Regen docs (#364)
diff --git a/docs/dyn/ml_v1beta1.projects.jobs.html b/docs/dyn/ml_v1beta1.projects.jobs.html
index 35a941e..57245ca 100644
--- a/docs/dyn/ml_v1beta1.projects.jobs.html
+++ b/docs/dyn/ml_v1beta1.projects.jobs.html
@@ -72,7 +72,7 @@
</style>
-<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>
+<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>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="#cancel">cancel(name=None, body, x__xgafv=None)</a></code></p>
@@ -136,9 +136,12 @@
The object takes the form of:
{ # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. # The current training job result.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
+ "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ # Only set for hyperparameter tuning jobs.
+ "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
+ # Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
@@ -159,23 +162,26 @@
},
},
],
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
- "startTime": "A String", # Output only. When the job processing was started.
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
+ # prediction. If not set, Google Cloud ML will pick the runtime version used
+ # during the CreateVersion request for this model version, or choose the
+ # latest stable version when model version information is not available
+ # such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
+ "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
+ # the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
@@ -185,6 +191,10 @@
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
+ "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
+ "jobId": "A String", # Required. The user-specified id of the job.
+ "state": "A String", # Output only. The detailed state of a job.
+ "startTime": "A String", # Output only. When the job processing was started.
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
@@ -194,6 +204,8 @@
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
+ # set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
@@ -229,6 +241,19 @@
# A machine with roughly twice the number of cores and roughly double the
# memory of <code suppresswarning="true">complex_model_m</code>.
# </dd>
+ # <dt>standard_gpu</dt>
+ # <dd>
+ # A machine equivalent to <code suppresswarning="true">standard</code> that
+ # also includes a
+ # <a href="ml/docs/how-tos/using-gpus">
+ # GPU that you can use in your trainer</a>.
+ # </dd>
+ # <dt>complex_model_m_gpu</dt>
+ # <dd>
+ # A machine equivalent to
+ # <code suppresswarning="true">coplex_model_m</code> that also includes
+ # four GPUs.
+ # </dd>
# </dl>
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
@@ -237,14 +262,19 @@
# the specified hyperparameters.
#
# Defaults to one.
+ "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
+ # current versions of Tensorflow, this tag name should exactly match what is
+ # shown in Tensorboard, including all scopes. For versions of Tensorflow
+ # prior to 0.12, this should be only the tag passed to tf.Summary.
+ # By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
- "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is INTEGER.
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
@@ -254,9 +284,9 @@
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
- "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
- "A String",
- ],
+ "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
+ # should be unset if type is `CATEGORICAL`. This value should be integers if
+ # type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
@@ -264,6 +294,10 @@
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
+ "goal": "A String", # Required. The type of goal to use for tuning. Available types are
+ # `MAXIMIZE` and `MINIMIZE`.
+ #
+ # Defaults to `MAXIMIZE`.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
@@ -274,16 +308,16 @@
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
+ "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
+ # and other data needed for training. This path is passed to your TensorFlow
+ # program as the 'job_dir' command-line argument. The benefit of specifying
+ # this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
"A String",
@@ -311,6 +345,7 @@
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
+ "nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
@@ -326,9 +361,12 @@
An object of the form:
{ # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. # The current training job result.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
+ "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ # Only set for hyperparameter tuning jobs.
+ "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
+ # Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
@@ -349,23 +387,26 @@
},
},
],
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
- "startTime": "A String", # Output only. When the job processing was started.
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
+ # prediction. If not set, Google Cloud ML will pick the runtime version used
+ # during the CreateVersion request for this model version, or choose the
+ # latest stable version when model version information is not available
+ # such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
+ "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
+ # the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
@@ -375,6 +416,10 @@
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
+ "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
+ "jobId": "A String", # Required. The user-specified id of the job.
+ "state": "A String", # Output only. The detailed state of a job.
+ "startTime": "A String", # Output only. When the job processing was started.
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
@@ -384,6 +429,8 @@
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
+ # set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
@@ -419,6 +466,19 @@
# A machine with roughly twice the number of cores and roughly double the
# memory of <code suppresswarning="true">complex_model_m</code>.
# </dd>
+ # <dt>standard_gpu</dt>
+ # <dd>
+ # A machine equivalent to <code suppresswarning="true">standard</code> that
+ # also includes a
+ # <a href="ml/docs/how-tos/using-gpus">
+ # GPU that you can use in your trainer</a>.
+ # </dd>
+ # <dt>complex_model_m_gpu</dt>
+ # <dd>
+ # A machine equivalent to
+ # <code suppresswarning="true">coplex_model_m</code> that also includes
+ # four GPUs.
+ # </dd>
# </dl>
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
@@ -427,14 +487,19 @@
# the specified hyperparameters.
#
# Defaults to one.
+ "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
+ # current versions of Tensorflow, this tag name should exactly match what is
+ # shown in Tensorboard, including all scopes. For versions of Tensorflow
+ # prior to 0.12, this should be only the tag passed to tf.Summary.
+ # By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
- "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is INTEGER.
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
@@ -444,9 +509,9 @@
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
- "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
- "A String",
- ],
+ "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
+ # should be unset if type is `CATEGORICAL`. This value should be integers if
+ # type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
@@ -454,6 +519,10 @@
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
+ "goal": "A String", # Required. The type of goal to use for tuning. Available types are
+ # `MAXIMIZE` and `MINIMIZE`.
+ #
+ # Defaults to `MAXIMIZE`.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
@@ -464,16 +533,16 @@
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
+ "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
+ # and other data needed for training. This path is passed to your TensorFlow
+ # program as the 'job_dir' command-line argument. The benefit of specifying
+ # this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
"A String",
@@ -501,6 +570,7 @@
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
+ "nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
@@ -525,9 +595,12 @@
An object of the form:
{ # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. # The current training job result.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
+ "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ # Only set for hyperparameter tuning jobs.
+ "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
+ # Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
@@ -548,23 +621,26 @@
},
},
],
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
- "startTime": "A String", # Output only. When the job processing was started.
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
+ # prediction. If not set, Google Cloud ML will pick the runtime version used
+ # during the CreateVersion request for this model version, or choose the
+ # latest stable version when model version information is not available
+ # such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
+ "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
+ # the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
@@ -574,6 +650,10 @@
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
+ "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
+ "jobId": "A String", # Required. The user-specified id of the job.
+ "state": "A String", # Output only. The detailed state of a job.
+ "startTime": "A String", # Output only. When the job processing was started.
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
@@ -583,6 +663,8 @@
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
+ # set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
@@ -618,6 +700,19 @@
# A machine with roughly twice the number of cores and roughly double the
# memory of <code suppresswarning="true">complex_model_m</code>.
# </dd>
+ # <dt>standard_gpu</dt>
+ # <dd>
+ # A machine equivalent to <code suppresswarning="true">standard</code> that
+ # also includes a
+ # <a href="ml/docs/how-tos/using-gpus">
+ # GPU that you can use in your trainer</a>.
+ # </dd>
+ # <dt>complex_model_m_gpu</dt>
+ # <dd>
+ # A machine equivalent to
+ # <code suppresswarning="true">coplex_model_m</code> that also includes
+ # four GPUs.
+ # </dd>
# </dl>
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
@@ -626,14 +721,19 @@
# the specified hyperparameters.
#
# Defaults to one.
+ "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
+ # current versions of Tensorflow, this tag name should exactly match what is
+ # shown in Tensorboard, including all scopes. For versions of Tensorflow
+ # prior to 0.12, this should be only the tag passed to tf.Summary.
+ # By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
- "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is INTEGER.
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
@@ -643,9 +743,9 @@
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
- "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
- "A String",
- ],
+ "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
+ # should be unset if type is `CATEGORICAL`. This value should be integers if
+ # type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
@@ -653,6 +753,10 @@
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
+ "goal": "A String", # Required. The type of goal to use for tuning. Available types are
+ # `MAXIMIZE` and `MINIMIZE`.
+ #
+ # Defaults to `MAXIMIZE`.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
@@ -663,16 +767,16 @@
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
+ "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
+ # and other data needed for training. This path is passed to your TensorFlow
+ # program as the 'job_dir' command-line argument. The benefit of specifying
+ # this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
"A String",
@@ -700,6 +804,7 @@
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
+ "nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
@@ -738,9 +843,12 @@
# subsequent call.
"jobs": [ # The list of jobs.
{ # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. # The current training job result.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
+ "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
+ "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ # Only set for hyperparameter tuning jobs.
+ "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"trials": [ # Results for individual Hyperparameter trials.
+ # Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
@@ -761,23 +869,26 @@
},
},
],
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
+ "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
},
- "startTime": "A String", # Output only. When the job processing was started.
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
"predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
"modelName": "A String", # Use this field if you want to use the default version for the specified
# model. The string must use the following format:
#
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
+ # prediction. If not set, Google Cloud ML will pick the runtime version used
+ # during the CreateVersion request for this model version, or choose the
+ # latest stable version when model version information is not available
+ # such as when the model is specified by uri.
"inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
# May contain wildcards.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
+ "uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
+ # the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
@@ -787,6 +898,10 @@
# `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
},
+ "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
+ "jobId": "A String", # Required. The user-specified id of the job.
+ "state": "A String", # Output only. The detailed state of a job.
+ "startTime": "A String", # Output only. When the job processing was started.
"trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
@@ -796,6 +911,8 @@
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
+ "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
+ # set, Google Cloud ML will choose the latest stable version.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
@@ -831,6 +948,19 @@
# A machine with roughly twice the number of cores and roughly double the
# memory of <code suppresswarning="true">complex_model_m</code>.
# </dd>
+ # <dt>standard_gpu</dt>
+ # <dd>
+ # A machine equivalent to <code suppresswarning="true">standard</code> that
+ # also includes a
+ # <a href="ml/docs/how-tos/using-gpus">
+ # GPU that you can use in your trainer</a>.
+ # </dd>
+ # <dt>complex_model_m_gpu</dt>
+ # <dd>
+ # A machine equivalent to
+ # <code suppresswarning="true">coplex_model_m</code> that also includes
+ # four GPUs.
+ # </dd>
# </dl>
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
@@ -839,14 +969,19 @@
# the specified hyperparameters.
#
# Defaults to one.
+ "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
+ # current versions of Tensorflow, this tag name should exactly match what is
+ # shown in Tensorboard, including all scopes. For versions of Tensorflow
+ # prior to 0.12, this should be only the tag passed to tf.Summary.
+ # By default, "training/hptuning/metric" will be used.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is `INTEGER`.
- "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is INTEGER.
+ "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
+ "A String",
+ ],
"discreteValues": [ # Required if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. For instance, this
@@ -856,9 +991,9 @@
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
- "categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
- "A String",
- ],
+ "minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
+ # should be unset if type is `CATEGORICAL`. This value should be integers if
+ # type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
@@ -866,6 +1001,10 @@
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
+ "goal": "A String", # Required. The type of goal to use for tuning. Available types are
+ # `MAXIMIZE` and `MINIMIZE`.
+ #
+ # Defaults to `MAXIMIZE`.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
@@ -876,16 +1015,16 @@
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
"args": [ # Optional. Command line arguments to pass to the program.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
+ "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
+ # and other data needed for training. This path is passed to your TensorFlow
+ # program as the 'job_dir' command-line argument. The benefit of specifying
+ # this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
"A String",
@@ -913,6 +1052,7 @@
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
+ "nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},