1<html><body> 2<style> 3 4body, h1, h2, h3, div, span, p, pre, a { 5 margin: 0; 6 padding: 0; 7 border: 0; 8 font-weight: inherit; 9 font-style: inherit; 10 font-size: 100%; 11 font-family: inherit; 12 vertical-align: baseline; 13} 14 15body { 16 font-size: 13px; 17 padding: 1em; 18} 19 20h1 { 21 font-size: 26px; 22 margin-bottom: 1em; 23} 24 25h2 { 26 font-size: 24px; 27 margin-bottom: 1em; 28} 29 30h3 { 31 font-size: 20px; 32 margin-bottom: 1em; 33 margin-top: 1em; 34} 35 36pre, code { 37 line-height: 1.5; 38 font-family: Monaco, 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Lucida Console', monospace; 39} 40 41pre { 42 margin-top: 0.5em; 43} 44 45h1, h2, h3, p { 46 font-family: Arial, sans serif; 47} 48 49h1, h2, h3 { 50 border-bottom: solid #CCC 1px; 51} 52 53.toc_element { 54 margin-top: 0.5em; 55} 56 57.firstline { 58 margin-left: 2 em; 59} 60 61.method { 62 margin-top: 1em; 63 border: solid 1px #CCC; 64 padding: 1em; 65 background: #EEE; 66} 67 68.details { 69 font-weight: bold; 70 font-size: 14px; 71} 72 73</style> 74 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> 76<h2>Instance Methods</h2> 77<p class="toc_element"> 78 <code><a href="#cancel">cancel(name, 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, 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, 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, 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, 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, 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. Output only. # The current training job result. 140 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. 141 # Only set for hyperparameter tuning jobs. 142 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job. 143 "trials": [ # Results for individual Hyperparameter trials. 144 # Only set for hyperparameter tuning jobs. 145 { # Represents the result of a single hyperparameter tuning trial from a 146 # training job. The TrainingOutput object that is returned on successful 147 # completion of a training job with hyperparameter tuning includes a list 148 # of HyperparameterOutput objects, one for each successful trial. 149 "hyperparameters": { # The hyperparameters given to this trial. 150 "a_key": "A String", 151 }, 152 "trialId": "A String", # The trial id for these results. 153 "allMetrics": [ # All recorded object metrics for this trial. 154 { # An observed value of a metric. 155 "trainingStep": "A String", # The global training step for this metric. 156 "objectiveValue": 3.14, # The objective value at this training step. 157 }, 158 ], 159 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial. 160 "trainingStep": "A String", # The global training step for this metric. 161 "objectiveValue": 3.14, # The objective value at this training step. 162 }, 163 }, 164 ], 165 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. 166 }, 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. 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. 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> 213 # <dt>standard_gpu</dt> 214 # <dd> 215 # A machine equivalent to <code suppresswarning="true">standard</code> that 216 # also includes a 217 # <a href="/ml-engine/docs/how-tos/using-gpus"> 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">complex_model_m</code> that also includes 224 # four GPUs. 225 # </dd> 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. 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. 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 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories. 245 "A String", 246 ], 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 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field 257 # should be unset if type is `CATEGORICAL`. This value should be integers if 258 # type is INTEGER. 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 "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. 290 "packageUris": [ # Required. The Google Cloud Storage location of the packages with 291 # the training program and any additional dependencies. 292 # The maximum number of package URIs is 100. 293 "A String", 294 ], 295 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each 296 # replica in the cluster will be of the type specified in `worker_type`. 297 # 298 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you 299 # set this value, you must also set `worker_type`. 300 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 301 # job's parameter server. 302 # 303 # The supported values are the same as those described in the entry for 304 # `master_type`. 305 # 306 # This value must be present when `scaleTier` is set to `CUSTOM` and 307 # `parameter_server_count` is greater than zero. 308 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training 309 # job. Each replica in the cluster will be of the type specified in 310 # `parameter_server_type`. 311 # 312 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you 313 # set this value, you must also set `parameter_server_type`. 314 }, 315 "startTime": "A String", # Output only. When the job processing was started. 316 "errorMessage": "A String", # Output only. The details of a failure or a cancellation. 317 "jobId": "A String", # Required. The user-specified id of the job. 318 "state": "A String", # Output only. The detailed state of a job. 319 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job. 320 "modelName": "A String", # Use this field if you want to use the default version for the specified 321 # model. The string must use the following format: 322 # 323 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"` 324 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch 325 # prediction. If not set, Google Cloud ML will pick the runtime version used 326 # during the CreateVersion request for this model version, or choose the 327 # latest stable version when model version information is not available 328 # such as when the model is specified by uri. 329 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files. 330 # May contain wildcards. 331 "A String", 332 ], 333 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. 334 # Defaults to 10 if not specified. 335 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for 336 # the model to use. 337 "outputPath": "A String", # Required. The output Google Cloud Storage location. 338 "dataFormat": "A String", # Required. The format of the input data files. 339 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The 340 # string is formatted the same way as `model_version`, with the addition 341 # of the version information: 342 # 343 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"` 344 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. 345 }, 346 "endTime": "A String", # Output only. When the job processing was completed. 347 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result. 348 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time. 349 "nodeHours": 3.14, # Node hours used by the batch prediction job. 350 "predictionCount": "A String", # The number of generated predictions. 351 "errorCount": "A String", # The number of data instances which resulted in errors. 352 }, 353 "createTime": "A String", # Output only. When the job was created. 354 } 355 356 x__xgafv: string, V1 error format. 357 Allowed values 358 1 - v1 error format 359 2 - v2 error format 360 361Returns: 362 An object of the form: 363 364 { # Represents a training or prediction job. 365 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result. 366 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. 367 # Only set for hyperparameter tuning jobs. 368 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job. 369 "trials": [ # Results for individual Hyperparameter trials. 370 # Only set for hyperparameter tuning jobs. 371 { # Represents the result of a single hyperparameter tuning trial from a 372 # training job. The TrainingOutput object that is returned on successful 373 # completion of a training job with hyperparameter tuning includes a list 374 # of HyperparameterOutput objects, one for each successful trial. 375 "hyperparameters": { # The hyperparameters given to this trial. 376 "a_key": "A String", 377 }, 378 "trialId": "A String", # The trial id for these results. 379 "allMetrics": [ # All recorded object metrics for this trial. 380 { # An observed value of a metric. 381 "trainingStep": "A String", # The global training step for this metric. 382 "objectiveValue": 3.14, # The objective value at this training step. 383 }, 384 ], 385 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial. 386 "trainingStep": "A String", # The global training step for this metric. 387 "objectiveValue": 3.14, # The objective value at this training step. 388 }, 389 }, 390 ], 391 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. 392 }, 393 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job. 394 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 395 # job's worker nodes. 396 # 397 # The supported values are the same as those described in the entry for 398 # `masterType`. 399 # 400 # This value must be present when `scaleTier` is set to `CUSTOM` and 401 # `workerCount` is greater than zero. 402 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not 403 # set, Google Cloud ML will choose the latest stable version. 404 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers 405 # and parameter servers. 406 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training 407 # job's master worker. 408 # 409 # The following types are supported: 410 # 411 # <dl> 412 # <dt>standard</dt> 413 # <dd> 414 # A basic machine configuration suitable for training simple models with 415 # small to moderate datasets. 416 # </dd> 417 # <dt>large_model</dt> 418 # <dd> 419 # A machine with a lot of memory, specially suited for parameter servers 420 # when your model is large (having many hidden layers or layers with very 421 # large numbers of nodes). 422 # </dd> 423 # <dt>complex_model_s</dt> 424 # <dd> 425 # A machine suitable for the master and workers of the cluster when your 426 # model requires more computation than the standard machine can handle 427 # satisfactorily. 428 # </dd> 429 # <dt>complex_model_m</dt> 430 # <dd> 431 # A machine with roughly twice the number of cores and roughly double the 432 # memory of <code suppresswarning="true">complex_model_s</code>. 433 # </dd> 434 # <dt>complex_model_l</dt> 435 # <dd> 436 # A machine with roughly twice the number of cores and roughly double the 437 # memory of <code suppresswarning="true">complex_model_m</code>. 438 # </dd> 439 # <dt>standard_gpu</dt> 440 # <dd> 441 # A machine equivalent to <code suppresswarning="true">standard</code> that 442 # also includes a 443 # <a href="/ml-engine/docs/how-tos/using-gpus"> 444 # GPU that you can use in your trainer</a>. 445 # </dd> 446 # <dt>complex_model_m_gpu</dt> 447 # <dd> 448 # A machine equivalent to 449 # <code suppresswarning="true">complex_model_m</code> that also includes 450 # four GPUs. 451 # </dd> 452 # </dl> 453 # 454 # You must set this value when `scaleTier` is set to `CUSTOM`. 455 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune. 456 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize 457 # the specified hyperparameters. 458 # 459 # Defaults to one. 460 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For 461 # current versions of Tensorflow, this tag name should exactly match what is 462 # shown in Tensorboard, including all scopes. For versions of Tensorflow 463 # prior to 0.12, this should be only the tag passed to tf.Summary. 464 # By default, "training/hptuning/metric" will be used. 465 "params": [ # Required. The set of parameters to tune. 466 { # Represents a single hyperparameter to optimize. 467 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field 468 # should be unset if type is `CATEGORICAL`. This value should be integers if 469 # type is `INTEGER`. 470 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories. 471 "A String", 472 ], 473 "discreteValues": [ # Required if type is `DISCRETE`. 474 # A list of feasible points. 475 # The list should be in strictly increasing order. For instance, this 476 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list 477 # should not contain more than 1,000 values. 478 3.14, 479 ], 480 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in 481 # a HyperparameterSpec message. E.g., "learning_rate". 482 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field 483 # should be unset if type is `CATEGORICAL`. This value should be integers if 484 # type is INTEGER. 485 "type": "A String", # Required. The type of the parameter. 486 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. 487 # Leave unset for categorical parameters. 488 # Some kind of scaling is strongly recommended for real or integral 489 # parameters (e.g., `UNIT_LINEAR_SCALE`). 490 }, 491 ], 492 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. 493 # You can reduce the time it takes to perform hyperparameter tuning by adding 494 # trials in parallel. However, each trail only benefits from the information 495 # gained in completed trials. That means that a trial does not get access to 496 # the results of trials running at the same time, which could reduce the 497 # quality of the overall optimization. 498 # 499 # Each trial will use the same scale tier and machine types. 500 # 501 # Defaults to one. 502 "goal": "A String", # Required. The type of goal to use for tuning. Available types are 503 # `MAXIMIZE` and `MINIMIZE`. 504 # 505 # Defaults to `MAXIMIZE`. 506 }, 507 "region": "A String", # Required. The Google Compute Engine region to run the training job in. 508 "args": [ # Optional. Command line arguments to pass to the program. 509 "A String", 510 ], 511 "pythonModule": "A String", # Required. The Python module name to run after installing the packages. 512 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs 513 # and other data needed for training. This path is passed to your TensorFlow 514 # program as the 'job_dir' command-line argument. The benefit of specifying 515 # this field is that Cloud ML validates the path for use in training. 516 "packageUris": [ # Required. The Google Cloud Storage location of the packages with 517 # the training program and any additional dependencies. 518 # The maximum number of package URIs is 100. 519 "A String", 520 ], 521 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each 522 # replica in the cluster will be of the type specified in `worker_type`. 523 # 524 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you 525 # set this value, you must also set `worker_type`. 526 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 527 # job's parameter server. 528 # 529 # The supported values are the same as those described in the entry for 530 # `master_type`. 531 # 532 # This value must be present when `scaleTier` is set to `CUSTOM` and 533 # `parameter_server_count` is greater than zero. 534 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training 535 # job. Each replica in the cluster will be of the type specified in 536 # `parameter_server_type`. 537 # 538 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you 539 # set this value, you must also set `parameter_server_type`. 540 }, 541 "startTime": "A String", # Output only. When the job processing was started. 542 "errorMessage": "A String", # Output only. The details of a failure or a cancellation. 543 "jobId": "A String", # Required. The user-specified id of the job. 544 "state": "A String", # Output only. The detailed state of a job. 545 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job. 546 "modelName": "A String", # Use this field if you want to use the default version for the specified 547 # model. The string must use the following format: 548 # 549 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"` 550 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch 551 # prediction. If not set, Google Cloud ML will pick the runtime version used 552 # during the CreateVersion request for this model version, or choose the 553 # latest stable version when model version information is not available 554 # such as when the model is specified by uri. 555 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files. 556 # May contain wildcards. 557 "A String", 558 ], 559 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. 560 # Defaults to 10 if not specified. 561 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for 562 # the model to use. 563 "outputPath": "A String", # Required. The output Google Cloud Storage location. 564 "dataFormat": "A String", # Required. The format of the input data files. 565 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The 566 # string is formatted the same way as `model_version`, with the addition 567 # of the version information: 568 # 569 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"` 570 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. 571 }, 572 "endTime": "A String", # Output only. When the job processing was completed. 573 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result. 574 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time. 575 "nodeHours": 3.14, # Node hours used by the batch prediction job. 576 "predictionCount": "A String", # The number of generated predictions. 577 "errorCount": "A String", # The number of data instances which resulted in errors. 578 }, 579 "createTime": "A String", # Output only. When the job was created. 580 }</pre> 581</div> 582 583<div class="method"> 584 <code class="details" id="get">get(name, x__xgafv=None)</code> 585 <pre>Describes a job. 586 587Args: 588 name: string, Required. The name of the job to get the description of. 589 590Authorization: requires `Viewer` role on the parent project. (required) 591 x__xgafv: string, V1 error format. 592 Allowed values 593 1 - v1 error format 594 2 - v2 error format 595 596Returns: 597 An object of the form: 598 599 { # Represents a training or prediction job. 600 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result. 601 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. 602 # Only set for hyperparameter tuning jobs. 603 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job. 604 "trials": [ # Results for individual Hyperparameter trials. 605 # Only set for hyperparameter tuning jobs. 606 { # Represents the result of a single hyperparameter tuning trial from a 607 # training job. The TrainingOutput object that is returned on successful 608 # completion of a training job with hyperparameter tuning includes a list 609 # of HyperparameterOutput objects, one for each successful trial. 610 "hyperparameters": { # The hyperparameters given to this trial. 611 "a_key": "A String", 612 }, 613 "trialId": "A String", # The trial id for these results. 614 "allMetrics": [ # All recorded object metrics for this trial. 615 { # An observed value of a metric. 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 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial. 621 "trainingStep": "A String", # The global training step for this metric. 622 "objectiveValue": 3.14, # The objective value at this training step. 623 }, 624 }, 625 ], 626 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. 627 }, 628 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job. 629 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 630 # job's worker nodes. 631 # 632 # The supported values are the same as those described in the entry for 633 # `masterType`. 634 # 635 # This value must be present when `scaleTier` is set to `CUSTOM` and 636 # `workerCount` is greater than zero. 637 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not 638 # set, Google Cloud ML will choose the latest stable version. 639 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers 640 # and parameter servers. 641 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training 642 # job's master worker. 643 # 644 # The following types are supported: 645 # 646 # <dl> 647 # <dt>standard</dt> 648 # <dd> 649 # A basic machine configuration suitable for training simple models with 650 # small to moderate datasets. 651 # </dd> 652 # <dt>large_model</dt> 653 # <dd> 654 # A machine with a lot of memory, specially suited for parameter servers 655 # when your model is large (having many hidden layers or layers with very 656 # large numbers of nodes). 657 # </dd> 658 # <dt>complex_model_s</dt> 659 # <dd> 660 # A machine suitable for the master and workers of the cluster when your 661 # model requires more computation than the standard machine can handle 662 # satisfactorily. 663 # </dd> 664 # <dt>complex_model_m</dt> 665 # <dd> 666 # A machine with roughly twice the number of cores and roughly double the 667 # memory of <code suppresswarning="true">complex_model_s</code>. 668 # </dd> 669 # <dt>complex_model_l</dt> 670 # <dd> 671 # A machine with roughly twice the number of cores and roughly double the 672 # memory of <code suppresswarning="true">complex_model_m</code>. 673 # </dd> 674 # <dt>standard_gpu</dt> 675 # <dd> 676 # A machine equivalent to <code suppresswarning="true">standard</code> that 677 # also includes a 678 # <a href="/ml-engine/docs/how-tos/using-gpus"> 679 # GPU that you can use in your trainer</a>. 680 # </dd> 681 # <dt>complex_model_m_gpu</dt> 682 # <dd> 683 # A machine equivalent to 684 # <code suppresswarning="true">complex_model_m</code> that also includes 685 # four GPUs. 686 # </dd> 687 # </dl> 688 # 689 # You must set this value when `scaleTier` is set to `CUSTOM`. 690 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune. 691 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize 692 # the specified hyperparameters. 693 # 694 # Defaults to one. 695 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For 696 # current versions of Tensorflow, this tag name should exactly match what is 697 # shown in Tensorboard, including all scopes. For versions of Tensorflow 698 # prior to 0.12, this should be only the tag passed to tf.Summary. 699 # By default, "training/hptuning/metric" will be used. 700 "params": [ # Required. The set of parameters to tune. 701 { # Represents a single hyperparameter to optimize. 702 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field 703 # should be unset if type is `CATEGORICAL`. This value should be integers if 704 # type is `INTEGER`. 705 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories. 706 "A String", 707 ], 708 "discreteValues": [ # Required if type is `DISCRETE`. 709 # A list of feasible points. 710 # The list should be in strictly increasing order. For instance, this 711 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list 712 # should not contain more than 1,000 values. 713 3.14, 714 ], 715 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in 716 # a HyperparameterSpec message. E.g., "learning_rate". 717 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field 718 # should be unset if type is `CATEGORICAL`. This value should be integers if 719 # type is INTEGER. 720 "type": "A String", # Required. The type of the parameter. 721 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. 722 # Leave unset for categorical parameters. 723 # Some kind of scaling is strongly recommended for real or integral 724 # parameters (e.g., `UNIT_LINEAR_SCALE`). 725 }, 726 ], 727 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. 728 # You can reduce the time it takes to perform hyperparameter tuning by adding 729 # trials in parallel. However, each trail only benefits from the information 730 # gained in completed trials. That means that a trial does not get access to 731 # the results of trials running at the same time, which could reduce the 732 # quality of the overall optimization. 733 # 734 # Each trial will use the same scale tier and machine types. 735 # 736 # Defaults to one. 737 "goal": "A String", # Required. The type of goal to use for tuning. Available types are 738 # `MAXIMIZE` and `MINIMIZE`. 739 # 740 # Defaults to `MAXIMIZE`. 741 }, 742 "region": "A String", # Required. The Google Compute Engine region to run the training job in. 743 "args": [ # Optional. Command line arguments to pass to the program. 744 "A String", 745 ], 746 "pythonModule": "A String", # Required. The Python module name to run after installing the packages. 747 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs 748 # and other data needed for training. This path is passed to your TensorFlow 749 # program as the 'job_dir' command-line argument. The benefit of specifying 750 # this field is that Cloud ML validates the path for use in training. 751 "packageUris": [ # Required. The Google Cloud Storage location of the packages with 752 # the training program and any additional dependencies. 753 # The maximum number of package URIs is 100. 754 "A String", 755 ], 756 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each 757 # replica in the cluster will be of the type specified in `worker_type`. 758 # 759 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you 760 # set this value, you must also set `worker_type`. 761 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 762 # job's parameter server. 763 # 764 # The supported values are the same as those described in the entry for 765 # `master_type`. 766 # 767 # This value must be present when `scaleTier` is set to `CUSTOM` and 768 # `parameter_server_count` is greater than zero. 769 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training 770 # job. Each replica in the cluster will be of the type specified in 771 # `parameter_server_type`. 772 # 773 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you 774 # set this value, you must also set `parameter_server_type`. 775 }, 776 "startTime": "A String", # Output only. When the job processing was started. 777 "errorMessage": "A String", # Output only. The details of a failure or a cancellation. 778 "jobId": "A String", # Required. The user-specified id of the job. 779 "state": "A String", # Output only. The detailed state of a job. 780 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job. 781 "modelName": "A String", # Use this field if you want to use the default version for the specified 782 # model. The string must use the following format: 783 # 784 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"` 785 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch 786 # prediction. If not set, Google Cloud ML will pick the runtime version used 787 # during the CreateVersion request for this model version, or choose the 788 # latest stable version when model version information is not available 789 # such as when the model is specified by uri. 790 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files. 791 # May contain wildcards. 792 "A String", 793 ], 794 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. 795 # Defaults to 10 if not specified. 796 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for 797 # the model to use. 798 "outputPath": "A String", # Required. The output Google Cloud Storage location. 799 "dataFormat": "A String", # Required. The format of the input data files. 800 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The 801 # string is formatted the same way as `model_version`, with the addition 802 # of the version information: 803 # 804 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"` 805 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. 806 }, 807 "endTime": "A String", # Output only. When the job processing was completed. 808 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result. 809 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time. 810 "nodeHours": 3.14, # Node hours used by the batch prediction job. 811 "predictionCount": "A String", # The number of generated predictions. 812 "errorCount": "A String", # The number of data instances which resulted in errors. 813 }, 814 "createTime": "A String", # Output only. When the job was created. 815 }</pre> 816</div> 817 818<div class="method"> 819 <code class="details" id="list">list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code> 820 <pre>Lists the jobs in the project. 821 822Args: 823 parent: string, Required. The name of the project for which to list jobs. 824 825Authorization: requires `Viewer` role on the specified project. (required) 826 pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there 827are more remaining results than this number, the response message will 828contain a valid value in the `next_page_token` field. 829 830The default value is 20, and the maximum page size is 100. 831 filter: string, Optional. Specifies the subset of jobs to retrieve. 832 pageToken: string, Optional. A page token to request the next page of results. 833 834You get the token from the `next_page_token` field of the response from 835the previous call. 836 x__xgafv: string, V1 error format. 837 Allowed values 838 1 - v1 error format 839 2 - v2 error format 840 841Returns: 842 An object of the form: 843 844 { # Response message for the ListJobs method. 845 "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a 846 # subsequent call. 847 "jobs": [ # The list of jobs. 848 { # Represents a training or prediction job. 849 "trainingOutput": { # Represents results of a training job. Output only. # The current training job result. 850 "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully. 851 # Only set for hyperparameter tuning jobs. 852 "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job. 853 "trials": [ # Results for individual Hyperparameter trials. 854 # Only set for hyperparameter tuning jobs. 855 { # Represents the result of a single hyperparameter tuning trial from a 856 # training job. The TrainingOutput object that is returned on successful 857 # completion of a training job with hyperparameter tuning includes a list 858 # of HyperparameterOutput objects, one for each successful trial. 859 "hyperparameters": { # The hyperparameters given to this trial. 860 "a_key": "A String", 861 }, 862 "trialId": "A String", # The trial id for these results. 863 "allMetrics": [ # All recorded object metrics for this trial. 864 { # An observed value of a metric. 865 "trainingStep": "A String", # The global training step for this metric. 866 "objectiveValue": 3.14, # The objective value at this training step. 867 }, 868 ], 869 "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial. 870 "trainingStep": "A String", # The global training step for this metric. 871 "objectiveValue": 3.14, # The objective value at this training step. 872 }, 873 }, 874 ], 875 "consumedMLUnits": 3.14, # The amount of ML units consumed by the job. 876 }, 877 "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job. 878 "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 879 # job's worker nodes. 880 # 881 # The supported values are the same as those described in the entry for 882 # `masterType`. 883 # 884 # This value must be present when `scaleTier` is set to `CUSTOM` and 885 # `workerCount` is greater than zero. 886 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not 887 # set, Google Cloud ML will choose the latest stable version. 888 "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers 889 # and parameter servers. 890 "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training 891 # job's master worker. 892 # 893 # The following types are supported: 894 # 895 # <dl> 896 # <dt>standard</dt> 897 # <dd> 898 # A basic machine configuration suitable for training simple models with 899 # small to moderate datasets. 900 # </dd> 901 # <dt>large_model</dt> 902 # <dd> 903 # A machine with a lot of memory, specially suited for parameter servers 904 # when your model is large (having many hidden layers or layers with very 905 # large numbers of nodes). 906 # </dd> 907 # <dt>complex_model_s</dt> 908 # <dd> 909 # A machine suitable for the master and workers of the cluster when your 910 # model requires more computation than the standard machine can handle 911 # satisfactorily. 912 # </dd> 913 # <dt>complex_model_m</dt> 914 # <dd> 915 # A machine with roughly twice the number of cores and roughly double the 916 # memory of <code suppresswarning="true">complex_model_s</code>. 917 # </dd> 918 # <dt>complex_model_l</dt> 919 # <dd> 920 # A machine with roughly twice the number of cores and roughly double the 921 # memory of <code suppresswarning="true">complex_model_m</code>. 922 # </dd> 923 # <dt>standard_gpu</dt> 924 # <dd> 925 # A machine equivalent to <code suppresswarning="true">standard</code> that 926 # also includes a 927 # <a href="/ml-engine/docs/how-tos/using-gpus"> 928 # GPU that you can use in your trainer</a>. 929 # </dd> 930 # <dt>complex_model_m_gpu</dt> 931 # <dd> 932 # A machine equivalent to 933 # <code suppresswarning="true">complex_model_m</code> that also includes 934 # four GPUs. 935 # </dd> 936 # </dl> 937 # 938 # You must set this value when `scaleTier` is set to `CUSTOM`. 939 "hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune. 940 "maxTrials": 42, # Optional. How many training trials should be attempted to optimize 941 # the specified hyperparameters. 942 # 943 # Defaults to one. 944 "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For 945 # current versions of Tensorflow, this tag name should exactly match what is 946 # shown in Tensorboard, including all scopes. For versions of Tensorflow 947 # prior to 0.12, this should be only the tag passed to tf.Summary. 948 # By default, "training/hptuning/metric" will be used. 949 "params": [ # Required. The set of parameters to tune. 950 { # Represents a single hyperparameter to optimize. 951 "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field 952 # should be unset if type is `CATEGORICAL`. This value should be integers if 953 # type is `INTEGER`. 954 "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories. 955 "A String", 956 ], 957 "discreteValues": [ # Required if type is `DISCRETE`. 958 # A list of feasible points. 959 # The list should be in strictly increasing order. For instance, this 960 # parameter might have possible settings of 1.5, 2.5, and 4.0. This list 961 # should not contain more than 1,000 values. 962 3.14, 963 ], 964 "parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in 965 # a HyperparameterSpec message. E.g., "learning_rate". 966 "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field 967 # should be unset if type is `CATEGORICAL`. This value should be integers if 968 # type is INTEGER. 969 "type": "A String", # Required. The type of the parameter. 970 "scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube. 971 # Leave unset for categorical parameters. 972 # Some kind of scaling is strongly recommended for real or integral 973 # parameters (e.g., `UNIT_LINEAR_SCALE`). 974 }, 975 ], 976 "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently. 977 # You can reduce the time it takes to perform hyperparameter tuning by adding 978 # trials in parallel. However, each trail only benefits from the information 979 # gained in completed trials. That means that a trial does not get access to 980 # the results of trials running at the same time, which could reduce the 981 # quality of the overall optimization. 982 # 983 # Each trial will use the same scale tier and machine types. 984 # 985 # Defaults to one. 986 "goal": "A String", # Required. The type of goal to use for tuning. Available types are 987 # `MAXIMIZE` and `MINIMIZE`. 988 # 989 # Defaults to `MAXIMIZE`. 990 }, 991 "region": "A String", # Required. The Google Compute Engine region to run the training job in. 992 "args": [ # Optional. Command line arguments to pass to the program. 993 "A String", 994 ], 995 "pythonModule": "A String", # Required. The Python module name to run after installing the packages. 996 "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs 997 # and other data needed for training. This path is passed to your TensorFlow 998 # program as the 'job_dir' command-line argument. The benefit of specifying 999 # this field is that Cloud ML validates the path for use in training. 1000 "packageUris": [ # Required. The Google Cloud Storage location of the packages with 1001 # the training program and any additional dependencies. 1002 # The maximum number of package URIs is 100. 1003 "A String", 1004 ], 1005 "workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each 1006 # replica in the cluster will be of the type specified in `worker_type`. 1007 # 1008 # This value can only be used when `scale_tier` is set to `CUSTOM`. If you 1009 # set this value, you must also set `worker_type`. 1010 "parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training 1011 # job's parameter server. 1012 # 1013 # The supported values are the same as those described in the entry for 1014 # `master_type`. 1015 # 1016 # This value must be present when `scaleTier` is set to `CUSTOM` and 1017 # `parameter_server_count` is greater than zero. 1018 "parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training 1019 # job. Each replica in the cluster will be of the type specified in 1020 # `parameter_server_type`. 1021 # 1022 # This value can only be used when `scale_tier` is set to `CUSTOM`.If you 1023 # set this value, you must also set `parameter_server_type`. 1024 }, 1025 "startTime": "A String", # Output only. When the job processing was started. 1026 "errorMessage": "A String", # Output only. The details of a failure or a cancellation. 1027 "jobId": "A String", # Required. The user-specified id of the job. 1028 "state": "A String", # Output only. The detailed state of a job. 1029 "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job. 1030 "modelName": "A String", # Use this field if you want to use the default version for the specified 1031 # model. The string must use the following format: 1032 # 1033 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"` 1034 "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch 1035 # prediction. If not set, Google Cloud ML will pick the runtime version used 1036 # during the CreateVersion request for this model version, or choose the 1037 # latest stable version when model version information is not available 1038 # such as when the model is specified by uri. 1039 "inputPaths": [ # Required. The Google Cloud Storage location of the input data files. 1040 # May contain wildcards. 1041 "A String", 1042 ], 1043 "maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing. 1044 # Defaults to 10 if not specified. 1045 "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for 1046 # the model to use. 1047 "outputPath": "A String", # Required. The output Google Cloud Storage location. 1048 "dataFormat": "A String", # Required. The format of the input data files. 1049 "versionName": "A String", # Use this field if you want to specify a version of the model to use. The 1050 # string is formatted the same way as `model_version`, with the addition 1051 # of the version information: 1052 # 1053 # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"` 1054 "region": "A String", # Required. The Google Compute Engine region to run the prediction job in. 1055 }, 1056 "endTime": "A String", # Output only. When the job processing was completed. 1057 "predictionOutput": { # Represents results of a prediction job. # The current prediction job result. 1058 "outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time. 1059 "nodeHours": 3.14, # Node hours used by the batch prediction job. 1060 "predictionCount": "A String", # The number of generated predictions. 1061 "errorCount": "A String", # The number of data instances which resulted in errors. 1062 }, 1063 "createTime": "A String", # Output only. When the job was created. 1064 }, 1065 ], 1066 }</pre> 1067</div> 1068 1069<div class="method"> 1070 <code class="details" id="list_next">list_next(previous_request, previous_response)</code> 1071 <pre>Retrieves the next page of results. 1072 1073Args: 1074 previous_request: The request for the previous page. (required) 1075 previous_response: The response from the request for the previous page. (required) 1076 1077Returns: 1078 A request object that you can call 'execute()' on to request the next 1079 page. Returns None if there are no more items in the collection. 1080 </pre> 1081</div> 1082 1083</body></html>