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75<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning</a> . <a href="ml_v1beta1.projects.html">projects</a> . <a href="ml_v1beta1.projects.jobs.html">jobs</a></h1>
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.
139 "trainingOutput": { # Represents results of a training job. # The current training job result.
140 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
141 "trials": [ # Results for individual Hyperparameter trials.
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 ],
162 },
163 "startTime": "A String", # Output only. When the job processing was started.
164 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
165 "jobId": "A String", # Required. The user-specified id of the job.
166 "state": "A String", # Output only. The detailed state of a job.
167 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
168 "modelName": "A String", # Use this field if you want to use the default version for the specified
169 # model. The string must use the following format:
170 #
171 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
172 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
173 # May contain wildcards.
174 "A String",
175 ],
176 "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
177 # Defaults to 10.
178 "outputPath": "A String", # Required. The output Google Cloud Storage location.
179 "dataFormat": "A String", # Required. The format of the input data files.
180 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
181 # string is formatted the same way as `model_version`, with the addition
182 # of the version information:
183 #
184 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
185 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
186 },
187 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
188 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
189 # job's worker nodes.
190 #
191 # The supported values are the same as those described in the entry for
192 # `masterType`.
193 #
194 # This value must be present when `scaleTier` is set to `CUSTOM` and
195 # `workerCount` is greater than zero.
196 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
197 # and parameter servers.
198 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
199 # job's master worker.
200 #
201 # The following types are supported:
202 #
203 # <dl>
204 # <dt>standard</dt>
205 # <dd>
206 # A basic machine configuration suitable for training simple models with
207 # small to moderate datasets.
208 # </dd>
209 # <dt>large_model</dt>
210 # <dd>
211 # A machine with a lot of memory, specially suited for parameter servers
212 # when your model is large (having many hidden layers or layers with very
213 # large numbers of nodes).
214 # </dd>
215 # <dt>complex_model_s</dt>
216 # <dd>
217 # A machine suitable for the master and workers of the cluster when your
218 # model requires more computation than the standard machine can handle
219 # satisfactorily.
220 # </dd>
221 # <dt>complex_model_m</dt>
222 # <dd>
223 # A machine with roughly twice the number of cores and roughly double the
224 # memory of <code suppresswarning="true">complex_model_s</code>.
225 # </dd>
226 # <dt>complex_model_l</dt>
227 # <dd>
228 # A machine with roughly twice the number of cores and roughly double the
229 # memory of <code suppresswarning="true">complex_model_m</code>.
230 # </dd>
231 # </dl>
232 #
233 # You must set this value when `scaleTier` is set to `CUSTOM`.
234 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
235 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
236 # the specified hyperparameters.
237 #
238 # Defaults to one.
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`.
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.
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".
256 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
257 "A String",
258 ],
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.
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`.
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.
286 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
287 # the training program and any additional dependencies.
288 "A String",
289 ],
290 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
291 # replica in the cluster will be of the type specified in `worker_type`.
292 #
293 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
294 # set this value, you must also set `worker_type`.
295 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
296 # job's parameter server.
297 #
298 # The supported values are the same as those described in the entry for
299 # `master_type`.
300 #
301 # This value must be present when `scaleTier` is set to `CUSTOM` and
302 # `parameter_server_count` is greater than zero.
303 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
304 # job. Each replica in the cluster will be of the type specified in
305 # `parameter_server_type`.
306 #
307 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
308 # set this value, you must also set `parameter_server_type`.
309 },
310 "endTime": "A String", # Output only. When the job processing was completed.
311 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
312 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
313 "predictionCount": "A String", # The number of generated predictions.
314 "errorCount": "A String", # The number of data instances which resulted in errors.
315 },
316 "createTime": "A String", # Output only. When the job was created.
317 }
318
319 x__xgafv: string, V1 error format.
320 Allowed values
321 1 - v1 error format
322 2 - v2 error format
323
324Returns:
325 An object of the form:
326
327 { # Represents a training or prediction job.
328 "trainingOutput": { # Represents results of a training job. # The current training job result.
329 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
330 "trials": [ # Results for individual Hyperparameter trials.
331 { # Represents the result of a single hyperparameter tuning trial from a
332 # training job. The TrainingOutput object that is returned on successful
333 # completion of a training job with hyperparameter tuning includes a list
334 # of HyperparameterOutput objects, one for each successful trial.
335 "hyperparameters": { # The hyperparameters given to this trial.
336 "a_key": "A String",
337 },
338 "trialId": "A String", # The trial id for these results.
339 "allMetrics": [ # All recorded object metrics for this trial.
340 { # An observed value of a metric.
341 "trainingStep": "A String", # The global training step for this metric.
342 "objectiveValue": 3.14, # The objective value at this training step.
343 },
344 ],
345 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
346 "trainingStep": "A String", # The global training step for this metric.
347 "objectiveValue": 3.14, # The objective value at this training step.
348 },
349 },
350 ],
351 },
352 "startTime": "A String", # Output only. When the job processing was started.
353 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
354 "jobId": "A String", # Required. The user-specified id of the job.
355 "state": "A String", # Output only. The detailed state of a job.
356 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
357 "modelName": "A String", # Use this field if you want to use the default version for the specified
358 # model. The string must use the following format:
359 #
360 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
361 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
362 # May contain wildcards.
363 "A String",
364 ],
365 "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
366 # Defaults to 10.
367 "outputPath": "A String", # Required. The output Google Cloud Storage location.
368 "dataFormat": "A String", # Required. The format of the input data files.
369 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
370 # string is formatted the same way as `model_version`, with the addition
371 # of the version information:
372 #
373 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
374 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
375 },
376 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
377 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
378 # job's worker nodes.
379 #
380 # The supported values are the same as those described in the entry for
381 # `masterType`.
382 #
383 # This value must be present when `scaleTier` is set to `CUSTOM` and
384 # `workerCount` is greater than zero.
385 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
386 # and parameter servers.
387 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
388 # job's master worker.
389 #
390 # The following types are supported:
391 #
392 # <dl>
393 # <dt>standard</dt>
394 # <dd>
395 # A basic machine configuration suitable for training simple models with
396 # small to moderate datasets.
397 # </dd>
398 # <dt>large_model</dt>
399 # <dd>
400 # A machine with a lot of memory, specially suited for parameter servers
401 # when your model is large (having many hidden layers or layers with very
402 # large numbers of nodes).
403 # </dd>
404 # <dt>complex_model_s</dt>
405 # <dd>
406 # A machine suitable for the master and workers of the cluster when your
407 # model requires more computation than the standard machine can handle
408 # satisfactorily.
409 # </dd>
410 # <dt>complex_model_m</dt>
411 # <dd>
412 # A machine with roughly twice the number of cores and roughly double the
413 # memory of <code suppresswarning="true">complex_model_s</code>.
414 # </dd>
415 # <dt>complex_model_l</dt>
416 # <dd>
417 # A machine with roughly twice the number of cores and roughly double the
418 # memory of <code suppresswarning="true">complex_model_m</code>.
419 # </dd>
420 # </dl>
421 #
422 # You must set this value when `scaleTier` is set to `CUSTOM`.
423 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
424 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
425 # the specified hyperparameters.
426 #
427 # Defaults to one.
428 "params": [ # Required. The set of parameters to tune.
429 { # Represents a single hyperparameter to optimize.
430 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
431 # should be unset if type is `CATEGORICAL`. This value should be integers if
432 # type is `INTEGER`.
433 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
434 # should be unset if type is `CATEGORICAL`. This value should be integers if
435 # type is INTEGER.
436 "discreteValues": [ # Required if type is `DISCRETE`.
437 # A list of feasible points.
438 # The list should be in strictly increasing order. For instance, this
439 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
440 # should not contain more than 1,000 values.
441 3.14,
442 ],
443 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
444 # a HyperparameterSpec message. E.g., "learning_rate".
445 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
446 "A String",
447 ],
448 "type": "A String", # Required. The type of the parameter.
449 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
450 # Leave unset for categorical parameters.
451 # Some kind of scaling is strongly recommended for real or integral
452 # parameters (e.g., `UNIT_LINEAR_SCALE`).
453 },
454 ],
455 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
456 # You can reduce the time it takes to perform hyperparameter tuning by adding
457 # trials in parallel. However, each trail only benefits from the information
458 # gained in completed trials. That means that a trial does not get access to
459 # the results of trials running at the same time, which could reduce the
460 # quality of the overall optimization.
461 #
462 # Each trial will use the same scale tier and machine types.
463 #
464 # Defaults to one.
465 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
466 # `MAXIMIZE` and `MINIMIZE`.
467 #
468 # Defaults to `MAXIMIZE`.
469 },
470 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
471 "args": [ # Optional. Command line arguments to pass to the program.
472 "A String",
473 ],
474 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
475 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
476 # the training program and any additional dependencies.
477 "A String",
478 ],
479 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
480 # replica in the cluster will be of the type specified in `worker_type`.
481 #
482 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
483 # set this value, you must also set `worker_type`.
484 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
485 # job's parameter server.
486 #
487 # The supported values are the same as those described in the entry for
488 # `master_type`.
489 #
490 # This value must be present when `scaleTier` is set to `CUSTOM` and
491 # `parameter_server_count` is greater than zero.
492 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
493 # job. Each replica in the cluster will be of the type specified in
494 # `parameter_server_type`.
495 #
496 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
497 # set this value, you must also set `parameter_server_type`.
498 },
499 "endTime": "A String", # Output only. When the job processing was completed.
500 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
501 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
502 "predictionCount": "A String", # The number of generated predictions.
503 "errorCount": "A String", # The number of data instances which resulted in errors.
504 },
505 "createTime": "A String", # Output only. When the job was created.
506 }</pre>
507</div>
508
509<div class="method">
510 <code class="details" id="get">get(name=None, x__xgafv=None)</code>
511 <pre>Describes a job.
512
513Args:
514 name: string, Required. The name of the job to get the description of.
515
516Authorization: requires `Viewer` role on the parent project. (required)
517 x__xgafv: string, V1 error format.
518 Allowed values
519 1 - v1 error format
520 2 - v2 error format
521
522Returns:
523 An object of the form:
524
525 { # Represents a training or prediction job.
526 "trainingOutput": { # Represents results of a training job. # The current training job result.
527 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
528 "trials": [ # Results for individual Hyperparameter trials.
529 { # Represents the result of a single hyperparameter tuning trial from a
530 # training job. The TrainingOutput object that is returned on successful
531 # completion of a training job with hyperparameter tuning includes a list
532 # of HyperparameterOutput objects, one for each successful trial.
533 "hyperparameters": { # The hyperparameters given to this trial.
534 "a_key": "A String",
535 },
536 "trialId": "A String", # The trial id for these results.
537 "allMetrics": [ # All recorded object metrics for this trial.
538 { # An observed value of a metric.
539 "trainingStep": "A String", # The global training step for this metric.
540 "objectiveValue": 3.14, # The objective value at this training step.
541 },
542 ],
543 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
544 "trainingStep": "A String", # The global training step for this metric.
545 "objectiveValue": 3.14, # The objective value at this training step.
546 },
547 },
548 ],
549 },
550 "startTime": "A String", # Output only. When the job processing was started.
551 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
552 "jobId": "A String", # Required. The user-specified id of the job.
553 "state": "A String", # Output only. The detailed state of a job.
554 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
555 "modelName": "A String", # Use this field if you want to use the default version for the specified
556 # model. The string must use the following format:
557 #
558 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
559 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
560 # May contain wildcards.
561 "A String",
562 ],
563 "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
564 # Defaults to 10.
565 "outputPath": "A String", # Required. The output Google Cloud Storage location.
566 "dataFormat": "A String", # Required. The format of the input data files.
567 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
568 # string is formatted the same way as `model_version`, with the addition
569 # of the version information:
570 #
571 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
572 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
573 },
574 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
575 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
576 # job's worker nodes.
577 #
578 # The supported values are the same as those described in the entry for
579 # `masterType`.
580 #
581 # This value must be present when `scaleTier` is set to `CUSTOM` and
582 # `workerCount` is greater than zero.
583 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
584 # and parameter servers.
585 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
586 # job's master worker.
587 #
588 # The following types are supported:
589 #
590 # <dl>
591 # <dt>standard</dt>
592 # <dd>
593 # A basic machine configuration suitable for training simple models with
594 # small to moderate datasets.
595 # </dd>
596 # <dt>large_model</dt>
597 # <dd>
598 # A machine with a lot of memory, specially suited for parameter servers
599 # when your model is large (having many hidden layers or layers with very
600 # large numbers of nodes).
601 # </dd>
602 # <dt>complex_model_s</dt>
603 # <dd>
604 # A machine suitable for the master and workers of the cluster when your
605 # model requires more computation than the standard machine can handle
606 # satisfactorily.
607 # </dd>
608 # <dt>complex_model_m</dt>
609 # <dd>
610 # A machine with roughly twice the number of cores and roughly double the
611 # memory of <code suppresswarning="true">complex_model_s</code>.
612 # </dd>
613 # <dt>complex_model_l</dt>
614 # <dd>
615 # A machine with roughly twice the number of cores and roughly double the
616 # memory of <code suppresswarning="true">complex_model_m</code>.
617 # </dd>
618 # </dl>
619 #
620 # You must set this value when `scaleTier` is set to `CUSTOM`.
621 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
622 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
623 # the specified hyperparameters.
624 #
625 # Defaults to one.
626 "params": [ # Required. The set of parameters to tune.
627 { # Represents a single hyperparameter to optimize.
628 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
629 # should be unset if type is `CATEGORICAL`. This value should be integers if
630 # type is `INTEGER`.
631 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
632 # should be unset if type is `CATEGORICAL`. This value should be integers if
633 # type is INTEGER.
634 "discreteValues": [ # Required if type is `DISCRETE`.
635 # A list of feasible points.
636 # The list should be in strictly increasing order. For instance, this
637 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
638 # should not contain more than 1,000 values.
639 3.14,
640 ],
641 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
642 # a HyperparameterSpec message. E.g., "learning_rate".
643 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
644 "A String",
645 ],
646 "type": "A String", # Required. The type of the parameter.
647 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
648 # Leave unset for categorical parameters.
649 # Some kind of scaling is strongly recommended for real or integral
650 # parameters (e.g., `UNIT_LINEAR_SCALE`).
651 },
652 ],
653 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
654 # You can reduce the time it takes to perform hyperparameter tuning by adding
655 # trials in parallel. However, each trail only benefits from the information
656 # gained in completed trials. That means that a trial does not get access to
657 # the results of trials running at the same time, which could reduce the
658 # quality of the overall optimization.
659 #
660 # Each trial will use the same scale tier and machine types.
661 #
662 # Defaults to one.
663 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
664 # `MAXIMIZE` and `MINIMIZE`.
665 #
666 # Defaults to `MAXIMIZE`.
667 },
668 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
669 "args": [ # Optional. Command line arguments to pass to the program.
670 "A String",
671 ],
672 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
673 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
674 # the training program and any additional dependencies.
675 "A String",
676 ],
677 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
678 # replica in the cluster will be of the type specified in `worker_type`.
679 #
680 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
681 # set this value, you must also set `worker_type`.
682 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
683 # job's parameter server.
684 #
685 # The supported values are the same as those described in the entry for
686 # `master_type`.
687 #
688 # This value must be present when `scaleTier` is set to `CUSTOM` and
689 # `parameter_server_count` is greater than zero.
690 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
691 # job. Each replica in the cluster will be of the type specified in
692 # `parameter_server_type`.
693 #
694 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
695 # set this value, you must also set `parameter_server_type`.
696 },
697 "endTime": "A String", # Output only. When the job processing was completed.
698 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
699 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
700 "predictionCount": "A String", # The number of generated predictions.
701 "errorCount": "A String", # The number of data instances which resulted in errors.
702 },
703 "createTime": "A String", # Output only. When the job was created.
704 }</pre>
705</div>
706
707<div class="method">
708 <code class="details" id="list">list(parent=None, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code>
709 <pre>Lists the jobs in the project.
710
711Args:
712 parent: string, Required. The name of the project for which to list jobs.
713
714Authorization: requires `Viewer` role on the specified project. (required)
715 pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there
716are more remaining results than this number, the response message will
717contain a valid value in the `next_page_token` field.
718
719The default value is 20, and the maximum page size is 100.
720 filter: string, Optional. Specifies the subset of jobs to retrieve.
721 pageToken: string, Optional. A page token to request the next page of results.
722
723You get the token from the `next_page_token` field of the response from
724the previous call.
725 x__xgafv: string, V1 error format.
726 Allowed values
727 1 - v1 error format
728 2 - v2 error format
729
730Returns:
731 An object of the form:
732
733 { # Response message for the ListJobs method.
734 "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
735 # subsequent call.
736 "jobs": [ # The list of jobs.
737 { # Represents a training or prediction job.
738 "trainingOutput": { # Represents results of a training job. # The current training job result.
739 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
740 "trials": [ # Results for individual Hyperparameter trials.
741 { # Represents the result of a single hyperparameter tuning trial from a
742 # training job. The TrainingOutput object that is returned on successful
743 # completion of a training job with hyperparameter tuning includes a list
744 # of HyperparameterOutput objects, one for each successful trial.
745 "hyperparameters": { # The hyperparameters given to this trial.
746 "a_key": "A String",
747 },
748 "trialId": "A String", # The trial id for these results.
749 "allMetrics": [ # All recorded object metrics for this trial.
750 { # An observed value of a metric.
751 "trainingStep": "A String", # The global training step for this metric.
752 "objectiveValue": 3.14, # The objective value at this training step.
753 },
754 ],
755 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
756 "trainingStep": "A String", # The global training step for this metric.
757 "objectiveValue": 3.14, # The objective value at this training step.
758 },
759 },
760 ],
761 },
762 "startTime": "A String", # Output only. When the job processing was started.
763 "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
764 "jobId": "A String", # Required. The user-specified id of the job.
765 "state": "A String", # Output only. The detailed state of a job.
766 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
767 "modelName": "A String", # Use this field if you want to use the default version for the specified
768 # model. The string must use the following format:
769 #
770 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
771 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
772 # May contain wildcards.
773 "A String",
774 ],
775 "maxWorkerCount": "A String", # Optional. The maximum amount of workers to be used for parallel processing.
776 # Defaults to 10.
777 "outputPath": "A String", # Required. The output Google Cloud Storage location.
778 "dataFormat": "A String", # Required. The format of the input data files.
779 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The
780 # string is formatted the same way as `model_version`, with the addition
781 # of the version information:
782 #
783 # `"project/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
784 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
785 },
786 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
787 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
788 # job's worker nodes.
789 #
790 # The supported values are the same as those described in the entry for
791 # `masterType`.
792 #
793 # This value must be present when `scaleTier` is set to `CUSTOM` and
794 # `workerCount` is greater than zero.
795 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
796 # and parameter servers.
797 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
798 # job's master worker.
799 #
800 # The following types are supported:
801 #
802 # <dl>
803 # <dt>standard</dt>
804 # <dd>
805 # A basic machine configuration suitable for training simple models with
806 # small to moderate datasets.
807 # </dd>
808 # <dt>large_model</dt>
809 # <dd>
810 # A machine with a lot of memory, specially suited for parameter servers
811 # when your model is large (having many hidden layers or layers with very
812 # large numbers of nodes).
813 # </dd>
814 # <dt>complex_model_s</dt>
815 # <dd>
816 # A machine suitable for the master and workers of the cluster when your
817 # model requires more computation than the standard machine can handle
818 # satisfactorily.
819 # </dd>
820 # <dt>complex_model_m</dt>
821 # <dd>
822 # A machine with roughly twice the number of cores and roughly double the
823 # memory of <code suppresswarning="true">complex_model_s</code>.
824 # </dd>
825 # <dt>complex_model_l</dt>
826 # <dd>
827 # A machine with roughly twice the number of cores and roughly double the
828 # memory of <code suppresswarning="true">complex_model_m</code>.
829 # </dd>
830 # </dl>
831 #
832 # You must set this value when `scaleTier` is set to `CUSTOM`.
833 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
834 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize
835 # the specified hyperparameters.
836 #
837 # Defaults to one.
838 "params": [ # Required. The set of parameters to tune.
839 { # Represents a single hyperparameter to optimize.
840 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
841 # should be unset if type is `CATEGORICAL`. This value should be integers if
842 # type is `INTEGER`.
843 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
844 # should be unset if type is `CATEGORICAL`. This value should be integers if
845 # type is INTEGER.
846 "discreteValues": [ # Required if type is `DISCRETE`.
847 # A list of feasible points.
848 # The list should be in strictly increasing order. For instance, this
849 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
850 # should not contain more than 1,000 values.
851 3.14,
852 ],
853 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
854 # a HyperparameterSpec message. E.g., "learning_rate".
855 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
856 "A String",
857 ],
858 "type": "A String", # Required. The type of the parameter.
859 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
860 # Leave unset for categorical parameters.
861 # Some kind of scaling is strongly recommended for real or integral
862 # parameters (e.g., `UNIT_LINEAR_SCALE`).
863 },
864 ],
865 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
866 # You can reduce the time it takes to perform hyperparameter tuning by adding
867 # trials in parallel. However, each trail only benefits from the information
868 # gained in completed trials. That means that a trial does not get access to
869 # the results of trials running at the same time, which could reduce the
870 # quality of the overall optimization.
871 #
872 # Each trial will use the same scale tier and machine types.
873 #
874 # Defaults to one.
875 "goal": "A String", # Required. The type of goal to use for tuning. Available types are
876 # `MAXIMIZE` and `MINIMIZE`.
877 #
878 # Defaults to `MAXIMIZE`.
879 },
880 "region": "A String", # Required. The Google Compute Engine region to run the training job in.
881 "args": [ # Optional. Command line arguments to pass to the program.
882 "A String",
883 ],
884 "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
885 "packageUris": [ # Required. The Google Cloud Storage location of the packages with
886 # the training program and any additional dependencies.
887 "A String",
888 ],
889 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
890 # replica in the cluster will be of the type specified in `worker_type`.
891 #
892 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
893 # set this value, you must also set `worker_type`.
894 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
895 # job's parameter server.
896 #
897 # The supported values are the same as those described in the entry for
898 # `master_type`.
899 #
900 # This value must be present when `scaleTier` is set to `CUSTOM` and
901 # `parameter_server_count` is greater than zero.
902 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
903 # job. Each replica in the cluster will be of the type specified in
904 # `parameter_server_type`.
905 #
906 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
907 # set this value, you must also set `parameter_server_type`.
908 },
909 "endTime": "A String", # Output only. When the job processing was completed.
910 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
911 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
912 "predictionCount": "A String", # The number of generated predictions.
913 "errorCount": "A String", # The number of data instances which resulted in errors.
914 },
915 "createTime": "A String", # Output only. When the job was created.
916 },
917 ],
918 }</pre>
919</div>
920
921<div class="method">
922 <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
923 <pre>Retrieves the next page of results.
924
925Args:
926 previous_request: The request for the previous page. (required)
927 previous_response: The response from the request for the previous page. (required)
928
929Returns:
930 A request object that you can call 'execute()' on to request the next
931 page. Returns None if there are no more items in the collection.
932 </pre>
933</div>
934
935</body></html>