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gerrit-public.fairphone.software / platform / external / python / google-api-python-client / e833b79ffee531ae64e11175718bb2fffc504950 / . / docs / dyn / ml_v1beta1.projects.jobs.html
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Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]1<html><body>
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Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]75<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>
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]76<h2>Instance Methods</h2>
77<p class="toc_element">
78 <code><a href="#cancel">cancel(name=None, body, x__xgafv=None)</a></code></p>
79<p class="firstline">Cancels a running job.</p>
80<p class="toc_element">
81 <code><a href="#create">create(parent=None, body, x__xgafv=None)</a></code></p>
82<p class="firstline">Creates a training or a batch prediction job.</p>
83<p class="toc_element">
84 <code><a href="#get">get(name=None, x__xgafv=None)</a></code></p>
85<p class="firstline">Describes a job.</p>
86<p class="toc_element">
87 <code><a href="#list">list(parent=None, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</a></code></p>
88<p class="firstline">Lists the jobs in the project.</p>
89<p class="toc_element">
90 <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
91<p class="firstline">Retrieves the next page of results.</p>
92<h3>Method Details</h3>
93<div class="method">
94 <code class="details" id="cancel">cancel(name=None, body, x__xgafv=None)</code>
95 <pre>Cancels a running job.
96
97Args:
98 name: string, Required. The name of the job to cancel.
99
100Authorization: requires `Editor` role on the parent project. (required)
101 body: object, The request body. (required)
102 The object takes the form of:
103
104{ # Request message for the CancelJob method.
105 }
106
107 x__xgafv: string, V1 error format.
108 Allowed values
109 1 - v1 error format
110 2 - v2 error format
111
112Returns:
113 An object of the form:
114
115 { # A generic empty message that you can re-use to avoid defining duplicated
116 # empty messages in your APIs. A typical example is to use it as the request
117 # or the response type of an API method. For instance:
118 #
119 # service Foo {
120 # rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
121 # }
122 #
123 # The JSON representation for `Empty` is empty JSON object `{}`.
124 }</pre>
125</div>
126
127<div class="method">
128 <code class="details" id="create">create(parent=None, body, x__xgafv=None)</code>
129 <pre>Creates a training or a batch prediction job.
130
131Args:
132 parent: string, Required. The project name.
133
134Authorization: requires `Editor` role on the specified project. (required)
135 body: object, The request body. (required)
136 The object takes the form of:
137
138{ # Represents a training or prediction job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]139 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]140 "trials": [ # Results for individual Hyperparameter trials.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]141 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]142 { # Represents the result of a single hyperparameter tuning trial from a
143 # training job. The TrainingOutput object that is returned on successful
144 # completion of a training job with hyperparameter tuning includes a list
145 # of HyperparameterOutput objects, one for each successful trial.
146 "hyperparameters": { # The hyperparameters given to this trial.
147 "a_key": "A String",
148 },
149 "trialId": "A String", # The trial id for these results.
150 "allMetrics": [ # All recorded object metrics for this trial.
151 { # An observed value of a metric.
152 "trainingStep": "A String", # The global training step for this metric.
153 "objectiveValue": 3.14, # The objective value at this training step.
154 },
155 ],
156 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
157 "trainingStep": "A String", # The global training step for this metric.
158 "objectiveValue": 3.14, # The objective value at this training step.
159 },
160 },
161 ],
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]162 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]163 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]164 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
165 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]166 },
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]167 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
168 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
169 # job's worker nodes.
170 #
171 # The supported values are the same as those described in the entry for
172 # `masterType`.
173 #
174 # This value must be present when `scaleTier` is set to `CUSTOM` and
175 # `workerCount` is greater than zero.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]176 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
177 # set, Google Cloud ML will choose the latest stable version.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]178 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
179 # and parameter servers.
180 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
181 # job's master worker.
182 #
183 # The following types are supported:
184 #
185 # <dl>
186 # <dt>standard</dt>
187 # <dd>
188 # A basic machine configuration suitable for training simple models with
189 # small to moderate datasets.
190 # </dd>
191 # <dt>large_model</dt>
192 # <dd>
193 # A machine with a lot of memory, specially suited for parameter servers
194 # when your model is large (having many hidden layers or layers with very
195 # large numbers of nodes).
196 # </dd>
197 # <dt>complex_model_s</dt>
198 # <dd>
199 # A machine suitable for the master and workers of the cluster when your
200 # model requires more computation than the standard machine can handle
201 # satisfactorily.
202 # </dd>
203 # <dt>complex_model_m</dt>
204 # <dd>
205 # A machine with roughly twice the number of cores and roughly double the
206 # memory of <code suppresswarning="true">complex_model_s</code>.
207 # </dd>
208 # <dt>complex_model_l</dt>
209 # <dd>
210 # A machine with roughly twice the number of cores and roughly double the
211 # memory of <code suppresswarning="true">complex_model_m</code>.
212 # </dd>
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]213 # <dt>standard_gpu</dt>
214 # <dd>
215 # A machine equivalent to <code suppresswarning="true">standard</code> that
216 # also includes a
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]217 # <a href="/ml-engine/docs/how-tos/using-gpus">
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]218 # GPU that you can use in your trainer</a>.
219 # </dd>
220 # <dt>complex_model_m_gpu</dt>
221 # <dd>
222 # A machine equivalent to
223 # <code suppresswarning="true">coplex_model_m</code> that also includes
224 # four GPUs.
225 # </dd>
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]226 # </dl>
227 #
228 # You must set this value when `scaleTier` is set to `CUSTOM`.
229 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
230 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
231 # the specified hyperparameters.
232 #
233 # Defaults to one.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]234 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
235 # current versions of Tensorflow, this tag name should exactly match what is
236 # shown in Tensorboard, including all scopes. For versions of Tensorflow
237 # prior to 0.12, this should be only the tag passed to tf.Summary.
238 # By default, "training/hptuning/metric" will be used.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]239 "params": [ # Required. The set of parameters to tune.
240 { # Represents a single hyperparameter to optimize.
241 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
242 # should be unset if type is `CATEGORICAL`. This value should be integers if
243 # type is `INTEGER`.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]244 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
245 # should be unset if type is `CATEGORICAL`. This value should be integers if
246 # type is INTEGER.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]247 "discreteValues": [ # Required if type is `DISCRETE`.
248 # A list of feasible points.
249 # The list should be in strictly increasing order. For instance, this
250 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
251 # should not contain more than 1,000 values.
252 3.14,
253 ],
254 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
255 # a HyperparameterSpec message. E.g., "learning_rate".
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]256 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
257 "A String",
258 ],
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]259 "type": "A String", # Required. The type of the parameter.
260 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
261 # Leave unset for categorical parameters.
262 # Some kind of scaling is strongly recommended for real or integral
263 # parameters (e.g., `UNIT_LINEAR_SCALE`).
264 },
265 ],
266 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
267 # You can reduce the time it takes to perform hyperparameter tuning by adding
268 # trials in parallel. However, each trail only benefits from the information
269 # gained in completed trials. That means that a trial does not get access to
270 # the results of trials running at the same time, which could reduce the
271 # quality of the overall optimization.
272 #
273 # Each trial will use the same scale tier and machine types.
274 #
275 # Defaults to one.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]276 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
277 # `MAXIMIZE` and `MINIMIZE`.
278 #
279 # Defaults to `MAXIMIZE`.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]280 },
281 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
282 "args": [ # Optional. Command line arguments to pass to the program.
283 "A String",
284 ],
285 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]286 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
287 # and other data needed for training. This path is passed to your TensorFlow
288 # program as the 'job_dir' command-line argument. The benefit of specifying
289 # this field is that Cloud ML validates the path for use in training.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]290 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
291 # the training program and any additional dependencies.
292 "A String",
293 ],
294 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
295 # replica in the cluster will be of the type specified in `worker_type`.
296 #
297 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
298 # set this value, you must also set `worker_type`.
299 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
300 # job's parameter server.
301 #
302 # The supported values are the same as those described in the entry for
303 # `master_type`.
304 #
305 # This value must be present when `scaleTier` is set to `CUSTOM` and
306 # `parameter_server_count` is greater than zero.
307 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
308 # job. Each replica in the cluster will be of the type specified in
309 # `parameter_server_type`.
310 #
311 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
312 # set this value, you must also set `parameter_server_type`.
313 },
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]314 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
315 "modelName": "A String", # Use this field if you want to use the default version for the specified
316 # model. The string must use the following format:
317 #
318 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
319 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
320 # prediction. If not set, Google Cloud ML will pick the runtime version used
321 # during the CreateVersion request for this model version, or choose the
322 # latest stable version when model version information is not available
323 # such as when the model is specified by uri.
324 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
325 # May contain wildcards.
326 "A String",
327 ],
328 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
329 # Defaults to 10 if not specified.
330 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
331 # the model to use.
332 "outputPath": "A String", # Required. The output Google Cloud Storage location.
333 "dataFormat": "A String", # Required. The format of the input data files.
334 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
335 # string is formatted the same way as `model_version`, with the addition
336 # of the version information:
337 #
338 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
339 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
340 },
341 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
342 "jobId": "A String", # Required. The user-specified id of the job.
343 "state": "A String", # Output only. The detailed state of a job.
344 "startTime": "A String", # Output only. When the job processing was started.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]345 "endTime": "A String", # Output only. When the job processing was completed.
346 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
347 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]348 "nodeHours": 3.14, # Node hours used by the batch prediction job.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]349 "predictionCount": "A String", # The number of generated predictions.
350 "errorCount": "A String", # The number of data instances which resulted in errors.
351 },
352 "createTime": "A String", # Output only. When the job was created.
353 }
354
355 x__xgafv: string, V1 error format.
356 Allowed values
357 1 - v1 error format
358 2 - v2 error format
359
360Returns:
361 An object of the form:
362
363 { # Represents a training or prediction job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]364 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]365 "trials": [ # Results for individual Hyperparameter trials.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]366 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]367 { # Represents the result of a single hyperparameter tuning trial from a
368 # training job. The TrainingOutput object that is returned on successful
369 # completion of a training job with hyperparameter tuning includes a list
370 # of HyperparameterOutput objects, one for each successful trial.
371 "hyperparameters": { # The hyperparameters given to this trial.
372 "a_key": "A String",
373 },
374 "trialId": "A String", # The trial id for these results.
375 "allMetrics": [ # All recorded object metrics for this trial.
376 { # An observed value of a metric.
377 "trainingStep": "A String", # The global training step for this metric.
378 "objectiveValue": 3.14, # The objective value at this training step.
379 },
380 ],
381 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
382 "trainingStep": "A String", # The global training step for this metric.
383 "objectiveValue": 3.14, # The objective value at this training step.
384 },
385 },
386 ],
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]387 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]388 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]389 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
390 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]391 },
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]392 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
393 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
394 # job's worker nodes.
395 #
396 # The supported values are the same as those described in the entry for
397 # `masterType`.
398 #
399 # This value must be present when `scaleTier` is set to `CUSTOM` and
400 # `workerCount` is greater than zero.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]401 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
402 # set, Google Cloud ML will choose the latest stable version.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]403 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
404 # and parameter servers.
405 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
406 # job's master worker.
407 #
408 # The following types are supported:
409 #
410 # <dl>
411 # <dt>standard</dt>
412 # <dd>
413 # A basic machine configuration suitable for training simple models with
414 # small to moderate datasets.
415 # </dd>
416 # <dt>large_model</dt>
417 # <dd>
418 # A machine with a lot of memory, specially suited for parameter servers
419 # when your model is large (having many hidden layers or layers with very
420 # large numbers of nodes).
421 # </dd>
422 # <dt>complex_model_s</dt>
423 # <dd>
424 # A machine suitable for the master and workers of the cluster when your
425 # model requires more computation than the standard machine can handle
426 # satisfactorily.
427 # </dd>
428 # <dt>complex_model_m</dt>
429 # <dd>
430 # A machine with roughly twice the number of cores and roughly double the
431 # memory of <code suppresswarning="true">complex_model_s</code>.
432 # </dd>
433 # <dt>complex_model_l</dt>
434 # <dd>
435 # A machine with roughly twice the number of cores and roughly double the
436 # memory of <code suppresswarning="true">complex_model_m</code>.
437 # </dd>
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]438 # <dt>standard_gpu</dt>
439 # <dd>
440 # A machine equivalent to <code suppresswarning="true">standard</code> that
441 # also includes a
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]442 # <a href="/ml-engine/docs/how-tos/using-gpus">
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]443 # GPU that you can use in your trainer</a>.
444 # </dd>
445 # <dt>complex_model_m_gpu</dt>
446 # <dd>
447 # A machine equivalent to
448 # <code suppresswarning="true">coplex_model_m</code> that also includes
449 # four GPUs.
450 # </dd>
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]451 # </dl>
452 #
453 # You must set this value when `scaleTier` is set to `CUSTOM`.
454 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
455 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
456 # the specified hyperparameters.
457 #
458 # Defaults to one.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]459 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
460 # current versions of Tensorflow, this tag name should exactly match what is
461 # shown in Tensorboard, including all scopes. For versions of Tensorflow
462 # prior to 0.12, this should be only the tag passed to tf.Summary.
463 # By default, "training/hptuning/metric" will be used.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]464 "params": [ # Required. The set of parameters to tune.
465 { # Represents a single hyperparameter to optimize.
466 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
467 # should be unset if type is `CATEGORICAL`. This value should be integers if
468 # type is `INTEGER`.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]469 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
470 # should be unset if type is `CATEGORICAL`. This value should be integers if
471 # type is INTEGER.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]472 "discreteValues": [ # Required if type is `DISCRETE`.
473 # A list of feasible points.
474 # The list should be in strictly increasing order. For instance, this
475 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
476 # should not contain more than 1,000 values.
477 3.14,
478 ],
479 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
480 # a HyperparameterSpec message. E.g., "learning_rate".
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]481 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
482 "A String",
483 ],
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]484 "type": "A String", # Required. The type of the parameter.
485 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
486 # Leave unset for categorical parameters.
487 # Some kind of scaling is strongly recommended for real or integral
488 # parameters (e.g., `UNIT_LINEAR_SCALE`).
489 },
490 ],
491 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
492 # You can reduce the time it takes to perform hyperparameter tuning by adding
493 # trials in parallel. However, each trail only benefits from the information
494 # gained in completed trials. That means that a trial does not get access to
495 # the results of trials running at the same time, which could reduce the
496 # quality of the overall optimization.
497 #
498 # Each trial will use the same scale tier and machine types.
499 #
500 # Defaults to one.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]501 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
502 # `MAXIMIZE` and `MINIMIZE`.
503 #
504 # Defaults to `MAXIMIZE`.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]505 },
506 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
507 "args": [ # Optional. Command line arguments to pass to the program.
508 "A String",
509 ],
510 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]511 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
512 # and other data needed for training. This path is passed to your TensorFlow
513 # program as the 'job_dir' command-line argument. The benefit of specifying
514 # this field is that Cloud ML validates the path for use in training.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]515 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
516 # the training program and any additional dependencies.
517 "A String",
518 ],
519 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
520 # replica in the cluster will be of the type specified in `worker_type`.
521 #
522 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
523 # set this value, you must also set `worker_type`.
524 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
525 # job's parameter server.
526 #
527 # The supported values are the same as those described in the entry for
528 # `master_type`.
529 #
530 # This value must be present when `scaleTier` is set to `CUSTOM` and
531 # `parameter_server_count` is greater than zero.
532 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
533 # job. Each replica in the cluster will be of the type specified in
534 # `parameter_server_type`.
535 #
536 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
537 # set this value, you must also set `parameter_server_type`.
538 },
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]539 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
540 "modelName": "A String", # Use this field if you want to use the default version for the specified
541 # model. The string must use the following format:
542 #
543 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
544 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
545 # prediction. If not set, Google Cloud ML will pick the runtime version used
546 # during the CreateVersion request for this model version, or choose the
547 # latest stable version when model version information is not available
548 # such as when the model is specified by uri.
549 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
550 # May contain wildcards.
551 "A String",
552 ],
553 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
554 # Defaults to 10 if not specified.
555 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
556 # the model to use.
557 "outputPath": "A String", # Required. The output Google Cloud Storage location.
558 "dataFormat": "A String", # Required. The format of the input data files.
559 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
560 # string is formatted the same way as `model_version`, with the addition
561 # of the version information:
562 #
563 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
564 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
565 },
566 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
567 "jobId": "A String", # Required. The user-specified id of the job.
568 "state": "A String", # Output only. The detailed state of a job.
569 "startTime": "A String", # Output only. When the job processing was started.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]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.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]573 "nodeHours": 3.14, # Node hours used by the batch prediction job.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]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.
578 }</pre>
579</div>
580
581<div class="method">
582 <code class="details" id="get">get(name=None, x__xgafv=None)</code>
583 <pre>Describes a job.
584
585Args:
586 name: string, Required. The name of the job to get the description of.
587
588Authorization: requires `Viewer` role on the parent project. (required)
589 x__xgafv: string, V1 error format.
590 Allowed values
591 1 - v1 error format
592 2 - v2 error format
593
594Returns:
595 An object of the form:
596
597 { # Represents a training or prediction job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]598 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]599 "trials": [ # Results for individual Hyperparameter trials.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]600 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]601 { # Represents the result of a single hyperparameter tuning trial from a
602 # training job. The TrainingOutput object that is returned on successful
603 # completion of a training job with hyperparameter tuning includes a list
604 # of HyperparameterOutput objects, one for each successful trial.
605 "hyperparameters": { # The hyperparameters given to this trial.
606 "a_key": "A String",
607 },
608 "trialId": "A String", # The trial id for these results.
609 "allMetrics": [ # All recorded object metrics for this trial.
610 { # An observed value of a metric.
611 "trainingStep": "A String", # The global training step for this metric.
612 "objectiveValue": 3.14, # The objective value at this training step.
613 },
614 ],
615 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
616 "trainingStep": "A String", # The global training step for this metric.
617 "objectiveValue": 3.14, # The objective value at this training step.
618 },
619 },
620 ],
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]621 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]622 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]623 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
624 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]625 },
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]626 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
627 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
628 # job's worker nodes.
629 #
630 # The supported values are the same as those described in the entry for
631 # `masterType`.
632 #
633 # This value must be present when `scaleTier` is set to `CUSTOM` and
634 # `workerCount` is greater than zero.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]635 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
636 # set, Google Cloud ML will choose the latest stable version.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]637 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
638 # and parameter servers.
639 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
640 # job's master worker.
641 #
642 # The following types are supported:
643 #
644 # <dl>
645 # <dt>standard</dt>
646 # <dd>
647 # A basic machine configuration suitable for training simple models with
648 # small to moderate datasets.
649 # </dd>
650 # <dt>large_model</dt>
651 # <dd>
652 # A machine with a lot of memory, specially suited for parameter servers
653 # when your model is large (having many hidden layers or layers with very
654 # large numbers of nodes).
655 # </dd>
656 # <dt>complex_model_s</dt>
657 # <dd>
658 # A machine suitable for the master and workers of the cluster when your
659 # model requires more computation than the standard machine can handle
660 # satisfactorily.
661 # </dd>
662 # <dt>complex_model_m</dt>
663 # <dd>
664 # A machine with roughly twice the number of cores and roughly double the
665 # memory of <code suppresswarning="true">complex_model_s</code>.
666 # </dd>
667 # <dt>complex_model_l</dt>
668 # <dd>
669 # A machine with roughly twice the number of cores and roughly double the
670 # memory of <code suppresswarning="true">complex_model_m</code>.
671 # </dd>
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]672 # <dt>standard_gpu</dt>
673 # <dd>
674 # A machine equivalent to <code suppresswarning="true">standard</code> that
675 # also includes a
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]676 # <a href="/ml-engine/docs/how-tos/using-gpus">
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]677 # GPU that you can use in your trainer</a>.
678 # </dd>
679 # <dt>complex_model_m_gpu</dt>
680 # <dd>
681 # A machine equivalent to
682 # <code suppresswarning="true">coplex_model_m</code> that also includes
683 # four GPUs.
684 # </dd>
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]685 # </dl>
686 #
687 # You must set this value when `scaleTier` is set to `CUSTOM`.
688 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
689 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
690 # the specified hyperparameters.
691 #
692 # Defaults to one.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]693 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
694 # current versions of Tensorflow, this tag name should exactly match what is
695 # shown in Tensorboard, including all scopes. For versions of Tensorflow
696 # prior to 0.12, this should be only the tag passed to tf.Summary.
697 # By default, "training/hptuning/metric" will be used.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]698 "params": [ # Required. The set of parameters to tune.
699 { # Represents a single hyperparameter to optimize.
700 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
701 # should be unset if type is `CATEGORICAL`. This value should be integers if
702 # type is `INTEGER`.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]703 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
704 # should be unset if type is `CATEGORICAL`. This value should be integers if
705 # type is INTEGER.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]706 "discreteValues": [ # Required if type is `DISCRETE`.
707 # A list of feasible points.
708 # The list should be in strictly increasing order. For instance, this
709 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
710 # should not contain more than 1,000 values.
711 3.14,
712 ],
713 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
714 # a HyperparameterSpec message. E.g., "learning_rate".
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]715 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
716 "A String",
717 ],
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]718 "type": "A String", # Required. The type of the parameter.
719 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
720 # Leave unset for categorical parameters.
721 # Some kind of scaling is strongly recommended for real or integral
722 # parameters (e.g., `UNIT_LINEAR_SCALE`).
723 },
724 ],
725 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
726 # You can reduce the time it takes to perform hyperparameter tuning by adding
727 # trials in parallel. However, each trail only benefits from the information
728 # gained in completed trials. That means that a trial does not get access to
729 # the results of trials running at the same time, which could reduce the
730 # quality of the overall optimization.
731 #
732 # Each trial will use the same scale tier and machine types.
733 #
734 # Defaults to one.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]735 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
736 # `MAXIMIZE` and `MINIMIZE`.
737 #
738 # Defaults to `MAXIMIZE`.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]739 },
740 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
741 "args": [ # Optional. Command line arguments to pass to the program.
742 "A String",
743 ],
744 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]745 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
746 # and other data needed for training. This path is passed to your TensorFlow
747 # program as the 'job_dir' command-line argument. The benefit of specifying
748 # this field is that Cloud ML validates the path for use in training.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]749 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
750 # the training program and any additional dependencies.
751 "A String",
752 ],
753 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
754 # replica in the cluster will be of the type specified in `worker_type`.
755 #
756 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
757 # set this value, you must also set `worker_type`.
758 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
759 # job's parameter server.
760 #
761 # The supported values are the same as those described in the entry for
762 # `master_type`.
763 #
764 # This value must be present when `scaleTier` is set to `CUSTOM` and
765 # `parameter_server_count` is greater than zero.
766 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
767 # job. Each replica in the cluster will be of the type specified in
768 # `parameter_server_type`.
769 #
770 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
771 # set this value, you must also set `parameter_server_type`.
772 },
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]773 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
774 "modelName": "A String", # Use this field if you want to use the default version for the specified
775 # model. The string must use the following format:
776 #
777 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
778 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
779 # prediction. If not set, Google Cloud ML will pick the runtime version used
780 # during the CreateVersion request for this model version, or choose the
781 # latest stable version when model version information is not available
782 # such as when the model is specified by uri.
783 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
784 # May contain wildcards.
785 "A String",
786 ],
787 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
788 # Defaults to 10 if not specified.
789 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
790 # the model to use.
791 "outputPath": "A String", # Required. The output Google Cloud Storage location.
792 "dataFormat": "A String", # Required. The format of the input data files.
793 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
794 # string is formatted the same way as `model_version`, with the addition
795 # of the version information:
796 #
797 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
798 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
799 },
800 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
801 "jobId": "A String", # Required. The user-specified id of the job.
802 "state": "A String", # Output only. The detailed state of a job.
803 "startTime": "A String", # Output only. When the job processing was started.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[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.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]807 "nodeHours": 3.14, # Node hours used by the batch prediction job.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]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.
812 }</pre>
813</div>
814
815<div class="method">
816 <code class="details" id="list">list(parent=None, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code>
817 <pre>Lists the jobs in the project.
818
819Args:
820 parent: string, Required. The name of the project for which to list jobs.
821
822Authorization: requires `Viewer` role on the specified project. (required)
823 pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there
824are more remaining results than this number, the response message will
825contain a valid value in the `next_page_token` field.
826
827The 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
831You get the token from the `next_page_token` field of the response from
832the previous call.
833 x__xgafv: string, V1 error format.
834 Allowed values
835 1 - v1 error format
836 2 - v2 error format
837
838Returns:
839 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 Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]846 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]847 "trials": [ # Results for individual Hyperparameter trials.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]848 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]849 { # Represents the result of a single hyperparameter tuning trial from a
850 # training job. The TrainingOutput object that is returned on successful
851 # completion of a training job with hyperparameter tuning includes a list
852 # of HyperparameterOutput objects, one for each successful trial.
853 "hyperparameters": { # The hyperparameters given to this trial.
854 "a_key": "A String",
855 },
856 "trialId": "A String", # The trial id for these results.
857 "allMetrics": [ # All recorded object metrics for this trial.
858 { # An observed value of a metric.
859 "trainingStep": "A String", # The global training step for this metric.
860 "objectiveValue": 3.14, # The objective value at this training step.
861 },
862 ],
863 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
864 "trainingStep": "A String", # The global training step for this metric.
865 "objectiveValue": 3.14, # The objective value at this training step.
866 },
867 },
868 ],
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]869 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]870 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]871 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
872 # Only set for hyperparameter tuning jobs.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]873 },
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]874 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
875 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
876 # job's worker nodes.
877 #
878 # The supported values are the same as those described in the entry for
879 # `masterType`.
880 #
881 # This value must be present when `scaleTier` is set to `CUSTOM` and
882 # `workerCount` is greater than zero.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]883 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
884 # set, Google Cloud ML will choose the latest stable version.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]885 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
886 # and parameter servers.
887 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
888 # job's master worker.
889 #
890 # The following types are supported:
891 #
892 # <dl>
893 # <dt>standard</dt>
894 # <dd>
895 # A basic machine configuration suitable for training simple models with
896 # small to moderate datasets.
897 # </dd>
898 # <dt>large_model</dt>
899 # <dd>
900 # A machine with a lot of memory, specially suited for parameter servers
901 # when your model is large (having many hidden layers or layers with very
902 # large numbers of nodes).
903 # </dd>
904 # <dt>complex_model_s</dt>
905 # <dd>
906 # A machine suitable for the master and workers of the cluster when your
907 # model requires more computation than the standard machine can handle
908 # satisfactorily.
909 # </dd>
910 # <dt>complex_model_m</dt>
911 # <dd>
912 # A machine with roughly twice the number of cores and roughly double the
913 # memory of <code suppresswarning="true">complex_model_s</code>.
914 # </dd>
915 # <dt>complex_model_l</dt>
916 # <dd>
917 # A machine with roughly twice the number of cores and roughly double the
918 # memory of <code suppresswarning="true">complex_model_m</code>.
919 # </dd>
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]920 # <dt>standard_gpu</dt>
921 # <dd>
922 # A machine equivalent to <code suppresswarning="true">standard</code> that
923 # also includes a
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]924 # <a href="/ml-engine/docs/how-tos/using-gpus">
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]925 # GPU that you can use in your trainer</a>.
926 # </dd>
927 # <dt>complex_model_m_gpu</dt>
928 # <dd>
929 # A machine equivalent to
930 # <code suppresswarning="true">coplex_model_m</code> that also includes
931 # four GPUs.
932 # </dd>
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]933 # </dl>
934 #
935 # You must set this value when `scaleTier` is set to `CUSTOM`.
936 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
937 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
938 # the specified hyperparameters.
939 #
940 # Defaults to one.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]941 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
942 # current versions of Tensorflow, this tag name should exactly match what is
943 # shown in Tensorboard, including all scopes. For versions of Tensorflow
944 # prior to 0.12, this should be only the tag passed to tf.Summary.
945 # By default, "training/hptuning/metric" will be used.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]946 "params": [ # Required. The set of parameters to tune.
947 { # Represents a single hyperparameter to optimize.
948 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
949 # should be unset if type is `CATEGORICAL`. This value should be integers if
950 # type is `INTEGER`.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]951 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
952 # should be unset if type is `CATEGORICAL`. This value should be integers if
953 # type is INTEGER.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]954 "discreteValues": [ # Required if type is `DISCRETE`.
955 # A list of feasible points.
956 # The list should be in strictly increasing order. For instance, this
957 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
958 # should not contain more than 1,000 values.
959 3.14,
960 ],
961 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
962 # a HyperparameterSpec message. E.g., "learning_rate".
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]963 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
964 "A String",
965 ],
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]966 "type": "A String", # Required. The type of the parameter.
967 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
968 # Leave unset for categorical parameters.
969 # Some kind of scaling is strongly recommended for real or integral
970 # parameters (e.g., `UNIT_LINEAR_SCALE`).
971 },
972 ],
973 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
974 # You can reduce the time it takes to perform hyperparameter tuning by adding
975 # trials in parallel. However, each trail only benefits from the information
976 # gained in completed trials. That means that a trial does not get access to
977 # the results of trials running at the same time, which could reduce the
978 # quality of the overall optimization.
979 #
980 # Each trial will use the same scale tier and machine types.
981 #
982 # Defaults to one.
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]983 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
984 # `MAXIMIZE` and `MINIMIZE`.
985 #
986 # Defaults to `MAXIMIZE`.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]987 },
988 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
989 "args": [ # Optional. Command line arguments to pass to the program.
990 "A String",
991 ],
992 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]993 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
994 # and other data needed for training. This path is passed to your TensorFlow
995 # program as the 'job_dir' command-line argument. The benefit of specifying
996 # this field is that Cloud ML validates the path for use in training.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]997 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
998 # the training program and any additional dependencies.
999 "A String",
1000 ],
1001 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
1002 # replica in the cluster will be of the type specified in `worker_type`.
1003 #
1004 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
1005 # set this value, you must also set `worker_type`.
1006 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
1007 # job's parameter server.
1008 #
1009 # The supported values are the same as those described in the entry for
1010 # `master_type`.
1011 #
1012 # This value must be present when `scaleTier` is set to `CUSTOM` and
1013 # `parameter_server_count` is greater than zero.
1014 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
1015 # job. Each replica in the cluster will be of the type specified in
1016 # `parameter_server_type`.
1017 #
1018 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
1019 # set this value, you must also set `parameter_server_type`.
1020 },
Sai Cheemalapatie833b792017-03-24 15:06:46 -0700[diff] [blame^]1021 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
1022 "modelName": "A String", # Use this field if you want to use the default version for the specified
1023 # model. The string must use the following format:
1024 #
1025 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
1026 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
1027 # prediction. If not set, Google Cloud ML will pick the runtime version used
1028 # during the CreateVersion request for this model version, or choose the
1029 # latest stable version when model version information is not available
1030 # such as when the model is specified by uri.
1031 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
1032 # May contain wildcards.
1033 "A String",
1034 ],
1035 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
1036 # Defaults to 10 if not specified.
1037 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
1038 # the model to use.
1039 "outputPath": "A String", # Required. The output Google Cloud Storage location.
1040 "dataFormat": "A String", # Required. The format of the input data files.
1041 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
1042 # string is formatted the same way as `model_version`, with the addition
1043 # of the version information:
1044 #
1045 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
1046 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
1047 },
1048 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
1049 "jobId": "A String", # Required. The user-specified id of the job.
1050 "state": "A String", # Output only. The detailed state of a job.
1051 "startTime": "A String", # Output only. When the job processing was started.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]1052 "endTime": "A String", # Output only. When the job processing was completed.
1053 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
1054 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
Sai Cheemalapatic30d2b52017-03-13 12:12:03 -0400[diff] [blame]1055 "nodeHours": 3.14, # Node hours used by the batch prediction job.
Sai Cheemalapatiea3a5e12016-10-12 14:05:53 -0700[diff] [blame]1056 "predictionCount": "A String", # The number of generated predictions.
1057 "errorCount": "A String", # The number of data instances which resulted in errors.
1058 },
1059 "createTime": "A String", # Output only. When the job was created.
1060 },
1061 ],
1062 }</pre>
1063</div>
1064
1065<div class="method">
1066 <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
1067 <pre>Retrieves the next page of results.
1068
1069Args:
1070 previous_request: The request for the previous page. (required)
1071 previous_response: The response from the request for the previous page. (required)
1072
1073Returns:
1074 A request object that you can call 'execute()' on to request the next
1075 page. Returns None if there are no more items in the collection.
1076 </pre>
1077</div>
1078
1079</body></html>