. Output only. # The current training job result.
|
|
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
|
|
# Only set for hyperparameter tuning jobs.
|
|
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
|
|
"trials": [ # Results for individual Hyperparameter trials.
|
|
# Only set for hyperparameter tuning jobs.
|
|
{ # Represents the result of a single hyperparameter tuning trial from a
|
|
# training job. The TrainingOutput object that is returned on successful
|
|
# completion of a training job with hyperparameter tuning includes a list
|
|
# of HyperparameterOutput objects, one for each successful trial.
|
|
"hyperparameters": { # The hyperparameters given to this trial.
|
|
"a_key": "A String",
|
|
},
|
|
"trialId": "A String", # The trial id for these results.
|
|
"allMetrics": [ # All recorded object metrics for this trial.
|
|
{ # An observed value of a metric.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
],
|
|
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
|
|
"trainingStep": "A String", # The global training step for this metric.
|
|
"objectiveValue": 3.14, # The objective value at this training step.
|
|
},
|
|
},
|
|
],
|
|
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
|
|
},
|
|
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
|
|
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's worker nodes.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `masterType`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `workerCount` is greater than zero.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
|
|
# set, Google Cloud ML will choose the latest stable version.
|
|
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
|
|
# and parameter servers.
|
|
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's master worker.
|
|
#
|
|
# The following types are supported:
|
|
#
|
|
# <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>
|
|
# <dt>standard_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to <code suppresswarning="true">standard</code> that
|
|
# also includes a
|
|
# <a href="/ml-engine/docs/how-tos/using-gpus">
|
|
# GPU that you can use in your trainer</a>.
|
|
# </dd>
|
|
# <dt>complex_model_m_gpu</dt>
|
|
# <dd>
|
|
# A machine equivalent to
|
|
# <code suppresswarning="true">complex_model_m</code> that also includes
|
|
# four GPUs.
|
|
# </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.
|
|
"hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
|
|
# current versions of Tensorflow, this tag name should exactly match what is
|
|
# shown in Tensorboard, including all scopes. For versions of Tensorflow
|
|
# prior to 0.12, this should be only the tag passed to tf.Summary.
|
|
# By default, "training/hptuning/metric" will be used.
|
|
"params": [ # Required. The set of parameters to tune.
|
|
{ # Represents a single hyperparameter to optimize.
|
|
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is `INTEGER`.
|
|
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
|
|
"A String",
|
|
],
|
|
"discreteValues": [ # Required if type is `DISCRETE`.
|
|
# A list of feasible points.
|
|
# The list should be in strictly increasing order. For instance, this
|
|
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
|
|
# should not contain more than 1,000 values.
|
|
3.14,
|
|
],
|
|
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
|
|
# a HyperparameterSpec message. E.g., "learning_rate".
|
|
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
|
|
# should be unset if type is `CATEGORICAL`. This value should be integers if
|
|
# type is INTEGER.
|
|
"type": "A String", # Required. The type of the parameter.
|
|
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
|
|
# Leave unset for categorical parameters.
|
|
# Some kind of scaling is strongly recommended for real or integral
|
|
# parameters (e.g., `UNIT_LINEAR_SCALE`).
|
|
},
|
|
],
|
|
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
|
|
# You can reduce the time it takes to perform hyperparameter tuning by adding
|
|
# trials in parallel. However, each trail only benefits from the information
|
|
# gained in completed trials. That means that a trial does not get access to
|
|
# the results of trials running at the same time, which could reduce the
|
|
# quality of the overall optimization.
|
|
#
|
|
# Each trial will use the same scale tier and machine types.
|
|
#
|
|
# Defaults to one.
|
|
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
|
|
# `MAXIMIZE` and `MINIMIZE`.
|
|
#
|
|
# Defaults to `MAXIMIZE`.
|
|
},
|
|
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
|
|
"args": [ # Optional. Command line arguments to pass to the program.
|
|
"A String",
|
|
],
|
|
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
|
|
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
|
|
# and other data needed for training. This path is passed to your TensorFlow
|
|
# program as the 'job_dir' command-line argument. The benefit of specifying
|
|
# this field is that Cloud ML validates the path for use in training.
|
|
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
|
|
# the training program and any additional dependencies.
|
|
# The maximum number of package URIs is 100.
|
|
"A String",
|
|
],
|
|
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
|
|
# replica in the cluster will be of the type specified in `worker_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
|
|
# set this value, you must also set `worker_type`.
|
|
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
|
|
# job's parameter server.
|
|
#
|
|
# The supported values are the same as those described in the entry for
|
|
# `master_type`.
|
|
#
|
|
# This value must be present when `scaleTier` is set to `CUSTOM` and
|
|
# `parameter_server_count` is greater than zero.
|
|
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
|
|
# job. Each replica in the cluster will be of the type specified in
|
|
# `parameter_server_type`.
|
|
#
|
|
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
|
|
# set this value, you must also set `parameter_server_type`.
|
|
},
|
|
"startTime": "A String", # Output only. When the job processing was started.
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"jobId": "A String", # Required. The user-specified id of the job.
|
|
"state": "A String", # Output only. The detailed state of a job.
|
|
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
|
|
"modelName": "A String", # Use this field if you want to use the default version for the specified
|
|
# model. The string must use the following format:
|
|
#
|
|
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
|
|
# prediction. If not set, Google Cloud ML will pick the runtime version used
|
|
# during the CreateVersion request for this model version, or choose the
|
|
# latest stable version when model version information is not available
|
|
# such as when the model is specified by uri.
|
|
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
|
|
# May contain wildcards.
|
|
"A String",
|
|
],
|
|
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
|
|
# Defaults to 10 if not specified.
|
|
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
|
|
# the model to use.
|
|
"outputPath": "A String", # Required. The output Google Cloud Storage location.
|
|
"dataFormat": "A String", # Required. The format of the input data files.
|
|
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
|
|
# string is formatted the same way as `model_version`, with the addition
|
|
# of the version information:
|
|
#
|
|
# `"projects/<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.
|
|
},
|
|
"endTime": "A String", # Output only. When the job processing was completed.
|
|
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
|
|
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
|
|
"nodeHours": 3.14, # Node hours used by the batch prediction job.
|
|
"predictionCount": "A String", # The number of generated predictions.
|
|
"errorCount": "A String", # The number of data instances which resulted in errors.
|
|
},
|
|
"createTime": "A String", # Output only. When the job was created.
|
|
},
|
|
],
|
|
}</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> |