Regen all docs. (#700)
* Stop recursing if discovery == {}
* Generate docs with 'make docs'.
diff --git a/docs/dyn/ml_v1.projects.jobs.html b/docs/dyn/ml_v1.projects.jobs.html
index 50e5559..d0a9ac2 100644
--- a/docs/dyn/ml_v1.projects.jobs.html
+++ b/docs/dyn/ml_v1.projects.jobs.html
@@ -72,10 +72,10 @@
</style>
-<h1><a href="ml_v1.html">Google Cloud Machine Learning Engine</a> . <a href="ml_v1.projects.html">projects</a> . <a href="ml_v1.projects.jobs.html">jobs</a></h1>
+<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, x__xgafv=None)</a></code></p>
+ <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>
@@ -84,21 +84,31 @@
<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="#list">list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</a></code></p>
+ <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, x__xgafv=None)</code>
+ <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.
-
-Authorization: requires `Editor` role on the parent project. (required)
- body: object, The request body. (required)
+ 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.
@@ -129,14 +139,474 @@
<pre>Creates a training or a batch prediction job.
Args:
- parent: string, Required. The project name.
-
-Authorization: requires `Editor` role on the specified project. (required)
+ 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
@@ -146,35 +616,142 @@
"hyperparameters": { # The hyperparameters given to this trial.
"a_key": "A String",
},
- "trialId": "A String", # The trial id for these results.
- "allMetrics": [ # All recorded object metrics for this trial.
+ "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.
},
],
- "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.
+ "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.
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
- # Only set for hyperparameter tuning jobs.
+ "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.
+ },
},
- "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
+ "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
+ "modelName": "A String", # Use this field if you want to use the default version for the specified
+ # model. The string must use the following format:
+ #
+ # `"projects/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.
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
- # set, Google Cloud ML will choose the latest stable version.
+ "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
@@ -203,42 +780,120 @@
# <dt>complex_model_m</dt>
# <dd>
# A machine with roughly twice the number of cores and roughly double the
- # memory of <code suppresswarning="true">complex_model_s</code>.
+ # 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 <code suppresswarning="true">complex_model_m</code>.
+ # memory of <i>complex_model_m</i>.
# </dd>
# <dt>standard_gpu</dt>
# <dd>
- # A machine equivalent to <code suppresswarning="true">standard</code> that
- # also includes a
- # <a href="/ml-engine/docs/how-tos/using-gpus">
- # GPU that you can use in your trainer</a>.
+ # 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
- # <code suppresswarning="true">complex_model_m</code> that also includes
- # four GPUs.
+ # 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.
- "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.
+ "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 typeis `DOUBLE` or `INTEGER`. This field
+ "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.
@@ -263,10 +918,11 @@
# parameters (e.g., `UNIT_LINEAR_SCALE`).
},
],
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
+ "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
@@ -279,13 +935,19 @@
# 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
+ # 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.
@@ -297,32 +959,198 @@
#
# 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/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
- # prediction. If not set, Google Cloud ML will pick the runtime version used
+ # `"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.
- "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
+ "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
@@ -333,15 +1161,353 @@
# string is formatted the same way as `model_version`, with the addition
# of the version information:
#
- # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
- "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
- # May contain wildcards.
+ # `"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.
},
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"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.
@@ -351,243 +1517,18 @@
"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.
- "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
- "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",
- },
- "trialId": "A String", # The trial id for these results.
- "allMetrics": [ # All recorded object metrics for this trial.
- { # 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.
- },
- ],
- "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.
- },
- },
- ],
- "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
- # Only set for hyperparameter tuning jobs.
- },
- "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
- "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # job's worker nodes.
- #
- # The supported values are the same as those described in the entry for
- # `masterType`.
- #
- # This value must be present when `scaleTier` is set to `CUSTOM` and
- # `workerCount` is greater than zero.
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
- # set, Google Cloud ML will choose the latest stable version.
- "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
- # and parameter servers.
- "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # 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 <code suppresswarning="true">complex_model_s</code>.
- # </dd>
- # <dt>complex_model_l</dt>
- # <dd>
- # A machine with roughly twice the number of cores and roughly double the
- # memory of <code suppresswarning="true">complex_model_m</code>.
- # </dd>
- # <dt>standard_gpu</dt>
- # <dd>
- # A machine equivalent to <code suppresswarning="true">standard</code> that
- # also includes a
- # <a href="/ml-engine/docs/how-tos/using-gpus">
- # GPU that you can use in your trainer</a>.
- # </dd>
- # <dt>complex_model_m_gpu</dt>
- # <dd>
- # A machine equivalent to
- # <code suppresswarning="true">complex_model_m</code> that also includes
- # four GPUs.
- # </dd>
- # </dl>
- #
- # 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.
- "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
- # current versions of Tensorflow, this tag name should exactly match what is
- # shown in Tensorboard, including all scopes. For versions of Tensorflow
- # prior to 0.12, this should be only the tag passed to tf.Summary.
- # By default, "training/hptuning/metric" will be used.
- "params": [ # Required. The set of parameters to tune.
- { # Represents a single hyperparameter to optimize.
- "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is `INTEGER`.
- "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`).
- },
- ],
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
- "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
- # You can reduce the time it takes to perform hyperparameter tuning by adding
- # trials in parallel. However, each trail only benefits from the information
- # 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.
- "args": [ # Optional. Command line arguments to pass to the program.
- "A String",
- ],
- "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
- "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
- # and other data needed for training. This path is passed to your TensorFlow
- # program as the 'job_dir' command-line argument. The benefit of specifying
- # this field is that Cloud ML validates the path for use in training.
- "packageUris": [ # Required. The Google Cloud Storage location of the packages with
- # the training program and any additional dependencies.
- # 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`.
- "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 present when `scaleTier` is set to `CUSTOM` and
- # `parameter_server_count` is greater than zero.
- "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`.
- },
- "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
- "modelName": "A String", # Use this field if you want to use the default version for the specified
- # model. The string must use the following format:
- #
- # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
- # prediction. If not set, Google Cloud ML will pick the runtime version used
- # during the CreateVersion request for this model version, or choose the
- # latest stable version when model version information is not available
- # such as when the model is specified by uri.
- "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
- "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/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
- "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
- # May contain wildcards.
- "A String",
- ],
- },
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
- "startTime": "A String", # Output only. When the job processing was started.
- "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>
+ }</pre>
</div>
<div class="method">
- <code class="details" id="get">get(name, x__xgafv=None)</code>
- <pre>Describes a job.
+ <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:
- name: string, Required. The name of the job to get the description of.
-
-Authorization: requires `Viewer` role on the parent project. (required)
+ 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
@@ -596,239 +1537,212 @@
Returns:
An object of the form:
- { # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
- "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",
- },
- "trialId": "A String", # The trial id for these results.
- "allMetrics": [ # All recorded object metrics for this trial.
- { # 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.
- },
+ { # 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:mike@example.com",
+ # "group:admins@example.com",
+ # "domain:google.com",
+ # "serviceAccount:my-other-app@appspot.gserviceaccount.com"
+ # ]
+ # },
+ # {
+ # "role": "roles/viewer",
+ # "members": ["user:sean@example.com"]
+ # }
+ # ]
+ # }
+ #
+ # **YAML Example**
+ #
+ # bindings:
+ # - members:
+ # - user:mike@example.com
+ # - group:admins@example.com
+ # - domain:google.com
+ # - serviceAccount:my-other-app@appspot.gserviceaccount.com
+ # role: roles/owner
+ # - members:
+ # - user:sean@example.com
+ # 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, `alice@gmail.com` .
+ #
+ #
+ # * `serviceAccount:{emailid}`: An email address that represents a service
+ # account. For example, `my-other-app@appspot.gserviceaccount.com`.
+ #
+ # * `group:{emailid}`: An email address that represents a Google group.
+ # For example, `admins@example.com`.
+ #
+ #
+ # * `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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "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:bar@gmail.com"
+ # ]
+ # }
+ # ]
+ # }
+ # ]
+ # }
+ #
+ # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
+ # logging. It also exempts foo@gmail.com from DATA_READ logging, and
+ # bar@gmail.com 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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "log_type": "DATA_WRITE",
+ # }
+ # ]
+ # }
+ #
+ # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
+ # foo@gmail.com 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",
],
- "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.
- },
+ "logType": "A String", # The log type that this config enables.
},
],
- "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
- # Only set for hyperparameter tuning jobs.
+ "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.
},
- "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
- "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # job's worker nodes.
- #
- # The supported values are the same as those described in the entry for
- # `masterType`.
- #
- # This value must be present when `scaleTier` is set to `CUSTOM` and
- # `workerCount` is greater than zero.
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
- # set, Google Cloud ML will choose the latest stable version.
- "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
- # and parameter servers.
- "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # 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 <code suppresswarning="true">complex_model_s</code>.
- # </dd>
- # <dt>complex_model_l</dt>
- # <dd>
- # A machine with roughly twice the number of cores and roughly double the
- # memory of <code suppresswarning="true">complex_model_m</code>.
- # </dd>
- # <dt>standard_gpu</dt>
- # <dd>
- # A machine equivalent to <code suppresswarning="true">standard</code> that
- # also includes a
- # <a href="/ml-engine/docs/how-tos/using-gpus">
- # GPU that you can use in your trainer</a>.
- # </dd>
- # <dt>complex_model_m_gpu</dt>
- # <dd>
- # A machine equivalent to
- # <code suppresswarning="true">complex_model_m</code> that also includes
- # four GPUs.
- # </dd>
- # </dl>
- #
- # 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.
- "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
- # current versions of Tensorflow, this tag name should exactly match what is
- # shown in Tensorboard, including all scopes. For versions of Tensorflow
- # prior to 0.12, this should be only the tag passed to tf.Summary.
- # By default, "training/hptuning/metric" will be used.
- "params": [ # Required. The set of parameters to tune.
- { # Represents a single hyperparameter to optimize.
- "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is `INTEGER`.
- "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`).
- },
- ],
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
- "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
- # You can reduce the time it takes to perform hyperparameter tuning by adding
- # trials in parallel. However, each trail only benefits from the information
- # 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.
- "args": [ # Optional. Command line arguments to pass to the program.
- "A String",
- ],
- "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
- "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
- # and other data needed for training. This path is passed to your TensorFlow
- # program as the 'job_dir' command-line argument. The benefit of specifying
- # this field is that Cloud ML validates the path for use in training.
- "packageUris": [ # Required. The Google Cloud Storage location of the packages with
- # the training program and any additional dependencies.
- # 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`.
- "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 present when `scaleTier` is set to `CUSTOM` and
- # `parameter_server_count` is greater than zero.
- "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`.
- },
- "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
- "modelName": "A String", # Use this field if you want to use the default version for the specified
- # model. The string must use the following format:
- #
- # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
- # prediction. If not set, Google Cloud ML will pick the runtime version used
- # during the CreateVersion request for this model version, or choose the
- # latest stable version when model version information is not available
- # such as when the model is specified by uri.
- "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
- "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/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
- "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
- # May contain wildcards.
- "A String",
- ],
- },
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
- "startTime": "A String", # Output only. When the job processing was started.
- "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>
+ ],
+ }</pre>
</div>
<div class="method">
- <code class="details" id="list">list(parent, pageSize=None, filter=None, pageToken=None, x__xgafv=None)</code>
+ <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.
-
-Authorization: requires `Viewer` role on the specified project. (required)
- 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.
+ 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
@@ -837,6 +1751,20 @@
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:
@@ -846,222 +1774,455 @@
# subsequent call.
"jobs": [ # The list of jobs.
{ # Represents a training or prediction job.
- "trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
- "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",
- },
- "trialId": "A String", # The trial id for these results.
- "allMetrics": [ # All recorded object metrics for this trial.
- { # 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.
- },
- ],
- "finalMetric": { # An observed value of a metric. # The final objective metric seen for this trial.
+ "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`).
},
],
- "isHyperparameterTuningJob": True or False, # Whether this job is a hyperparameter tuning job.
- "consumedMLUnits": 3.14, # The amount of ML units consumed by the job.
- "completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
- # Only set for hyperparameter tuning jobs.
+ "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.
},
- "trainingInput": { # Represents input parameters for a training job. # Input parameters to create a training job.
- "workerType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # job's worker nodes.
- #
- # The supported values are the same as those described in the entry for
- # `masterType`.
- #
- # This value must be present when `scaleTier` is set to `CUSTOM` and
- # `workerCount` is greater than zero.
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for training. If not
- # set, Google Cloud ML will choose the latest stable version.
- "scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
- # and parameter servers.
- "masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
- # 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 <code suppresswarning="true">complex_model_s</code>.
- # </dd>
- # <dt>complex_model_l</dt>
- # <dd>
- # A machine with roughly twice the number of cores and roughly double the
- # memory of <code suppresswarning="true">complex_model_m</code>.
- # </dd>
- # <dt>standard_gpu</dt>
- # <dd>
- # A machine equivalent to <code suppresswarning="true">standard</code> that
- # also includes a
- # <a href="/ml-engine/docs/how-tos/using-gpus">
- # GPU that you can use in your trainer</a>.
- # </dd>
- # <dt>complex_model_m_gpu</dt>
- # <dd>
- # A machine equivalent to
- # <code suppresswarning="true">complex_model_m</code> that also includes
- # four GPUs.
- # </dd>
- # </dl>
- #
- # 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.
- "hyperparameterMetricTag": "A String", # Optional. The Tensorflow summary tag name to use for optimizing trials. For
- # current versions of Tensorflow, this tag name should exactly match what is
- # shown in Tensorboard, including all scopes. For versions of Tensorflow
- # prior to 0.12, this should be only the tag passed to tf.Summary.
- # By default, "training/hptuning/metric" will be used.
- "params": [ # Required. The set of parameters to tune.
- { # Represents a single hyperparameter to optimize.
- "maxValue": 3.14, # Required if typeis `DOUBLE` or `INTEGER`. This field
- # should be unset if type is `CATEGORICAL`. This value should be integers if
- # type is `INTEGER`.
- "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`).
- },
- ],
- "goal": "A String", # Required. The type of goal to use for tuning. Available types are
- # `MAXIMIZE` and `MINIMIZE`.
- #
- # Defaults to `MAXIMIZE`.
- "maxParallelTrials": 42, # Optional. The number of training trials to run concurrently.
- # You can reduce the time it takes to perform hyperparameter tuning by adding
- # trials in parallel. However, each trail only benefits from the information
- # 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.
},
- "region": "A String", # Required. The Google Compute Engine region to run the training job in.
- "args": [ # Optional. Command line arguments to pass to the program.
- "A String",
- ],
- "pythonModule": "A String", # Required. The Python module name to run after installing the packages.
- "jobDir": "A String", # Optional. A Google Cloud Storage path in which to store training outputs
- # and other data needed for training. This path is passed to your TensorFlow
- # program as the 'job_dir' command-line argument. The benefit of specifying
- # this field is that Cloud ML validates the path for use in training.
- "packageUris": [ # Required. The Google Cloud Storage location of the packages with
- # the training program and any additional dependencies.
- # 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`.
- "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 present when `scaleTier` is set to `CUSTOM` and
- # `parameter_server_count` is greater than zero.
- "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`.
+ "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).
},
- "predictionInput": { # Represents input parameters for a prediction job. # Input parameters to create a prediction job.
- "modelName": "A String", # Use this field if you want to use the default version for the specified
- # model. The string must use the following format:
- #
- # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
- "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this batch
- # prediction. If not set, Google Cloud ML will pick the runtime version used
- # during the CreateVersion request for this model version, or choose the
- # latest stable version when model version information is not available
- # such as when the model is specified by uri.
- "region": "A String", # Required. The Google Compute Engine region to run the prediction job in.
- "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/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
- "inputPaths": [ # Required. The Google Cloud Storage location of the input data files.
- # May contain wildcards.
- "A String",
- ],
+ "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).
},
- "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
- "jobId": "A String", # Required. The user-specified id of the job.
- "state": "A String", # Output only. The detailed state of a job.
- "startTime": "A String", # Output only. When the job processing was started.
- "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.
+ "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>
@@ -1080,4 +2241,1408 @@
</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:mike@example.com",
+ # "group:admins@example.com",
+ # "domain:google.com",
+ # "serviceAccount:my-other-app@appspot.gserviceaccount.com"
+ # ]
+ # },
+ # {
+ # "role": "roles/viewer",
+ # "members": ["user:sean@example.com"]
+ # }
+ # ]
+ # }
+ #
+ # **YAML Example**
+ #
+ # bindings:
+ # - members:
+ # - user:mike@example.com
+ # - group:admins@example.com
+ # - domain:google.com
+ # - serviceAccount:my-other-app@appspot.gserviceaccount.com
+ # role: roles/owner
+ # - members:
+ # - user:sean@example.com
+ # 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, `alice@gmail.com` .
+ #
+ #
+ # * `serviceAccount:{emailid}`: An email address that represents a service
+ # account. For example, `my-other-app@appspot.gserviceaccount.com`.
+ #
+ # * `group:{emailid}`: An email address that represents a Google group.
+ # For example, `admins@example.com`.
+ #
+ #
+ # * `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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "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:bar@gmail.com"
+ # ]
+ # }
+ # ]
+ # }
+ # ]
+ # }
+ #
+ # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
+ # logging. It also exempts foo@gmail.com from DATA_READ logging, and
+ # bar@gmail.com 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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "log_type": "DATA_WRITE",
+ # }
+ # ]
+ # }
+ #
+ # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
+ # foo@gmail.com 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:mike@example.com",
+ # "group:admins@example.com",
+ # "domain:google.com",
+ # "serviceAccount:my-other-app@appspot.gserviceaccount.com"
+ # ]
+ # },
+ # {
+ # "role": "roles/viewer",
+ # "members": ["user:sean@example.com"]
+ # }
+ # ]
+ # }
+ #
+ # **YAML Example**
+ #
+ # bindings:
+ # - members:
+ # - user:mike@example.com
+ # - group:admins@example.com
+ # - domain:google.com
+ # - serviceAccount:my-other-app@appspot.gserviceaccount.com
+ # role: roles/owner
+ # - members:
+ # - user:sean@example.com
+ # 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, `alice@gmail.com` .
+ #
+ #
+ # * `serviceAccount:{emailid}`: An email address that represents a service
+ # account. For example, `my-other-app@appspot.gserviceaccount.com`.
+ #
+ # * `group:{emailid}`: An email address that represents a Google group.
+ # For example, `admins@example.com`.
+ #
+ #
+ # * `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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "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:bar@gmail.com"
+ # ]
+ # }
+ # ]
+ # }
+ # ]
+ # }
+ #
+ # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
+ # logging. It also exempts foo@gmail.com from DATA_READ logging, and
+ # bar@gmail.com 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:foo@gmail.com"
+ # ]
+ # },
+ # {
+ # "log_type": "DATA_WRITE",
+ # }
+ # ]
+ # }
+ #
+ # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
+ # foo@gmail.com 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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