cancel(name=*, body=None, x__xgafv=None)
Cancels a running job.
create(parent=*, body=None, x__xgafv=None)
Creates a training or a batch prediction job.
Describes a job.
getIamPolicy(resource=None, options_requestedPolicyVersion=None, x__xgafv=None)
Gets the access control policy for a resource.
list(parent=*, pageSize=None, pageToken=None, x__xgafv=None, filter=None)
Lists the jobs in the project.
list_next(previous_request=*, previous_response=*)
Retrieves the next page of results.
patch(name=*, body=None, updateMask=None, x__xgafv=None)
Updates a specific job resource.
setIamPolicy(resource=None, body=None, x__xgafv=None)
Sets the access control policy on the specified resource. Replaces any
testIamPermissions(resource=None, body=None, x__xgafv=None)
Returns permissions that a caller has on the specified resource.
cancel(name=*, body=None, x__xgafv=None)
Cancels a running job.
Args:
name: string, Required. The name of the job to cancel. (required)
body: object, The request body.
The object takes the form of:
{ # Request message for the CancelJob method.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A generic empty message that you can re-use to avoid defining duplicated
# empty messages in your APIs. A typical example is to use it as the request
# or the response type of an API method. For instance:
#
# service Foo {
# rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
# }
#
# The JSON representation for `Empty` is empty JSON object `{}`.
}
create(parent=*, body=None, x__xgafv=None)
Creates a training or a batch prediction job.
Args:
parent: string, Required. The project name. (required)
body: object, The request body.
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.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
},
"jobId": "A String", # Required. The user-specified id of the job.
"labels": { # Optional. One or more labels that you can add, to organize your jobs.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply.
# For more information, see the documentation on
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
"a_key": "A String",
},
"state": "A String", # Output only. The detailed state of a job.
"etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
# prevent simultaneous updates of a job from overwriting each other.
# It is strongly suggested that systems make use of the `etag` in the
# read-modify-write cycle to perform job updates in order to avoid race
# conditions: An `etag` is returned in the response to `GetJob`, and
# systems are expected to put that etag in the request to `UpdateJob` to
# ensure that their change will be applied to the same version of the job.
"startTime": "A String", # Output only. When the job processing was started.
"endTime": "A String", # Output only. When the job processing was completed.
"predictionOutput": { # Represents results of a prediction job. # The current prediction job result.
"outputPath": "A String", # The output Google Cloud Storage location provided at the job creation time.
"nodeHours": 3.14, # Node hours used by the batch prediction job.
"predictionCount": "A String", # The number of generated predictions.
"errorCount": "A String", # The number of data instances which resulted in errors.
},
"createTime": "A String", # Output only. When the job was created.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than 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.
}
get(name=*, x__xgafv=None)
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.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than 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.
}
getIamPolicy(resource=None, options_requestedPolicyVersion=None, x__xgafv=None)
Gets the access control policy for a resource.
Returns an empty policy if the resource exists and does not have a policy
set.
Args:
resource: string, REQUIRED: The resource for which the policy is being requested.
See the operation documentation for the appropriate value for this field. (required)
options_requestedPolicyVersion: integer, Optional. The policy format version to be returned.
Valid values are 0, 1, and 3. Requests specifying an invalid value will be
rejected.
Requests for policies with any conditional bindings must specify version 3.
Policies without any conditional bindings may specify any valid value or
leave the field unset.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # An Identity and Access Management (IAM) policy, which specifies access
# controls for Google Cloud resources.
#
#
# A `Policy` is a collection of `bindings`. A `binding` binds one or more
# `members` to a single `role`. Members can be user accounts, service accounts,
# Google groups, and domains (such as G Suite). A `role` is a named list of
# permissions; each `role` can be an IAM predefined role or a user-created
# custom role.
#
# Optionally, a `binding` can specify a `condition`, which is a logical
# expression that allows access to a resource only if the expression evaluates
# to `true`. A condition can add constraints based on attributes of the
# request, the resource, or both.
#
# **JSON example:**
#
# {
# "bindings": [
# {
# "role": "roles/resourcemanager.organizationAdmin",
# "members": [
# "user:mike@example.com",
# "group:admins@example.com",
# "domain:google.com",
# "serviceAccount:my-project-id@appspot.gserviceaccount.com"
# ]
# },
# {
# "role": "roles/resourcemanager.organizationViewer",
# "members": ["user:eve@example.com"],
# "condition": {
# "title": "expirable access",
# "description": "Does not grant access after Sep 2020",
# "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')",
# }
# }
# ],
# "etag": "BwWWja0YfJA=",
# "version": 3
# }
#
# **YAML example:**
#
# bindings:
# - members:
# - user:mike@example.com
# - group:admins@example.com
# - domain:google.com
# - serviceAccount:my-project-id@appspot.gserviceaccount.com
# role: roles/resourcemanager.organizationAdmin
# - members:
# - user:eve@example.com
# role: roles/resourcemanager.organizationViewer
# condition:
# title: expirable access
# description: Does not grant access after Sep 2020
# expression: request.time < timestamp('2020-10-01T00:00:00.000Z')
# - etag: BwWWja0YfJA=
# - version: 3
#
# For a description of IAM and its features, see the
# [IAM documentation](https://cloud.google.com/iam/docs/).
"bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a
# `condition` that determines how and when the `bindings` are applied. Each
# of the `bindings` must contain at least one member.
{ # 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@example.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`.
#
# * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a user that has been recently deleted. For
# example, `alice@example.com?uid=123456789012345678901`. If the user is
# recovered, this value reverts to `user:{emailid}` and the recovered user
# retains the role in the binding.
#
# * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus
# unique identifier) representing a service account that has been recently
# deleted. For example,
# `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`.
# If the service account is undeleted, this value reverts to
# `serviceAccount:{emailid}` and the undeleted service account retains the
# role in the binding.
#
# * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a Google group that has been recently
# deleted. For example, `admins@example.com?uid=123456789012345678901`. If
# the group is recovered, this value reverts to `group:{emailid}` and the
# recovered group retains the role in the binding.
#
#
# * `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 a textual expression in the Common Expression Language (CEL) # 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.
# syntax. CEL is a C-like expression language. The syntax and semantics of CEL
# are documented at https://github.com/google/cel-spec.
#
# Example (Comparison):
#
# title: "Summary size limit"
# description: "Determines if a summary is less than 100 chars"
# expression: "document.summary.size() < 100"
#
# Example (Equality):
#
# title: "Requestor is owner"
# description: "Determines if requestor is the document owner"
# expression: "document.owner == request.auth.claims.email"
#
# Example (Logic):
#
# title: "Public documents"
# description: "Determine whether the document should be publicly visible"
# expression: "document.type != 'private' && document.type != 'internal'"
#
# Example (Data Manipulation):
#
# title: "Notification string"
# description: "Create a notification string with a timestamp."
# expression: "'New message received at ' + string(document.create_time)"
#
# The exact variables and functions that may be referenced within an expression
# are determined by the service that evaluates it. See the service
# documentation for additional information.
"description": "A String", # 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.
"location": "A String", # 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", # 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.
},
},
],
"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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "sampleservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:aliya@example.com"
# ]
# }
# ]
# }
# ]
# }
#
# For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts jose@example.com from DATA_READ logging, and
# aliya@example.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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# jose@example.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.
},
],
"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.
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
"version": 42, # Specifies the format of the policy.
#
# Valid values are `0`, `1`, and `3`. Requests that specify an invalid value
# are rejected.
#
# Any operation that affects conditional role bindings must specify version
# `3`. This requirement applies to the following operations:
#
# * Getting a policy that includes a conditional role binding
# * Adding a conditional role binding to a policy
# * Changing a conditional role binding in a policy
# * Removing any role binding, with or without a condition, from a policy
# that includes conditions
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
#
# If a policy does not include any conditions, operations on that policy may
# specify any valid version or leave the field unset.
}
list(parent=*, pageSize=None, pageToken=None, x__xgafv=None, filter=None)
Lists the jobs in the project.
If there are no jobs that match the request parameters, the list
request returns an empty response body: {}.
Args:
parent: string, Required. The name of the project for which to list jobs. (required)
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.
pageToken: string, Optional. A page token to request the next page of results.
You get the token from the `next_page_token` field of the response from
the previous call.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
filter: string, Optional. Specifies the subset of jobs to retrieve.
You can filter on the value of one or more attributes of the job object.
For example, retrieve jobs with a job identifier that starts with 'census':
<p><code>gcloud ai-platform jobs list --filter='jobId:census*'</code>
<p>List all failed jobs with names that start with 'rnn':
<p><code>gcloud ai-platform jobs list --filter='jobId:rnn*
AND state:FAILED'</code>
<p>For more examples, see the guide to
<a href="/ml-engine/docs/tensorflow/monitor-training">monitoring jobs</a>.
Returns:
An object of the form:
{ # Response message for the ListJobs method.
"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
# subsequent call.
"jobs": [ # The list of jobs.
{ # Represents a training or prediction job.
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
"trainingOutput": { # Represents results of a training job. Output only. # The current training job result.
"completedTrialCount": "A String", # The number of hyperparameter tuning trials that completed successfully.
# Only set for hyperparameter tuning jobs.
"trials": [ # Results for individual Hyperparameter trials.
# Only set for hyperparameter tuning jobs.
{ # Represents the result of a single hyperparameter tuning trial from a
# training job. The TrainingOutput object that is returned on successful
# completion of a training job with hyperparameter tuning includes a list
# of HyperparameterOutput objects, one for each successful trial.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than 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.
},
],
}
list_next(previous_request=*, previous_response=*)
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
patch(name=*, body=None, updateMask=None, x__xgafv=None)
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.
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.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than 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.
"startTime": "A String", # Output only. Start time for the 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.
},
"state": "A String", # Output only. The detailed state of the trial.
"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.
"endTime": "A String", # Output only. End time for the trial.
"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"`
"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".
"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.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
"maxWorkerCount": "A String", # Optional. The maximum number of workers to be used for parallel processing.
# Defaults to 10 if not specified.
"inputPaths": [ # Required. The Cloud Storage location of the input data files. May contain
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
"A String",
],
"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"`
"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.
"outputDataFormat": "A String", # Optional. Format of the output data files, defaults to JSON.
},
"trainingInput": { # Represents input parameters for a training job. When using the gcloud command # Input parameters to create a training job.
# 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 [submitting
# a training job](/ai-platform/training/docs/training-jobs).
"masterType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can 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`
#
# Learn more about [using Compute Engine machine
# types](/ml-engine/docs/machine-types#compute-engine-machine-types).
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
# Learn more about [using legacy machine
# types](/ml-engine/docs/machine-types#legacy-machine-types).
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPUs](/ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
"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.
"scheduling": { # All parameters related to scheduling of training jobs. # Optional. Scheduling options for a training job.
"maxRunningTime": "A String", # Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. By default there
# is no limit to the running time.
#
# If the training job is still running after this duration, AI Platform
# Training cancels it.
#
# For example, if you want to ensure your job runs for no more than 2 hours,
# set this field to `7200s` (2 hours * 60 minutes / hour * 60 seconds /
# minute).
#
# If you submit your training job using the `gcloud` tool, you can [provide
# this field in a `config.yaml`
# file](/ai-platform/training/docs/training-jobs#formatting_your_configuration_parameters).
# For example:
#
# ```yaml
# trainingInput:
# ...
# scheduling:
# maxRunningTime: 7200s
# ...
# ```
},
"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.
"evaluatorCount": "A String", # Optional. The number of evaluator replicas to use for the training job.
# Each replica in the cluster will be of the type specified in
# `evaluator_type`.
#
# This value can only be used when `scale_tier` is set to `CUSTOM`. If you
# set this value, you must also set `evaluator_type`.
#
# The default value is zero.
"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 Compute Engine machine
# types or both must be legacy 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.
"scaleTier": "A String", # Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
"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",
],
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"evaluatorConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for evaluators.
#
# You should only set `evaluatorConfig.acceleratorConfig` if
# `evaluatorType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
#
# Set `evaluatorConfig.imageUri` only if you build a custom image for
# your evaluator. If `evaluatorConfig.imageUri` has not been
# set, AI Platform uses the value of `masterConfig.imageUri`. Learn more about [configuring custom
# containers](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"useChiefInTfConfig": True or False, # Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this
# field.](/ai-platform/training/docs/distributed-training-details#chief-versus-master)
#
# This field has no effect for training jobs that don't use a custom
# container.
"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.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for training. You must
# either specify this field or specify `masterConfig.imageUri`.
#
# For more information, see the [runtime version
# list](/ai-platform/training/docs/runtime-version-list) and learn [how to
# manage runtime versions](/ai-platform/training/docs/versioning).
"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`.
"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.
"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".
"categoricalValues": [ # Required if type is `CATEGORICAL`. The list of possible categories.
"A String",
],
"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.
},
"args": [ # Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed
# to the container's <a class="external" target="_blank"
# href="https://docs.docker.com/engine/reference/builder/#entrypoint">
# `ENTRYPOINT`</a> command.
"A String",
],
"pythonModule": "A String", # Required. The Python module name to run after installing the packages.
"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.
"encryptionConfig": { # Represents a custom encryption key configuration that can be applied to # Optional. Options for using customer-managed encryption keys (CMEK) to
# protect resources created by a training job, instead of using Google's
# default encryption. If this is set, then all resources created by the
# training job will be encrypted with the customer-managed encryption key
# that you specify.
#
# [Learn how and when to use CMEK with AI Platform
# Training](/ai-platform/training/docs/cmek).
# a resource.
"kmsKeyName": "A String", # The Cloud KMS resource identifier of the customer-managed encryption key
# used to protect a resource, such as a training job. It has the following
# format:
# `projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}`
},
"parameterServerConfig": { # Represents the configuration for a replica in a cluster. # Optional. The configuration for parameter servers.
#
# You should only set `parameterServerConfig.acceleratorConfig` if
# `parameterServerType` is set to a Compute Engine machine type. [Learn
# about restrictions on accelerator configurations for
# training.](/ai-platform/training/docs/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](/ai-platform/training/docs/distributed-training-containers).
"tpuTfVersion": "A String", # The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica
# is a TPU worker that uses a custom container. Otherwise, do not specify
# this field. This must be a [runtime version that currently supports
# training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support).
#
# Note that the version of TensorFlow included in a runtime version may
# differ from the numbering of the runtime version itself, because it may
# have a different [patch
# version](https://www.tensorflow.org/guide/version_compat#semantic_versioning_20).
# In this field, you must specify the runtime version (TensorFlow minor
# version). For example, if your custom container runs TensorFlow `1.x.y`,
# specify `1.x`.
"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.](/ai-platform/training/docs/using-gpus#compute-engine-machine-types-with-gpu)
# Note that the AcceleratorConfig can be used in both Jobs and Versions.
# Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and
# [accelerators for online
# prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
"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](/ai-platform/training/docs/distributed-training-containers).
},
"region": "A String", # Required. The region to run the training job in. See the [available
# regions](/ai-platform/training/docs/regions) for AI Platform Training.
"pythonVersion": "A String", # Optional. The version of Python used in training. You must either specify
# this field or specify `masterConfig.imageUri`.
#
# The following Python versions are available:
#
# * Python '3.7' is available when `runtime_version` is set to '1.15' or
# later.
# * Python '3.5' is available when `runtime_version` is set to a version
# from '1.4' to '1.14'.
# * Python '2.7' is available when `runtime_version` is set to '1.15' or
# earlier.
#
# Read more about the Python versions available for [each runtime
# version](/ml-engine/docs/runtime-version-list).
"evaluatorType": "A String", # Optional. Specifies the type of virtual machine to use for your training
# job's evaluator 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `evaluatorCount` is greater than 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 Compute Engine machine
# types or both must be legacy machine types.
#
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than 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.
}
setIamPolicy(resource=None, body=None, x__xgafv=None)
Sets the access control policy on the specified resource. Replaces any
existing policy.
Can return Public Errors: NOT_FOUND, INVALID_ARGUMENT and PERMISSION_DENIED
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.
The object takes the form of:
{ # Request message for `SetIamPolicy` method.
"policy": { # An Identity and Access Management (IAM) policy, which specifies access # 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.
# controls for Google Cloud resources.
#
#
# A `Policy` is a collection of `bindings`. A `binding` binds one or more
# `members` to a single `role`. Members can be user accounts, service accounts,
# Google groups, and domains (such as G Suite). A `role` is a named list of
# permissions; each `role` can be an IAM predefined role or a user-created
# custom role.
#
# Optionally, a `binding` can specify a `condition`, which is a logical
# expression that allows access to a resource only if the expression evaluates
# to `true`. A condition can add constraints based on attributes of the
# request, the resource, or both.
#
# **JSON example:**
#
# {
# "bindings": [
# {
# "role": "roles/resourcemanager.organizationAdmin",
# "members": [
# "user:mike@example.com",
# "group:admins@example.com",
# "domain:google.com",
# "serviceAccount:my-project-id@appspot.gserviceaccount.com"
# ]
# },
# {
# "role": "roles/resourcemanager.organizationViewer",
# "members": ["user:eve@example.com"],
# "condition": {
# "title": "expirable access",
# "description": "Does not grant access after Sep 2020",
# "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')",
# }
# }
# ],
# "etag": "BwWWja0YfJA=",
# "version": 3
# }
#
# **YAML example:**
#
# bindings:
# - members:
# - user:mike@example.com
# - group:admins@example.com
# - domain:google.com
# - serviceAccount:my-project-id@appspot.gserviceaccount.com
# role: roles/resourcemanager.organizationAdmin
# - members:
# - user:eve@example.com
# role: roles/resourcemanager.organizationViewer
# condition:
# title: expirable access
# description: Does not grant access after Sep 2020
# expression: request.time < timestamp('2020-10-01T00:00:00.000Z')
# - etag: BwWWja0YfJA=
# - version: 3
#
# For a description of IAM and its features, see the
# [IAM documentation](https://cloud.google.com/iam/docs/).
"bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a
# `condition` that determines how and when the `bindings` are applied. Each
# of the `bindings` must contain at least one member.
{ # 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@example.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`.
#
# * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a user that has been recently deleted. For
# example, `alice@example.com?uid=123456789012345678901`. If the user is
# recovered, this value reverts to `user:{emailid}` and the recovered user
# retains the role in the binding.
#
# * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus
# unique identifier) representing a service account that has been recently
# deleted. For example,
# `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`.
# If the service account is undeleted, this value reverts to
# `serviceAccount:{emailid}` and the undeleted service account retains the
# role in the binding.
#
# * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a Google group that has been recently
# deleted. For example, `admins@example.com?uid=123456789012345678901`. If
# the group is recovered, this value reverts to `group:{emailid}` and the
# recovered group retains the role in the binding.
#
#
# * `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 a textual expression in the Common Expression Language (CEL) # 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.
# syntax. CEL is a C-like expression language. The syntax and semantics of CEL
# are documented at https://github.com/google/cel-spec.
#
# Example (Comparison):
#
# title: "Summary size limit"
# description: "Determines if a summary is less than 100 chars"
# expression: "document.summary.size() < 100"
#
# Example (Equality):
#
# title: "Requestor is owner"
# description: "Determines if requestor is the document owner"
# expression: "document.owner == request.auth.claims.email"
#
# Example (Logic):
#
# title: "Public documents"
# description: "Determine whether the document should be publicly visible"
# expression: "document.type != 'private' && document.type != 'internal'"
#
# Example (Data Manipulation):
#
# title: "Notification string"
# description: "Create a notification string with a timestamp."
# expression: "'New message received at ' + string(document.create_time)"
#
# The exact variables and functions that may be referenced within an expression
# are determined by the service that evaluates it. See the service
# documentation for additional information.
"description": "A String", # 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.
"location": "A String", # 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", # 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.
},
},
],
"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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "sampleservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:aliya@example.com"
# ]
# }
# ]
# }
# ]
# }
#
# For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts jose@example.com from DATA_READ logging, and
# aliya@example.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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# jose@example.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.
},
],
"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.
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
"version": 42, # Specifies the format of the policy.
#
# Valid values are `0`, `1`, and `3`. Requests that specify an invalid value
# are rejected.
#
# Any operation that affects conditional role bindings must specify version
# `3`. This requirement applies to the following operations:
#
# * Getting a policy that includes a conditional role binding
# * Adding a conditional role binding to a policy
# * Changing a conditional role binding in a policy
# * Removing any role binding, with or without a condition, from a policy
# that includes conditions
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
#
# If a policy does not include any conditions, operations on that policy may
# specify any valid version or leave the field unset.
},
"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:
{ # An Identity and Access Management (IAM) policy, which specifies access
# controls for Google Cloud resources.
#
#
# A `Policy` is a collection of `bindings`. A `binding` binds one or more
# `members` to a single `role`. Members can be user accounts, service accounts,
# Google groups, and domains (such as G Suite). A `role` is a named list of
# permissions; each `role` can be an IAM predefined role or a user-created
# custom role.
#
# Optionally, a `binding` can specify a `condition`, which is a logical
# expression that allows access to a resource only if the expression evaluates
# to `true`. A condition can add constraints based on attributes of the
# request, the resource, or both.
#
# **JSON example:**
#
# {
# "bindings": [
# {
# "role": "roles/resourcemanager.organizationAdmin",
# "members": [
# "user:mike@example.com",
# "group:admins@example.com",
# "domain:google.com",
# "serviceAccount:my-project-id@appspot.gserviceaccount.com"
# ]
# },
# {
# "role": "roles/resourcemanager.organizationViewer",
# "members": ["user:eve@example.com"],
# "condition": {
# "title": "expirable access",
# "description": "Does not grant access after Sep 2020",
# "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')",
# }
# }
# ],
# "etag": "BwWWja0YfJA=",
# "version": 3
# }
#
# **YAML example:**
#
# bindings:
# - members:
# - user:mike@example.com
# - group:admins@example.com
# - domain:google.com
# - serviceAccount:my-project-id@appspot.gserviceaccount.com
# role: roles/resourcemanager.organizationAdmin
# - members:
# - user:eve@example.com
# role: roles/resourcemanager.organizationViewer
# condition:
# title: expirable access
# description: Does not grant access after Sep 2020
# expression: request.time < timestamp('2020-10-01T00:00:00.000Z')
# - etag: BwWWja0YfJA=
# - version: 3
#
# For a description of IAM and its features, see the
# [IAM documentation](https://cloud.google.com/iam/docs/).
"bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a
# `condition` that determines how and when the `bindings` are applied. Each
# of the `bindings` must contain at least one member.
{ # 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@example.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`.
#
# * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a user that has been recently deleted. For
# example, `alice@example.com?uid=123456789012345678901`. If the user is
# recovered, this value reverts to `user:{emailid}` and the recovered user
# retains the role in the binding.
#
# * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus
# unique identifier) representing a service account that has been recently
# deleted. For example,
# `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`.
# If the service account is undeleted, this value reverts to
# `serviceAccount:{emailid}` and the undeleted service account retains the
# role in the binding.
#
# * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique
# identifier) representing a Google group that has been recently
# deleted. For example, `admins@example.com?uid=123456789012345678901`. If
# the group is recovered, this value reverts to `group:{emailid}` and the
# recovered group retains the role in the binding.
#
#
# * `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 a textual expression in the Common Expression Language (CEL) # 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.
# syntax. CEL is a C-like expression language. The syntax and semantics of CEL
# are documented at https://github.com/google/cel-spec.
#
# Example (Comparison):
#
# title: "Summary size limit"
# description: "Determines if a summary is less than 100 chars"
# expression: "document.summary.size() < 100"
#
# Example (Equality):
#
# title: "Requestor is owner"
# description: "Determines if requestor is the document owner"
# expression: "document.owner == request.auth.claims.email"
#
# Example (Logic):
#
# title: "Public documents"
# description: "Determine whether the document should be publicly visible"
# expression: "document.type != 'private' && document.type != 'internal'"
#
# Example (Data Manipulation):
#
# title: "Notification string"
# description: "Create a notification string with a timestamp."
# expression: "'New message received at ' + string(document.create_time)"
#
# The exact variables and functions that may be referenced within an expression
# are determined by the service that evaluates it. See the service
# documentation for additional information.
"description": "A String", # 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.
"location": "A String", # 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", # 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.
},
},
],
"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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# },
# {
# "log_type": "ADMIN_READ",
# }
# ]
# },
# {
# "service": "sampleservice.googleapis.com"
# "audit_log_configs": [
# {
# "log_type": "DATA_READ",
# },
# {
# "log_type": "DATA_WRITE",
# "exempted_members": [
# "user:aliya@example.com"
# ]
# }
# ]
# }
# ]
# }
#
# For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
# logging. It also exempts jose@example.com from DATA_READ logging, and
# aliya@example.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:jose@example.com"
# ]
# },
# {
# "log_type": "DATA_WRITE",
# }
# ]
# }
#
# This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
# jose@example.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.
},
],
"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.
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
"version": 42, # Specifies the format of the policy.
#
# Valid values are `0`, `1`, and `3`. Requests that specify an invalid value
# are rejected.
#
# Any operation that affects conditional role bindings must specify version
# `3`. This requirement applies to the following operations:
#
# * Getting a policy that includes a conditional role binding
# * Adding a conditional role binding to a policy
# * Changing a conditional role binding in a policy
# * Removing any role binding, with or without a condition, from a policy
# that includes conditions
#
# **Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows
# you to overwrite a version `3` policy with a version `1` policy, and all of
# the conditions in the version `3` policy are lost.
#
# If a policy does not include any conditions, operations on that policy may
# specify any valid version or leave the field unset.
}
testIamPermissions(resource=None, body=None, x__xgafv=None)
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.
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",
],
}