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<h1><a href="ml_v1.html">Cloud Machine Learning Engine</a> . <a href="ml_v1.projects.html">projects</a> . <a href="ml_v1.projects.jobs.html">jobs</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="#cancel">cancel(name, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Cancels a running job.</p>
<p class="toc_element">
<code><a href="#create">create(parent, body, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a training or a batch prediction job.</p>
<p class="toc_element">
<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Describes a job.</p>
<p class="toc_element">
<code><a href="#getIamPolicy">getIamPolicy(resource, x__xgafv=None)</a></code></p>
<p class="firstline">Gets the access control policy for a resource.</p>
<p class="toc_element">
<code><a href="#list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</a></code></p>
<p class="firstline">Lists the jobs in the project.</p>
<p class="toc_element">
<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
<code><a href="#patch">patch(name, body, updateMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Updates a specific job resource.</p>
<p class="toc_element">
<code><a href="#setIamPolicy">setIamPolicy(resource, body, x__xgafv=None)</a></code></p>
<p class="firstline">Sets the access control policy on the specified resource. Replaces any</p>
<p class="toc_element">
<code><a href="#testIamPermissions">testIamPermissions(resource, body, x__xgafv=None)</a></code></p>
<p class="firstline">Returns permissions that a caller has on the specified resource.</p>
<h3>Method Details</h3>
<div class="method">
<code class="details" id="cancel">cancel(name, body=None, x__xgafv=None)</code>
<pre>Cancels a running job.
Args:
name: string, Required. The name of the job to cancel. (required)
body: object, The request body.
The object takes the form of:
{ # Request message for the CancelJob method.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A generic empty message that you can re-use to avoid defining duplicated
# empty messages in your APIs. A typical example is to use it as the request
# or the response type of an API method. For instance:
#
# service Foo {
# rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
# }
#
# The JSON representation for `Empty` is empty JSON object `{}`.
}</pre>
</div>
<div class="method">
<code class="details" id="create">create(parent, body, x__xgafv=None)</code>
<pre>Creates a training or a batch prediction job.
Args:
parent: string, Required. The project name. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}</pre>
</div>
<div class="method">
<code class="details" id="get">get(name, x__xgafv=None)</code>
<pre>Describes a job.
Args:
name: string, Required. The name of the job to get the description of. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}</pre>
</div>
<div class="method">
<code class="details" id="getIamPolicy">getIamPolicy(resource, x__xgafv=None)</code>
<pre>Gets the access control policy for a resource.
Returns an empty policy if the resource exists and does not have a policy
set.
Args:
resource: string, REQUIRED: The resource for which the policy is being requested.
See the operation documentation for the appropriate value for this field. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Defines an Identity and Access Management (IAM) policy. It is used to
# specify access control policies for Cloud Platform resources.
#
#
# A `Policy` consists of a list of `bindings`. A `binding` binds a list of
# `members` to a `role`, where the members can be user accounts, Google groups,
# Google domains, and service accounts. A `role` is a named list of permissions
# defined by IAM.
#
# **JSON Example**
#
# {
# "bindings": [
# {
# "role": "roles/owner",
# "members": [
# "user:[email protected]",
# "group:[email protected]",
# "domain:google.com",
# "serviceAccount:[email protected]"
# ]
# },
# {
# "role": "roles/viewer",
# "members": ["user:[email protected]"]
# }
# ]
# }
#
# **YAML Example**
#
# bindings:
# - members:
# - user:[email protected]
# - group:[email protected]
# - domain:google.com
# - serviceAccount:[email protected]
# role: roles/owner
# - members:
# - user:[email protected]
# role: roles/viewer
#
#
# For a description of IAM and its features, see the
# [IAM developer's guide](https://cloud.google.com/iam/docs).
"bindings": [ # Associates a list of `members` to a `role`.
# `bindings` with no members will result in an error.
{ # Associates `members` with a `role`.
"role": "A String", # Role that is assigned to `members`.
# For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
"members": [ # Specifies the identities requesting access for a Cloud Platform resource.
# `members` can have the following values:
#
# * `allUsers`: A special identifier that represents anyone who is
# on the internet; with or without a Google account.
#
# * `allAuthenticatedUsers`: A special identifier that represents anyone
# who is authenticated with a Google account or a service account.
#
# * `user:{emailid}`: An email address that represents a specific Google
# account. For example, `[email protected]` .
#
#
# * `serviceAccount:{emailid}`: An email address that represents a service
# account. For example, `[email protected]`.
#
# * `group:{emailid}`: An email address that represents a Google group.
# For example, `[email protected]`.
#
#
# * `domain:{domain}`: The G Suite domain (primary) that represents all the
# users of that domain. For example, `google.com` or `example.com`.
#
"A String",
],
"condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
# NOTE: An unsatisfied condition will not allow user access via current
# binding. Different bindings, including their conditions, are examined
# independently.
#
# title: "User account presence"
# description: "Determines whether the request has a user account"
# expression: "size(request.user) > 0"
"description": "A String", # An optional description of the expression. This is a longer text which
# describes the expression, e.g. when hovered over it in a UI.
"expression": "A String", # Textual representation of an expression in
# Common Expression Language syntax.
#
# The application context of the containing message determines which
# well-known feature set of CEL is supported.
"location": "A String", # An optional string indicating the location of the expression for error
# reporting, e.g. a file name and a position in the file.
"title": "A String", # An optional title for the expression, i.e. a short string describing
# its purpose. This can be used e.g. in UIs which allow to enter the
# expression.
},
},
],
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a policy from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform policy updates in order to avoid race
# conditions: An `etag` is returned in the response to `getIamPolicy`, and
# systems are expected to put that etag in the request to `setIamPolicy` to
# ensure that their change will be applied to the same version of the policy.
#
# If no `etag` is provided in the call to `setIamPolicy`, then the existing
# policy is overwritten blindly.
"version": 42, # Deprecated.
"auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
{ # Specifies the audit configuration for a service.
# The configuration determines which permission types are logged, and what
# identities, if any, are exempted from logging.
# An AuditConfig must have one or more AuditLogConfigs.
#
# If there are AuditConfigs for both `allServices` and a specific service,
# the union of the two AuditConfigs is used for that service: the log_types
# specified in each AuditConfig are enabled, and the exempted_members in each
# AuditLogConfig are exempted.
#
# Example Policy with multiple AuditConfigs:
#
# {
# "audit_configs": [
# {
# "service": "allServices"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "fooservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:[email protected]"
# ]
# }
# ]
# }
# ]
# }
#
# For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts [email protected] from DATA_READ logging, and
# [email protected] from DATA_WRITE logging.
"auditLogConfigs": [ # The configuration for logging of each type of permission.
{ # Provides the configuration for logging a type of permissions.
# Example:
#
# {
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# [email protected] from DATA_READ logging.
"exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
# permission.
# Follows the same format of Binding.members.
"A String",
],
"logType": "A String", # The log type that this config enables.
},
],
"service": "A String", # Specifies a service that will be enabled for audit logging.
# For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
# `allServices` is a special value that covers all services.
},
],
}</pre>
</div>
<div class="method">
<code class="details" id="list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</code>
<pre>Lists the jobs in the project.
If there are no jobs that match the request parameters, the list
request returns an empty response body: {}.
Args:
parent: string, Required. The name of the project for which to list jobs. (required)
pageToken: string, Optional. A page token to request the next page of results.
You get the token from the `next_page_token` field of the response from
the previous call.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
pageSize: integer, Optional. The number of jobs to retrieve per "page" of results. If there
are more remaining results than this number, the response message will
contain a valid value in the `next_page_token` field.
The default value is 20, and the maximum page size is 100.
filter: string, Optional. Specifies the subset of jobs to retrieve.
You can filter on the value of one or more attributes of the job object.
For example, retrieve jobs with a job identifier that starts with 'census':
<p><code>gcloud ai-platform jobs list --filter='jobId:census*'</code>
<p>List all failed jobs with names that start with 'rnn':
<p><code>gcloud ai-platform jobs list --filter='jobId:rnn*
AND state:FAILED'</code>
<p>For more examples, see the guide to
<a href="/ml-engine/docs/tensorflow/monitor-training">monitoring jobs</a>.
Returns:
An object of the form:
{ # Response message for the ListJobs method.
"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
# subsequent call.
"jobs": [ # The list of jobs.
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
},
],
}</pre>
</div>
<div class="method">
<code class="details" id="list_next">list_next(previous_request, previous_response)</code>
<pre>Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
</pre>
</div>
<div class="method">
<code class="details" id="patch">patch(name, body, updateMask=None, x__xgafv=None)</code>
<pre>Updates a specific job resource.
Currently the only supported fields to update are `labels`.
Args:
name: string, Required. The job name. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
updateMask: string, Required. Specifies the path, relative to `Job`, of the field to update.
To adopt etag mechanism, include `etag` field in the mask, and include the
`etag` value in your job resource.
For example, to change the labels of a job, the `update_mask` parameter
would be specified as `labels`, `etag`, and the
`PATCH` request body would specify the new value, as follows:
{
"labels": {
"owner": "Google",
"color": "Blue"
}
"etag": "33a64df551425fcc55e4d42a148795d9f25f89d4"
}
If `etag` matches the one on the server, the labels of the job will be
replaced with the given ones, and the server end `etag` will be
recalculated.
Currently the only supported update masks are `labels` and `etag`.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
"finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
"allMetrics": [ # All recorded object metrics for this trial. This field is not currently
# populated.
{ # An observed value of a metric.
"trainingStep": "A String", # The global training step for this metric.
"objectiveValue": 3.14, # The objective value at this training step.
},
],
"isTrialStoppedEarly": True or False, # True if the trial is stopped early.
"trialId": "A String", # The trial id for these results.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for trials of built-in algorithms jobs that have succeeded.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
],
"isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
"isBuiltInAlgorithmJob": True or False, # Whether this job is a built-in Algorithm job.
"consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
"hyperparameterMetricTag": "A String", # The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See
# [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.FIELDS.hyperparameter_metric_tag)
# for more information. Only set for hyperparameter tuning jobs.
"builtInAlgorithmOutput": { # Represents output related to a built-in algorithm Job. # Details related to built-in algorithms jobs.
# Only set for built-in algorithms jobs.
"framework": "A String", # Framework on which the built-in algorithm was trained.
"modelPath": "A String", # The Cloud Storage path to the `model/` directory where the training job
# saves the trained model. Only set for successful jobs that don't use
# hyperparameter tuning.
"runtimeVersion": "A String", # AI Platform runtime version on which the built-in algorithm was
# trained.
"pythonVersion": "A String", # Python version on which the built-in algorithm was trained.
},
},
"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/YOUR_PROJECT/models/YOUR_MODEL"`
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this batch
# prediction. If not set, AI Platform 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.
"signatureName": "A String", # Optional. The name of the signature defined in the SavedModel to use for
# this job. Please refer to
# [SavedModel](https://tensorflow.github.io/serving/serving_basic.html)
# for information about how to use signatures.
#
# Defaults to
# [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants)
# , which is "serving_default".
"batchSize": "A String", # Optional. Number of records per batch, defaults to 64.
# The service will buffer batch_size number of records in memory before
# invoking one Tensorflow prediction call internally. So take the record
# size and memory available into consideration when setting this parameter.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"uri": "A String", # Use this field if you want to specify a Google Cloud Storage path for
# the model to use.
"outputPath": "A String", # Required. The output Google Cloud Storage location.
"dataFormat": "A String", # Required. The format of the input data files.
"versionName": "A String", # Use this field if you want to specify a version of the model to use. The
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
# `"projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"`
"region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the # Input parameters to create a training job.
# gcloud command to submit your training job, you can specify
# the input parameters as command-line arguments and/or in a YAML configuration
# file referenced from the --config command-line argument. For
# details, see the guide to
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
"workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's worker nodes.
#
# The supported values are the same as those described in the entry for
# `masterType`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
# machine](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `workerCount` is greater than zero.
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerConfigType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `parameterServerConfig.imageUri` only if you build a custom image for
# your parameter server. If `parameterServerConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`.
# Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker.
#
# The following types are supported:
#
# <dl>
# <dt>standard</dt>
# <dd>
# A basic machine configuration suitable for training simple models with
# small to moderate datasets.
# </dd>
# <dt>large_model</dt>
# <dd>
# A machine with a lot of memory, specially suited for parameter servers
# when your model is large (having many hidden layers or layers with very
# large numbers of nodes).
# </dd>
# <dt>complex_model_s</dt>
# <dd>
# A machine suitable for the master and workers of the cluster when your
# model requires more computation than the standard machine can handle
# satisfactorily.
# </dd>
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_s</i>.
# </dd>
# <dt>complex_model_l</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
# memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla K80 GPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-gpus">using GPUs to
# train your model</a>.
# </dd>
# <dt>complex_model_m_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>complex_model_l_gpu</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that also includes
# eight NVIDIA Tesla K80 GPUs.
# </dd>
# <dt>standard_p100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla P100 GPU.
# </dd>
# <dt>complex_model_m_p100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that also includes
# four NVIDIA Tesla P100 GPUs.
# </dd>
# <dt>standard_v100</dt>
# <dd>
# A machine equivalent to <i>standard</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>large_model_v100</dt>
# <dd>
# A machine equivalent to <i>large_model</i> that
# also includes a single NVIDIA Tesla V100 GPU.
# </dd>
# <dt>complex_model_m_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_m</i> that
# also includes four NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>complex_model_l_v100</dt>
# <dd>
# A machine equivalent to <i>complex_model_l</i> that
# also includes eight NVIDIA Tesla V100 GPUs.
# </dd>
# <dt>cloud_tpu</dt>
# <dd>
# A TPU VM including one Cloud TPU. See more about
# <a href="/ml-engine/docs/tensorflow/using-tpus">using TPUs to train
# your model</a>.
# </dd>
# </dl>
#
# You may also use certain Compute Engine machine types directly in this
# field. The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
# See more about [using Compute Engine machine
# types](/ml-engine/docs/tensorflow/machine-types#compute-engine-machine-types).
#
# You must set this value when `scaleTier` is set to `CUSTOM`.
"hyperparameters": { # Represents a set of hyperparameters to optimize. # Optional. The set of Hyperparameters to tune.
"maxTrials": 42, # Optional. How many training trials should be attempted to optimize
# the specified hyperparameters.
#
# Defaults to one.
"goal": "A String", # Required. The type of goal to use for tuning. Available types are
# `MAXIMIZE` and `MINIMIZE`.
#
# Defaults to `MAXIMIZE`.
"algorithm": "A String", # Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
"maxFailedTrials": 42, # Optional. The number of failed trials that need to be seen before failing
# the hyperparameter tuning job. You can specify this field to override the
# default failing criteria for AI Platform hyperparameter tuning jobs.
#
# Defaults to zero, which means the service decides when a hyperparameter
# job should fail.
"enableTrialEarlyStopping": True or False, # Optional. Indicates if the hyperparameter tuning job enables auto trial
# early stopping.
"resumePreviousJobId": "A String", # Optional. The prior hyperparameter tuning job id that users hope to
# continue with. The job id will be used to find the corresponding vizier
# study guid and resume the study.
"params": [ # Required. The set of parameters to tune.
{ # Represents a single hyperparameter to optimize.
"maxValue": 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
# parameter might have possible settings of 1.5, 2.5, and 4.0. This list
# should not contain more than 1,000 values.
3.14,
],
"parameterName": "A String", # Required. The parameter name must be unique amongst all ParameterConfigs in
# a HyperparameterSpec message. E.g., "learning_rate".
"minValue": 3.14, # Required if type is `DOUBLE` or `INTEGER`. This field
# should be unset if type is `CATEGORICAL`. This value should be integers if
# type is INTEGER.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # Optional. How the parameter should be scaled to the hypercube.
# Leave unset for categorical parameters.
# Some kind of scaling is strongly recommended for real or integral
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
"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.
"maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
# You can reduce the time it takes to perform hyperparameter tuning by adding
# trials in parallel. However, each trail only benefits from the information
# gained in completed trials. That means that a trial does not get access to
# the results of trials running at the same time, which could reduce the
# quality of the overall optimization.
#
# Each trial will use the same scale tier and machine types.
#
# Defaults to one.
},
"region": "A String", # Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"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.
"pythonVersion": "A String", # Optional. The version of Python used in training. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
"jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
# and other data needed for training. This path is passed to your TensorFlow
# program as the '--job-dir' command-line argument. The benefit of specifying
# this field is that Cloud ML validates the path for use in training.
"packageUris": [ # Required. The Google Cloud Storage location of the packages with
# the training program and any additional dependencies.
# The maximum number of package URIs is 100.
"A String",
],
"workerCount": "A String", # Optional. The number of worker replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `worker_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `worker_type`.
#
# The default value is zero.
"parameterServerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's parameter server.
#
# The supported values are the same as those described in the entry for
# `master_type`.
#
# This value must be consistent with the category of machine type that
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
"workerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for workers.
#
# You should only set `workerConfig.acceleratorConfig` if `workerType` is set
# to a Compute Engine machine type. [Learn about restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `workerConfig.imageUri` only if you build a custom image for your
# worker. If `workerConfig.imageUri` has not been set, AI Platform uses
# the value of `masterConfig.imageUri`. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"maxRunningTime": "A String", # Optional. The maximum job running time. The default is 7 days.
"masterConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for your master worker.
#
# You should only set `masterConfig.acceleratorConfig` if `masterType` is set
# to a Compute Engine machine type. Learn about [restrictions on accelerator
# configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `masterConfig.imageUri` only if you build a custom image. Only one of
# `masterConfig.imageUri` and `runtimeVersion` should be set. Learn more about
# [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
"acceleratorConfig": { # Represents a hardware accelerator request config. # Represents the type and number of accelerators used by the replica.
# [Learn about restrictions on accelerator configurations for
# training.](/ml-engine/docs/tensorflow/using-gpus#compute-engine-machine-types-with-gpu)
"count": "A String", # The number of accelerators to attach to each machine running the job.
"type": "A String", # The type of accelerator to use.
},
"imageUri": "A String", # The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/docs/distributed-training-containers).
},
"parameterServerCount": "A String", # Optional. The number of parameter server replicas to use for the training
# job. Each replica in the cluster will be of the type specified in
# `parameter_server_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`.If you
# set this value, you must also set `parameter_server_type`.
#
# The default value is zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}</pre>
</div>
<div class="method">
<code class="details" id="setIamPolicy">setIamPolicy(resource, body, x__xgafv=None)</code>
<pre>Sets the access control policy on the specified resource. Replaces any
existing policy.
Args:
resource: string, REQUIRED: The resource for which the policy is being specified.
See the operation documentation for the appropriate value for this field. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Request message for `SetIamPolicy` method.
"policy": { # Defines an Identity and Access Management (IAM) policy. It is used to # REQUIRED: The complete policy to be applied to the `resource`. The size of
# the policy is limited to a few 10s of KB. An empty policy is a
# valid policy but certain Cloud Platform services (such as Projects)
# might reject them.
# specify access control policies for Cloud Platform resources.
#
#
# A `Policy` consists of a list of `bindings`. A `binding` binds a list of
# `members` to a `role`, where the members can be user accounts, Google groups,
# Google domains, and service accounts. A `role` is a named list of permissions
# defined by IAM.
#
# **JSON Example**
#
# {
# "bindings": [
# {
# "role": "roles/owner",
# "members": [
# "user:[email protected]",
# "group:[email protected]",
# "domain:google.com",
# "serviceAccount:[email protected]"
# ]
# },
# {
# "role": "roles/viewer",
# "members": ["user:[email protected]"]
# }
# ]
# }
#
# **YAML Example**
#
# bindings:
# - members:
# - user:[email protected]
# - group:[email protected]
# - domain:google.com
# - serviceAccount:[email protected]
# role: roles/owner
# - members:
# - user:[email protected]
# role: roles/viewer
#
#
# For a description of IAM and its features, see the
# [IAM developer's guide](https://cloud.google.com/iam/docs).
"bindings": [ # Associates a list of `members` to a `role`.
# `bindings` with no members will result in an error.
{ # Associates `members` with a `role`.
"role": "A String", # Role that is assigned to `members`.
# For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
"members": [ # Specifies the identities requesting access for a Cloud Platform resource.
# `members` can have the following values:
#
# * `allUsers`: A special identifier that represents anyone who is
# on the internet; with or without a Google account.
#
# * `allAuthenticatedUsers`: A special identifier that represents anyone
# who is authenticated with a Google account or a service account.
#
# * `user:{emailid}`: An email address that represents a specific Google
# account. For example, `[email protected]` .
#
#
# * `serviceAccount:{emailid}`: An email address that represents a service
# account. For example, `[email protected]`.
#
# * `group:{emailid}`: An email address that represents a Google group.
# For example, `[email protected]`.
#
#
# * `domain:{domain}`: The G Suite domain (primary) that represents all the
# users of that domain. For example, `google.com` or `example.com`.
#
"A String",
],
"condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
# NOTE: An unsatisfied condition will not allow user access via current
# binding. Different bindings, including their conditions, are examined
# independently.
#
# title: "User account presence"
# description: "Determines whether the request has a user account"
# expression: "size(request.user) > 0"
"description": "A String", # An optional description of the expression. This is a longer text which
# describes the expression, e.g. when hovered over it in a UI.
"expression": "A String", # Textual representation of an expression in
# Common Expression Language syntax.
#
# The application context of the containing message determines which
# well-known feature set of CEL is supported.
"location": "A String", # An optional string indicating the location of the expression for error
# reporting, e.g. a file name and a position in the file.
"title": "A String", # An optional title for the expression, i.e. a short string describing
# its purpose. This can be used e.g. in UIs which allow to enter the
# expression.
},
},
],
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a policy from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform policy updates in order to avoid race
# conditions: An `etag` is returned in the response to `getIamPolicy`, and
# systems are expected to put that etag in the request to `setIamPolicy` to
# ensure that their change will be applied to the same version of the policy.
#
# If no `etag` is provided in the call to `setIamPolicy`, then the existing
# policy is overwritten blindly.
"version": 42, # Deprecated.
"auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
{ # Specifies the audit configuration for a service.
# The configuration determines which permission types are logged, and what
# identities, if any, are exempted from logging.
# An AuditConfig must have one or more AuditLogConfigs.
#
# If there are AuditConfigs for both `allServices` and a specific service,
# the union of the two AuditConfigs is used for that service: the log_types
# specified in each AuditConfig are enabled, and the exempted_members in each
# AuditLogConfig are exempted.
#
# Example Policy with multiple AuditConfigs:
#
# {
# "audit_configs": [
# {
# "service": "allServices"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "fooservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:[email protected]"
# ]
# }
# ]
# }
# ]
# }
#
# For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts [email protected] from DATA_READ logging, and
# [email protected] from DATA_WRITE logging.
"auditLogConfigs": [ # The configuration for logging of each type of permission.
{ # Provides the configuration for logging a type of permissions.
# Example:
#
# {
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# [email protected] from DATA_READ logging.
"exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
# permission.
# Follows the same format of Binding.members.
"A String",
],
"logType": "A String", # The log type that this config enables.
},
],
"service": "A String", # Specifies a service that will be enabled for audit logging.
# For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
# `allServices` is a special value that covers all services.
},
],
},
"updateMask": "A String", # OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only
# the fields in the mask will be modified. If no mask is provided, the
# following default mask is used:
# paths: "bindings, etag"
# This field is only used by Cloud IAM.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Defines an Identity and Access Management (IAM) policy. It is used to
# specify access control policies for Cloud Platform resources.
#
#
# A `Policy` consists of a list of `bindings`. A `binding` binds a list of
# `members` to a `role`, where the members can be user accounts, Google groups,
# Google domains, and service accounts. A `role` is a named list of permissions
# defined by IAM.
#
# **JSON Example**
#
# {
# "bindings": [
# {
# "role": "roles/owner",
# "members": [
# "user:[email protected]",
# "group:[email protected]",
# "domain:google.com",
# "serviceAccount:[email protected]"
# ]
# },
# {
# "role": "roles/viewer",
# "members": ["user:[email protected]"]
# }
# ]
# }
#
# **YAML Example**
#
# bindings:
# - members:
# - user:[email protected]
# - group:[email protected]
# - domain:google.com
# - serviceAccount:[email protected]
# role: roles/owner
# - members:
# - user:[email protected]
# role: roles/viewer
#
#
# For a description of IAM and its features, see the
# [IAM developer's guide](https://cloud.google.com/iam/docs).
"bindings": [ # Associates a list of `members` to a `role`.
# `bindings` with no members will result in an error.
{ # Associates `members` with a `role`.
"role": "A String", # Role that is assigned to `members`.
# For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
"members": [ # Specifies the identities requesting access for a Cloud Platform resource.
# `members` can have the following values:
#
# * `allUsers`: A special identifier that represents anyone who is
# on the internet; with or without a Google account.
#
# * `allAuthenticatedUsers`: A special identifier that represents anyone
# who is authenticated with a Google account or a service account.
#
# * `user:{emailid}`: An email address that represents a specific Google
# account. For example, `[email protected]` .
#
#
# * `serviceAccount:{emailid}`: An email address that represents a service
# account. For example, `[email protected]`.
#
# * `group:{emailid}`: An email address that represents a Google group.
# For example, `[email protected]`.
#
#
# * `domain:{domain}`: The G Suite domain (primary) that represents all the
# users of that domain. For example, `google.com` or `example.com`.
#
"A String",
],
"condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
# NOTE: An unsatisfied condition will not allow user access via current
# binding. Different bindings, including their conditions, are examined
# independently.
#
# title: "User account presence"
# description: "Determines whether the request has a user account"
# expression: "size(request.user) > 0"
"description": "A String", # An optional description of the expression. This is a longer text which
# describes the expression, e.g. when hovered over it in a UI.
"expression": "A String", # Textual representation of an expression in
# Common Expression Language syntax.
#
# The application context of the containing message determines which
# well-known feature set of CEL is supported.
"location": "A String", # An optional string indicating the location of the expression for error
# reporting, e.g. a file name and a position in the file.
"title": "A String", # An optional title for the expression, i.e. a short string describing
# its purpose. This can be used e.g. in UIs which allow to enter the
# expression.
},
},
],
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a policy from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform policy updates in order to avoid race
# conditions: An `etag` is returned in the response to `getIamPolicy`, and
# systems are expected to put that etag in the request to `setIamPolicy` to
# ensure that their change will be applied to the same version of the policy.
#
# If no `etag` is provided in the call to `setIamPolicy`, then the existing
# policy is overwritten blindly.
"version": 42, # Deprecated.
"auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
{ # Specifies the audit configuration for a service.
# The configuration determines which permission types are logged, and what
# identities, if any, are exempted from logging.
# An AuditConfig must have one or more AuditLogConfigs.
#
# If there are AuditConfigs for both `allServices` and a specific service,
# the union of the two AuditConfigs is used for that service: the log_types
# specified in each AuditConfig are enabled, and the exempted_members in each
# AuditLogConfig are exempted.
#
# Example Policy with multiple AuditConfigs:
#
# {
# "audit_configs": [
# {
# "service": "allServices"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "fooservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:[email protected]"
# ]
# }
# ]
# }
# ]
# }
#
# For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts [email protected] from DATA_READ logging, and
# [email protected] from DATA_WRITE logging.
"auditLogConfigs": [ # The configuration for logging of each type of permission.
{ # Provides the configuration for logging a type of permissions.
# Example:
#
# {
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# "exempted_members": [
# "user:[email protected]"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# [email protected] from DATA_READ logging.
"exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
# permission.
# Follows the same format of Binding.members.
"A String",
],
"logType": "A String", # The log type that this config enables.
},
],
"service": "A String", # Specifies a service that will be enabled for audit logging.
# For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
# `allServices` is a special value that covers all services.
},
],
}</pre>
</div>
<div class="method">
<code class="details" id="testIamPermissions">testIamPermissions(resource, body, x__xgafv=None)</code>
<pre>Returns permissions that a caller has on the specified resource.
If the resource does not exist, this will return an empty set of
permissions, not a NOT_FOUND error.
Note: This operation is designed to be used for building permission-aware
UIs and command-line tools, not for authorization checking. This operation
may "fail open" without warning.
Args:
resource: string, REQUIRED: The resource for which the policy detail is being requested.
See the operation documentation for the appropriate value for this field. (required)
body: object, The request body. (required)
The object takes the form of:
{ # Request message for `TestIamPermissions` method.
"permissions": [ # The set of permissions to check for the `resource`. Permissions with
# wildcards (such as '*' or 'storage.*') are not allowed. For more
# information see
# [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions).
"A String",
],
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response message for `TestIamPermissions` method.
"permissions": [ # A subset of `TestPermissionsRequest.permissions` that the caller is
# allowed.
"A String",
],
}</pre>
</div>
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