Types overview

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

GoogleApi__HttpBody

Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.

Fields
contentType	string The HTTP Content-Type header value specifying the content type of the body.
data	string (bytes format) The HTTP request/response body as raw binary.
extensions[]	object Application specific response metadata. Must be set in the first response for streaming APIs.

GoogleCloudMlV1_AutomatedStoppingConfig_DecayCurveAutomatedStoppingConfig

(No description provided)

Fields
useElapsedTime	boolean If true, measurement.elapsed_time is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.steps will be used as the x-axis.

GoogleCloudMlV1_AutomatedStoppingConfig_MedianAutomatedStoppingConfig

The median automated stopping rule stops a pending trial if the trial's best objective_value is strictly below the median 'performance' of all completed trials reported up to the trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the trial in each measurement.

Fields
useElapsedTime	boolean If true, the median automated stopping rule applies to measurement.use_elapsed_time, which means the elapsed_time field of the current trial's latest measurement is used to compute the median objective value for each completed trial.

GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric

An observed value of a metric.

Fields
objectiveValue	number (double format) The objective value at this training step.
trainingStep	string (int64 format) The global training step for this metric.

GoogleCloudMlV1_Measurement_Metric

A message representing a metric in the measurement.

Fields
metric	string Required. Metric name.
value	number (double format) Required. The value for this metric.

GoogleCloudMlV1_StudyConfigParameterSpec_CategoricalValueSpec

(No description provided)

Fields
values[]	string Must be specified if type is CATEGORICAL. The list of possible categories.

GoogleCloudMlV1_StudyConfigParameterSpec_DiscreteValueSpec

(No description provided)

Fields
values[]	number (double format) Must be specified 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.

GoogleCloudMlV1_StudyConfigParameterSpec_DoubleValueSpec

(No description provided)

Fields
maxValue	number (double format) Must be specified if type is DOUBLE. Maximum value of the parameter.
minValue	number (double format) Must be specified if type is DOUBLE. Minimum value of the parameter.

GoogleCloudMlV1_StudyConfigParameterSpec_IntegerValueSpec

(No description provided)

Fields
maxValue	string (int64 format) Must be specified if type is INTEGER. Maximum value of the parameter.
minValue	string (int64 format) Must be specified if type is INTEGER. Minimum value of the parameter.

GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentCategoricalValueSpec

Represents the spec to match categorical values from parent parameter.

Fields
values[]	string Matches values of the parent parameter with type 'CATEGORICAL'. All values must exist in categorical_value_spec of parent parameter.

GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentDiscreteValueSpec

Represents the spec to match discrete values from parent parameter.

Fields
values[]	number (double format) Matches values of the parent parameter with type 'DISCRETE'. All values must exist in discrete_value_spec of parent parameter.

GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentIntValueSpec

Represents the spec to match integer values from parent parameter.

Fields
values[]	string (int64 format) Matches values of the parent parameter with type 'INTEGER'. All values must lie in integer_value_spec of parent parameter.

GoogleCloudMlV1_StudyConfig_MetricSpec

Represents a metric to optimize.

Fields
goal	enum Required. The optimization goal of the metric.
Enum type. Can be one of the following:
GOAL_TYPE_UNSPECIFIED	Goal Type will default to maximize.
MAXIMIZE	Maximize the goal metric.
MINIMIZE	Minimize the goal metric.
metric	string Required. The name of the metric.

GoogleCloudMlV1_StudyConfig_ParameterSpec

Represents a single parameter to optimize.

Fields
categoricalValueSpec	object (GoogleCloudMlV1_StudyConfigParameterSpec_CategoricalValueSpec) The value spec for a 'CATEGORICAL' parameter.
childParameterSpecs[]	object (GoogleCloudMlV1_StudyConfig_ParameterSpec) A child node is active if the parameter's value matches the child node's matching_parent_values. If two items in child_parameter_specs have the same name, they must have disjoint matching_parent_values.
discreteValueSpec	object (GoogleCloudMlV1_StudyConfigParameterSpec_DiscreteValueSpec) The value spec for a 'DISCRETE' parameter.
doubleValueSpec	object (GoogleCloudMlV1_StudyConfigParameterSpec_DoubleValueSpec) The value spec for a 'DOUBLE' parameter.
integerValueSpec	object (GoogleCloudMlV1_StudyConfigParameterSpec_IntegerValueSpec) The value spec for an 'INTEGER' parameter.
parameter	string Required. The parameter name must be unique amongst all ParameterSpecs.
parentCategoricalValues	object (GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentCategoricalValueSpec) (No description provided)
parentDiscreteValues	object (GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentDiscreteValueSpec) (No description provided)
parentIntValues	object (GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentIntValueSpec) (No description provided)
scaleType	enum How the parameter should be scaled. Leave unset for categorical parameters.
Enum type. Can be one of the following:
SCALE_TYPE_UNSPECIFIED	By default, no scaling is applied.
UNIT_LINEAR_SCALE	Scales the feasible space to (0, 1) linearly.
UNIT_LOG_SCALE	Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
UNIT_REVERSE_LOG_SCALE	Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
type	enum Required. The type of the parameter.
Enum type. Can be one of the following:
PARAMETER_TYPE_UNSPECIFIED	You must specify a valid type. Using this unspecified type will result in an error.
DOUBLE	Type for real-valued parameters.
INTEGER	Type for integral parameters.
CATEGORICAL	The parameter is categorical, with a value chosen from the categories field.
DISCRETE	The parameter is real valued, with a fixed set of feasible points. If type==DISCRETE, feasible_points must be provided, and {min_value, max_value} will be ignored.

GoogleCloudMlV1_Trial_Parameter

A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.

Fields
floatValue	number (double format) Must be set if ParameterType is DOUBLE or DISCRETE.
intValue	string (int64 format) Must be set if ParameterType is INTEGER
parameter	string The name of the parameter.
stringValue	string Must be set if ParameterTypeis CATEGORICAL

GoogleCloudMlV1__AcceleratorConfig

Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about accelerators for training and accelerators for online prediction.

Fields
count	string (int64 format) The number of accelerators to attach to each machine running the job.
type	enum The type of accelerator to use.
Enum type. Can be one of the following:
ACCELERATOR_TYPE_UNSPECIFIED	Unspecified accelerator type. Default to no GPU.
NVIDIA_TESLA_K80	Nvidia Tesla K80 GPU.
NVIDIA_TESLA_P100	Nvidia Tesla P100 GPU.
NVIDIA_TESLA_V100	Nvidia V100 GPU.
NVIDIA_TESLA_P4	Nvidia Tesla P4 GPU.
NVIDIA_TESLA_T4	Nvidia T4 GPU.
NVIDIA_TESLA_A100	Nvidia A100 GPU.
TPU_V2	TPU v2.
TPU_V3	TPU v3.
TPU_V2_POD	TPU v2 POD.
TPU_V3_POD	TPU v3 POD.
TPU_V4_POD	TPU v4 POD.

GoogleCloudMlV1__AddTrialMeasurementRequest

The request message for the AddTrialMeasurement service method.

Fields
measurement	object (GoogleCloudMlV1__Measurement) Required. The measurement to be added to a trial.

GoogleCloudMlV1__AutoScaling

Options for automatically scaling a model.

Fields
maxNodes	integer (int32 format) The maximum number of nodes to scale this model under load. The actual value will depend on resource quota and availability.
metrics[]	object (GoogleCloudMlV1__MetricSpec) MetricSpec contains the specifications to use to calculate the desired nodes count.
minNodes	integer (int32 format) Optional. The minimum number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed. Therefore, the cost of operating this model will be at least rate * min_nodes * number of hours since last billing cycle, where rate is the cost per node-hour as documented in the pricing guide, even if no predictions are performed. There is additional cost for each prediction performed. Unlike manual scaling, if the load gets too heavy for the nodes that are up, the service will automatically add nodes to handle the increased load as well as scale back as traffic drops, always maintaining at least min_nodes. You will be charged for the time in which additional nodes are used. If min_nodes is not specified and AutoScaling is used with a legacy (MLS1) machine type, min_nodes defaults to 0, in which case, when traffic to a model stops (and after a cool-down period), nodes will be shut down and no charges will be incurred until traffic to the model resumes. If min_nodes is not specified and AutoScaling is used with a Compute Engine (N1) machine type, min_nodes defaults to 1. min_nodes must be at least 1 for use with a Compute Engine machine type. You can set min_nodes when creating the model version, and you can also update min_nodes for an existing version: update_body.json: { 'autoScaling': { 'minNodes': 5 } } HTTP request: PATCH https://ml.googleapis.com/v1/{name=projects//models//versions/*}?update_mask=autoScaling.minNodes -d @./update_body.json

GoogleCloudMlV1__AutomatedStoppingConfig

Configuration for Automated Early Stopping of Trials. If no implementation_config is set, automated early stopping will not be run.

Fields
decayCurveStoppingConfig	object (GoogleCloudMlV1_AutomatedStoppingConfig_DecayCurveAutomatedStoppingConfig) (No description provided)
medianAutomatedStoppingConfig	object (GoogleCloudMlV1_AutomatedStoppingConfig_MedianAutomatedStoppingConfig) (No description provided)

GoogleCloudMlV1__BuiltInAlgorithmOutput

Represents output related to a built-in algorithm Job.

Fields
framework	string Framework on which the built-in algorithm was trained.
modelPath	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.
pythonVersion	string Python version on which the built-in algorithm was trained.
runtimeVersion	string AI Platform runtime version on which the built-in algorithm was trained.

GoogleCloudMlV1__Capability

(No description provided)

Fields
availableAccelerators[]	string Available accelerators for the capability.
type	enum (No description provided)
Enum type. Can be one of the following:
TYPE_UNSPECIFIED	(No description provided)
TRAINING	(No description provided)
BATCH_PREDICTION	(No description provided)
ONLINE_PREDICTION	(No description provided)

GoogleCloudMlV1__CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Fields
createTime	string (Timestamp format) The time at which the operation was submitted.
study	string The name of the study that the trial belongs to.
trial	string The trial name.

GoogleCloudMlV1__CheckTrialEarlyStoppingStateResponse

The message will be placed in the response field of a completed google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Fields
endTime	string (Timestamp format) The time at which operation processing completed.
shouldStop	boolean True if the Trial should stop.
startTime	string (Timestamp format) The time at which the operation was started.

GoogleCloudMlV1__CompleteTrialRequest

The request message for the CompleteTrial service method.

Fields
finalMeasurement	object (GoogleCloudMlV1__Measurement) Optional. If provided, it will be used as the completed trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement
infeasibleReason	string Optional. A human readable reason why the trial was infeasible. This should only be provided if trial_infeasible is true.
trialInfeasible	boolean Optional. True if the trial cannot be run with the given Parameter, and final_measurement will be ignored.

GoogleCloudMlV1__Config

(No description provided)

Fields
tpuServiceAccount	string The service account Cloud ML uses to run on TPU node.

GoogleCloudMlV1__ContainerPort

Represents a network port in a single container. This message is a subset of the Kubernetes ContainerPort v1 core specification.

Fields
containerPort	integer (int32 format) Number of the port to expose on the container. This must be a valid port number: 0 < PORT_NUMBER < 65536.

GoogleCloudMlV1__ContainerSpec

Specification of a custom container for serving predictions. This message is a subset of the Kubernetes Container v1 core specification.

Fields
args[]	string Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the commmand field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by AI Platform Prediction and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command[]	string Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by AI Platform Prediction and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
env[]	object (GoogleCloudMlV1__EnvVar) Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
image	string URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry and begin with the hostname {REGION}-docker.pkg.dev, where {REGION} is replaced by the region that matches AI Platform Prediction regional endpoint that you are using. For example, if you are using the us-central1-ml.googleapis.com endpoint, then this URI must begin with us-central1-docker.pkg.dev. To use a custom container, the AI Platform Google-managed service account must have permission to pull (read) the Docker image at this URI. The AI Platform Google-managed service account has the following format: service-{PROJECT_NUMBER}@cloud-ml.google.com.iam.gserviceaccount.com {PROJECT_NUMBER} is replaced by your Google Cloud project number. By default, this service account has necessary permissions to pull an Artifact Registry image in the same Google Cloud project where you are using AI Platform Prediction. In this case, no configuration is necessary. If you want to use an image from a different Google Cloud project, learn how to grant the Artifact Registry Reader (roles/artifactregistry.reader) role for a repository to your projet's AI Platform Google-managed service account. To learn about the requirements for the Docker image itself, read Custom container requirements.
ports[]	object (GoogleCloudMlV1__ContainerPort) Immutable. List of ports to expose from the container. AI Platform Prediction sends any prediction requests that it receives to the first port on this list. AI Platform Prediction also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] AI Platform Prediction does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.

GoogleCloudMlV1__DiskConfig

Represents the config of disk options.

Fields
bootDiskSizeGb	integer (int32 format) Size in GB of the boot disk (default is 100GB).
bootDiskType	string Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

GoogleCloudMlV1__EncryptionConfig

Represents a custom encryption key configuration that can be applied to a resource.

Fields
kmsKeyName	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}

GoogleCloudMlV1__EnvVar

Represents an environment variable to be made available in a container. This message is a subset of the Kubernetes EnvVar v1 core specification.

Fields
name	string Name of the environment variable. Must be a valid C identifier and must not begin with the prefix AIP_.
value	string Value of the environment variable. Defaults to an empty string. In this field, you can reference environment variables set by AI Platform Prediction and environment variables set earlier in the same env field as where this message occurs. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $(VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME)

GoogleCloudMlV1__ExplainRequest

Request for explanations to be issued against a trained model.

Fields
httpBody	object (GoogleApi__HttpBody) Required. The explanation request body.

GoogleCloudMlV1__ExplanationConfig

Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. Learn more about feature attributions.

Fields
integratedGradientsAttribution	object (GoogleCloudMlV1__IntegratedGradientsAttribution) Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
sampledShapleyAttribution	object (GoogleCloudMlV1__SampledShapleyAttribution) An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.
xraiAttribution	object (GoogleCloudMlV1__XraiAttribution) Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.

GoogleCloudMlV1__GetConfigResponse

Returns service account information associated with a project.

Fields
config	object (GoogleCloudMlV1__Config) (No description provided)
serviceAccount	string The service account Cloud ML uses to access resources in the project.
serviceAccountProject	string (int64 format) The project number for service_account.

GoogleCloudMlV1__HyperparameterOutput

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.

Fields
allMetrics[]	object (GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric) All recorded object metrics for this trial. This field is not currently populated.
builtInAlgorithmOutput	object (GoogleCloudMlV1__BuiltInAlgorithmOutput) Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
endTime	string (Timestamp format) Output only. End time for the trial.
finalMetric	object (GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric) The final objective metric seen for this trial.
hyperparameters	map (key: string, value: string) The hyperparameters given to this trial.
isTrialStoppedEarly	boolean True if the trial is stopped early.
startTime	string (Timestamp format) Output only. Start time for the trial.
state	enum Output only. The detailed state of the trial.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The job state is unspecified.
QUEUED	The job has been just created and processing has not yet begun.
PREPARING	The service is preparing to run the job.
RUNNING	The job is in progress.
SUCCEEDED	The job completed successfully.
FAILED	The job failed. error_message should contain the details of the failure.
CANCELLING	The job is being cancelled. error_message should describe the reason for the cancellation.
CANCELLED	The job has been cancelled. error_message should describe the reason for the cancellation.
trialId	string The trial id for these results.
webAccessUris	map (key: string, value: string) URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's training_input.enable_web_access is true. The keys are names of each node in the training job; for example, master-replica-0 for the master node, worker-replica-0 for the first worker, and ps-replica-0 for the first parameter server. The values are the URIs for each node's interactive shell.

GoogleCloudMlV1__HyperparameterSpec

Represents a set of hyperparameters to optimize.

Fields
algorithm	enum Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
Enum type. Can be one of the following:
ALGORITHM_UNSPECIFIED	The default algorithm used by the hyperparameter tuning service. This is a Bayesian optimization algorithm.
GRID_SEARCH	Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
RANDOM_SEARCH	Simple random search within the feasible space.
enableTrialEarlyStopping	boolean Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
goal	enum Required. The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
Enum type. Can be one of the following:
GOAL_TYPE_UNSPECIFIED	Goal Type will default to maximize.
MAXIMIZE	Maximize the goal metric.
MINIMIZE	Minimize the goal metric.
hyperparameterMetricTag	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.
maxFailedTrials	integer (int32 format) 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.
maxParallelTrials	integer (int32 format) 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.
maxTrials	integer (int32 format) Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
params[]	object (GoogleCloudMlV1__ParameterSpec) Required. The set of parameters to tune.
resumePreviousJobId	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.

GoogleCloudMlV1__IntegratedGradientsAttribution

Attributes credit by computing the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Fields
numIntegralSteps	integer (int32 format) Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.

GoogleCloudMlV1__Job

Represents a training or prediction job.

Fields
createTime	string (Timestamp format) Output only. When the job was created.
endTime	string (Timestamp format) Output only. When the job processing was completed.
errorMessage	string Output only. The details of a failure or a cancellation.
etag	string (bytes format) 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.
jobId	string Required. The user-specified id of the job.
jobPosition	string (int64 format) Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
labels	map (key: string, value: string) 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 using labels.
predictionInput	object (GoogleCloudMlV1__PredictionInput) Input parameters to create a prediction job.
predictionOutput	object (GoogleCloudMlV1__PredictionOutput) The current prediction job result.
startTime	string (Timestamp format) Output only. When the job processing was started.
state	enum Output only. The detailed state of a job.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The job state is unspecified.
QUEUED	The job has been just created and processing has not yet begun.
PREPARING	The service is preparing to run the job.
RUNNING	The job is in progress.
SUCCEEDED	The job completed successfully.
FAILED	The job failed. error_message should contain the details of the failure.
CANCELLING	The job is being cancelled. error_message should describe the reason for the cancellation.
CANCELLED	The job has been cancelled. error_message should describe the reason for the cancellation.
trainingInput	object (GoogleCloudMlV1__TrainingInput) Input parameters to create a training job.
trainingOutput	object (GoogleCloudMlV1__TrainingOutput) The current training job result.

GoogleCloudMlV1__ListJobsResponse

Response message for the ListJobs method.

Fields
jobs[]	object (GoogleCloudMlV1__Job) The list of jobs.
nextPageToken	string Optional. Pass this token as the page_token field of the request for a subsequent call.

GoogleCloudMlV1__ListLocationsResponse

(No description provided)

Fields
locations[]	object (GoogleCloudMlV1__Location) Locations where at least one type of CMLE capability is available.
nextPageToken	string Optional. Pass this token as the page_token field of the request for a subsequent call.

GoogleCloudMlV1__ListModelsResponse

Response message for the ListModels method.

Fields
models[]	object (GoogleCloudMlV1__Model) The list of models.
nextPageToken	string Optional. Pass this token as the page_token field of the request for a subsequent call.

GoogleCloudMlV1__ListOptimalTrialsResponse

The response message for the ListOptimalTrials method.

Fields
trials[]	object (GoogleCloudMlV1__Trial) The pareto-optimal trials for multiple objective study or the optimal trial for single objective study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency

GoogleCloudMlV1__ListStudiesResponse

(No description provided)

Fields
studies[]	object (GoogleCloudMlV1__Study) The studies associated with the project.

GoogleCloudMlV1__ListTrialsResponse

The response message for the ListTrials method.

Fields
trials[]	object (GoogleCloudMlV1__Trial) The trials associated with the study.

GoogleCloudMlV1__ListVersionsResponse

Response message for the ListVersions method.

Fields
nextPageToken	string Optional. Pass this token as the page_token field of the request for a subsequent call.
versions[]	object (GoogleCloudMlV1__Version) The list of versions.

GoogleCloudMlV1__Location

(No description provided)

Fields
capabilities[]	object (GoogleCloudMlV1__Capability) Capabilities available in the location.
name	string (No description provided)

GoogleCloudMlV1__ManualScaling

Options for manually scaling a model.

Fields
nodes	integer (int32 format) The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed, so the cost of operating this model will be proportional to nodes * number of hours since last billing cycle plus the cost for each prediction performed.

GoogleCloudMlV1__Measurement

A message representing a measurement.

Fields
elapsedTime	string (Duration format) Output only. Time that the trial has been running at the point of this measurement.
metrics[]	object (GoogleCloudMlV1_Measurement_Metric) Provides a list of metrics that act as inputs into the objective function.
stepCount	string (int64 format) The number of steps a machine learning model has been trained for. Must be non-negative.

GoogleCloudMlV1__MetricSpec

MetricSpec contains the specifications to use to calculate the desired nodes count when autoscaling is enabled.

Fields
name	enum metric name.
Enum type. Can be one of the following:
METRIC_NAME_UNSPECIFIED	Unspecified MetricName.
CPU_USAGE	CPU usage.
GPU_DUTY_CYCLE	GPU duty cycle.
target	integer (int32 format) Target specifies the target value for the given metric; once real metric deviates from the threshold by a certain percentage, the node count changes.

GoogleCloudMlV1__Model

Represents a machine learning solution. A model can have multiple versions, each of which is a deployed, trained model ready to receive prediction requests. The model itself is just a container.

Fields
defaultVersion	object (GoogleCloudMlV1__Version) Output only. The default version of the model. This version will be used to handle prediction requests that do not specify a version. You can change the default version by calling projects.models.versions.setDefault.
description	string Optional. The description specified for the model when it was created.
etag	string (bytes format) etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform model updates in order to avoid race conditions: An etag is returned in the response to GetModel, and systems are expected to put that etag in the request to UpdateModel to ensure that their change will be applied to the model as intended.
labels	map (key: string, value: string) Optional. One or more labels that you can add, to organize your models. 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 using labels. Note that this field is not updatable for mls1* models.
name	string Required. The name specified for the model when it was created. The model name must be unique within the project it is created in.
onlinePredictionConsoleLogging	boolean Optional. If true, online prediction nodes send stderr and stdout streams to Cloud Logging. These can be more verbose than the standard access logs (see onlinePredictionLogging) and can incur higher cost. However, they are helpful for debugging. Note that logs may incur a cost, especially if your project receives prediction requests at a high QPS. Estimate your costs before enabling this option. Default is false.
onlinePredictionLogging	boolean Optional. If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option. Default is false.
regions[]	string Optional. The list of regions where the model is going to be deployed. Only one region per model is supported. Defaults to 'us-central1' if nothing is set. See the available regions for AI Platform services. Note: * No matter where a model is deployed, it can always be accessed by users from anywhere, both for online and batch prediction. * The region for a batch prediction job is set by the region field when submitting the batch prediction job and does not take its value from this field.

GoogleCloudMlV1__OperationMetadata

Represents the metadata of the long-running operation.

Fields
createTime	string (Timestamp format) The time the operation was submitted.
endTime	string (Timestamp format) The time operation processing completed.
isCancellationRequested	boolean Indicates whether a request to cancel this operation has been made.
labels	map (key: string, value: string) The user labels, inherited from the model or the model version being operated on.
modelName	string Contains the name of the model associated with the operation.
operationType	enum The operation type.
Enum type. Can be one of the following:
OPERATION_TYPE_UNSPECIFIED	Unspecified operation type.
CREATE_VERSION	An operation to create a new version.
DELETE_VERSION	An operation to delete an existing version.
DELETE_MODEL	An operation to delete an existing model.
UPDATE_MODEL	An operation to update an existing model.
UPDATE_VERSION	An operation to update an existing version.
UPDATE_CONFIG	An operation to update project configuration.
projectNumber	string (int64 format) Contains the project number associated with the operation.
startTime	string (Timestamp format) The time operation processing started.
version	object (GoogleCloudMlV1__Version) Contains the version associated with the operation.

GoogleCloudMlV1__ParameterSpec

Represents a single hyperparameter to optimize.

Fields
categoricalValues[]	string Required if type is CATEGORICAL. The list of possible categories.
discreteValues[]	number (double format) 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.
maxValue	number (double format) 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	number (double format) 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.
parameterName	string Required. The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
scaleType	enum 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).
Enum type. Can be one of the following:
NONE	By default, no scaling is applied.
UNIT_LINEAR_SCALE	Scales the feasible space to (0, 1) linearly.
UNIT_LOG_SCALE	Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
UNIT_REVERSE_LOG_SCALE	Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
type	enum Required. The type of the parameter.
Enum type. Can be one of the following:
PARAMETER_TYPE_UNSPECIFIED	You must specify a valid type. Using this unspecified type will result in an error.
DOUBLE	Type for real-valued parameters.
INTEGER	Type for integral parameters.
CATEGORICAL	The parameter is categorical, with a value chosen from the categories field.
DISCRETE	The parameter is real valued, with a fixed set of feasible points. If type==DISCRETE, feasible_points must be provided, and {min_value, max_value} will be ignored.

GoogleCloudMlV1__PredictRequest

Request for predictions to be issued against a trained model.

Fields
httpBody	object (GoogleApi__HttpBody) Required. The prediction request body. Refer to the request body details section for more information on how to structure your request.

GoogleCloudMlV1__PredictionInput

Represents input parameters for a prediction job.

Fields
batchSize	string (int64 format) 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.
dataFormat	enum Required. The format of the input data files.
Enum type. Can be one of the following:
DATA_FORMAT_UNSPECIFIED	Unspecified format.
JSON	Each line of the file is a JSON dictionary representing one record.
TEXT	Deprecated. Use JSON instead.
TF_RECORD	The source file is a TFRecord file. Currently available only for input data.
TF_RECORD_GZIP	The source file is a GZIP-compressed TFRecord file. Currently available only for input data.
CSV	Values are comma-separated rows, with keys in a separate file. Currently available only for output data.
inputPaths[]	string Required. The Cloud Storage location of the input data files. May contain wildcards.
maxWorkerCount	string (int64 format) Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
modelName	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"
outputDataFormat	enum Optional. Format of the output data files, defaults to JSON.
Enum type. Can be one of the following:
DATA_FORMAT_UNSPECIFIED	Unspecified format.
JSON	Each line of the file is a JSON dictionary representing one record.
TEXT	Deprecated. Use JSON instead.
TF_RECORD	The source file is a TFRecord file. Currently available only for input data.
TF_RECORD_GZIP	The source file is a GZIP-compressed TFRecord file. Currently available only for input data.
CSV	Values are comma-separated rows, with keys in a separate file. Currently available only for output data.
outputPath	string Required. The output Google Cloud Storage location.
region	string Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
runtimeVersion	string Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
signatureName	string Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to SavedModel for information about how to use signatures. Defaults to DEFAULT_SERVING_SIGNATURE_DEF_KEY , which is "serving_default".
uri	string Use this field if you want to specify a Google Cloud Storage path for the model to use.
versionName	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"

GoogleCloudMlV1__PredictionOutput

Represents results of a prediction job.

Fields
errorCount	string (int64 format) The number of data instances which resulted in errors.
nodeHours	number (double format) Node hours used by the batch prediction job.
outputPath	string The output Google Cloud Storage location provided at the job creation time.
predictionCount	string (int64 format) The number of generated predictions.

GoogleCloudMlV1__ReplicaConfig

Represents the configuration for a replica in a cluster.

Fields
acceleratorConfig	object (GoogleCloudMlV1__AcceleratorConfig) Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
containerArgs[]	string Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
containerCommand[]	string The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
diskConfig	object (GoogleCloudMlV1__DiskConfig) Represents the configuration of disk options.
imageUri	string The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
tpuTfVersion	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. 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. 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.

GoogleCloudMlV1__RequestLoggingConfig

Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by BigQuery quotas and limits. If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using continuous evaluation, you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs.

Fields
bigqueryTableName	string Required. Fully qualified BigQuery table name in the following format: " project_id.dataset_name.table_name" The specified table must already exist, and the "Cloud ML Service Agent" for your project must have permission to write to it. The table must have the following schema: Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE groundtruth STRING NULLABLE
samplingPercentage	number (double format) Percentage of requests to be logged, expressed as a fraction from 0 to 1. For example, if you want to log 10% of requests, enter 0.1. The sampling window is the lifetime of the model version. Defaults to 0.

GoogleCloudMlV1__RouteMap

Specifies HTTP paths served by a custom container. AI Platform Prediction sends requests to these paths on the container; the custom container must run an HTTP server that responds to these requests with appropriate responses. Read Custom container requirements for details on how to create your container image to meet these requirements.

Fields
health	string HTTP path on the container to send health checkss to. AI Platform Prediction intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then AI Platform Prediction intermittently sends a GET request to the /bar path on the port of your container specified by the first value of Version.container.ports. If you don't specify this field, it defaults to the following value: /v1/models/ MODEL/versions/VERSION The placeholders in this value are replaced as follows: * MODEL: The name of the parent Model. This does not include the "projects/PROJECT_ID/models/" prefix that the API returns in output; it is the bare model name, as provided to projects.models.create. * VERSION: The name of the model version. This does not include the "projects/PROJECT_ID /models/MODEL/versions/" prefix that the API returns in output; it is the bare version name, as provided to projects.models.versions.create.
predict	string HTTP path on the container to send prediction requests to. AI Platform Prediction forwards requests sent using projects.predict to this path on the container's IP address and port. AI Platform Prediction then returns the container's response in the API response. For example, if you set this field to /foo, then when AI Platform Prediction receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of Version.container.ports. If you don't specify this field, it defaults to the following value: /v1/models/MODEL/versions/VERSION:predict The placeholders in this value are replaced as follows: * MODEL: The name of the parent Model. This does not include the "projects/PROJECT_ID/models/" prefix that the API returns in output; it is the bare model name, as provided to projects.models.create. * VERSION: The name of the model version. This does not include the "projects/PROJECT_ID/models/MODEL/versions/" prefix that the API returns in output; it is the bare version name, as provided to projects.models.versions.create.

GoogleCloudMlV1__SampledShapleyAttribution

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

Fields
numPaths	integer (int32 format) The number of feature permutations to consider when approximating the Shapley values.

GoogleCloudMlV1__Scheduling

All parameters related to scheduling of training jobs.

Fields
maxRunningTime	string (Duration format) Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by s. If not specified, this field defaults to 604800s (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the RUNNING state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. 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 specify this field in a config.yaml file. For example: yaml trainingInput: scheduling: maxRunningTime: 7200s
maxWaitTime	string (Duration format) Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by s. If not specified, there is no limit to the wait time. The minimum for this field is 1800s (30 minutes). If the training job has not entered the RUNNING state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a VM restart, this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to 3600s (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the QUEUED or PREPARING state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the gcloud tool, you can specify this field in a config.yaml file. For example: yaml trainingInput: scheduling: maxWaitTime: 3600s
priority	integer (int32 format) Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.

GoogleCloudMlV1__Study

A message representing a Study.

Fields
createTime	string (Timestamp format) Output only. Time at which the study was created.
inactiveReason	string Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
name	string Output only. The name of a study.
state	enum Output only. The detailed state of a study.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The study state is unspecified.
ACTIVE	The study is active.
INACTIVE	The study is stopped due to an internal error.
COMPLETED	The study is done when the service exhausts the parameter search space or max_trial_count is reached.
studyConfig	object (GoogleCloudMlV1__StudyConfig) Required. Configuration of the study.

GoogleCloudMlV1__StudyConfig

Represents configuration of a study.

Fields
algorithm	enum The search algorithm specified for the study.
Enum type. Can be one of the following:
ALGORITHM_UNSPECIFIED	The default algorithm used by the Cloud AI Platform Vizier service.
GAUSSIAN_PROCESS_BANDIT	Gaussian Process Bandit.
GRID_SEARCH	Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
RANDOM_SEARCH	Simple random search within the feasible space.
automatedStoppingConfig	object (GoogleCloudMlV1__AutomatedStoppingConfig) Configuration for automated stopping of unpromising Trials.
metrics[]	object (GoogleCloudMlV1_StudyConfig_MetricSpec) Metric specs for the study.
parameters[]	object (GoogleCloudMlV1_StudyConfig_ParameterSpec) Required. The set of parameters to tune.

GoogleCloudMlV1__SuggestTrialsMetadata

Metadata field of a google.longrunning.Operation associated with a SuggestTrialsRequest.

Fields
clientId	string The identifier of the client that is requesting the suggestion.
createTime	string (Timestamp format) The time operation was submitted.
study	string The name of the study that the trial belongs to.
suggestionCount	integer (int32 format) The number of suggestions requested.

GoogleCloudMlV1__SuggestTrialsRequest

The request message for the SuggestTrial service method.

Fields
clientId	string Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same client_id, the service will return the identical suggested trial if the trial is pending, and provide a new trial if the last suggested trial was completed.
suggestionCount	integer (int32 format) Required. The number of suggestions requested.

GoogleCloudMlV1__SuggestTrialsResponse

This message will be placed in the response field of a completed google.longrunning.Operation associated with a SuggestTrials request.

Fields
endTime	string (Timestamp format) The time at which operation processing completed.
startTime	string (Timestamp format) The time at which the operation was started.
studyState	enum The state of the study.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The study state is unspecified.
ACTIVE	The study is active.
INACTIVE	The study is stopped due to an internal error.
COMPLETED	The study is done when the service exhausts the parameter search space or max_trial_count is reached.
trials[]	object (GoogleCloudMlV1__Trial) A list of trials.

GoogleCloudMlV1__TrainingInput

Represents input parameters for a training job. When using the gcloud command to submit your training job, you can specify the input parameters as command-line arguments and/or in a YAML configuration file referenced from the --config command-line argument. For details, see the guide to submitting a training job.

Fields
args[]	string 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 ENTRYPOINT command.
enableWebAccess	boolean Optional. Whether you want AI Platform Training to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).
encryptionConfig	object (GoogleCloudMlV1__EncryptionConfig) 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.
evaluatorConfig	object (GoogleCloudMlV1__ReplicaConfig) 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. 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.
evaluatorCount	string (int64 format) 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.
evaluatorType	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.
hyperparameters	object (GoogleCloudMlV1__HyperparameterSpec) Optional. The set of Hyperparameters to tune.
jobDir	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.
masterConfig	object (GoogleCloudMlV1__ReplicaConfig) 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. 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.
masterType	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. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of 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.
network	string Optional. The full name of the Compute Engine network to which the Job is peered. For example, projects/12345/global/networks/myVPC. The format of this field is projects/{project}/global/networks/{network}, where {project} is a project number (like 12345) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. Learn about using VPC Network Peering..
packageUris[]	string 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.
parameterServerConfig	object (GoogleCloudMlV1__ReplicaConfig) 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. 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.
parameterServerCount	string (int64 format) 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.
parameterServerType	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.
pythonModule	string Required. The Python module name to run after installing the packages.
pythonVersion	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.
region	string Required. The region to run the training job in. See the available regions for AI Platform Training.
runtimeVersion	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 and learn how to manage runtime versions.
scaleTier	enum Required. Specifies the machine types, the number of replicas for workers and parameter servers.
Enum type. Can be one of the following:
BASIC	A single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
STANDARD_1	Many workers and a few parameter servers.
PREMIUM_1	A large number of workers with many parameter servers.
BASIC_GPU	A single worker instance with a GPU.
BASIC_TPU	A single worker instance with a Cloud TPU.
CUSTOM	The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines: * You must set TrainingInput.masterType to specify the type of machine to use for your master node. This is the only required setting. * You may set TrainingInput.workerCount to specify the number of workers to use. If you specify one or more workers, you must also set TrainingInput.workerType to specify the type of machine to use for your worker nodes. * You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use. If you specify one or more parameter servers, you must also set TrainingInput.parameterServerType to specify the type of machine to use for your parameter servers. Note that all of your workers must use the same machine type, which can be different from your parameter server type and master type. Your parameter servers must likewise use the same machine type, which can be different from your worker type and master type.
scheduling	object (GoogleCloudMlV1__Scheduling) Optional. Scheduling options for a training job.
serviceAccount	string Optional. The email address of a service account to use when running the training appplication. You must have the iam.serviceAccounts.actAs permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the roles/iam.serviceAccountAdmin role for the specified service account. Learn more about configuring a service account. If not specified, the AI Platform Training Google-managed service account is used by default.
useChiefInTfConfig	boolean 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. This field has no effect for training jobs that don't use a custom container.
workerConfig	object (GoogleCloudMlV1__ReplicaConfig) 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. 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.
workerCount	string (int64 format) 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.
workerType	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. This value must be present when scaleTier is set to CUSTOM and workerCount is greater than zero.

GoogleCloudMlV1__TrainingOutput

Represents results of a training job. Output only.

Fields
builtInAlgorithmOutput	object (GoogleCloudMlV1__BuiltInAlgorithmOutput) Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
completedTrialCount	string (int64 format) The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
consumedMLUnits	number (double format) The amount of ML units consumed by the job.
hyperparameterMetricTag	string The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
isBuiltInAlgorithmJob	boolean Whether this job is a built-in Algorithm job.
isHyperparameterTuningJob	boolean Whether this job is a hyperparameter tuning job.
trials[]	object (GoogleCloudMlV1__HyperparameterOutput) Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
webAccessUris	map (key: string, value: string) Output only. URIs for accessing interactive shells (one URI for each training node). Only available if training_input.enable_web_access is true. The keys are names of each node in the training job; for example, master-replica-0 for the master node, worker-replica-0 for the first worker, and ps-replica-0 for the first parameter server. The values are the URIs for each node's interactive shell.

GoogleCloudMlV1__Trial

A message representing a trial.

Fields
clientId	string Output only. The identifier of the client that originally requested this trial.
endTime	string (Timestamp format) Output only. Time at which the trial's status changed to COMPLETED.
finalMeasurement	object (GoogleCloudMlV1__Measurement) The final measurement containing the objective value.
infeasibleReason	string Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
measurements[]	object (GoogleCloudMlV1__Measurement) A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
name	string Output only. Name of the trial assigned by the service.
parameters[]	object (GoogleCloudMlV1_Trial_Parameter) The parameters of the trial.
startTime	string (Timestamp format) Output only. Time at which the trial was started.
state	enum The detailed state of a trial.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The trial state is unspecified.
REQUESTED	Indicates that a specific trial has been requested, but it has not yet been suggested by the service.
ACTIVE	Indicates that the trial has been suggested.
COMPLETED	Indicates that the trial is done, and either has a final_measurement set, or is marked as trial_infeasible.
STOPPING	Indicates that the trial should stop according to the service.
trialInfeasible	boolean Output only. If true, the parameters in this trial are not attempted again.

GoogleCloudMlV1__Version

Represents a version of the model. Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions. You can get information about all of the versions of a given model by calling projects.models.versions.list.

Fields
acceleratorConfig	object (GoogleCloudMlV1__AcceleratorConfig) Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the machineType field. Learn more about using GPUs for online prediction.
autoScaling	object (GoogleCloudMlV1__AutoScaling) Automatically scale the number of nodes used to serve the model in response to increases and decreases in traffic. Care should be taken to ramp up traffic according to the model's ability to scale or you will start seeing increases in latency and 429 response codes.
container	object (GoogleCloudMlV1__ContainerSpec) Optional. Specifies a custom container to use for serving predictions. If you specify this field, then machineType is required. If you specify this field, then deploymentUri is optional. If you specify this field, then you must not specify runtimeVersion, packageUris, framework, pythonVersion, or predictionClass.
createTime	string (Timestamp format) Output only. The time the version was created.
deploymentUri	string The Cloud Storage URI of a directory containing trained model artifacts to be used to create the model version. See the guide to deploying models for more information. The total number of files under this directory must not exceed 1000. During projects.models.versions.create, AI Platform Prediction copies all files from the specified directory to a location managed by the service. From then on, AI Platform Prediction uses these copies of the model artifacts to serve predictions, not the original files in Cloud Storage, so this location is useful only as a historical record. If you specify container, then this field is optional. Otherwise, it is required. Learn how to use this field with a custom container.
description	string Optional. The description specified for the version when it was created.
errorMessage	string Output only. The details of a failure or a cancellation.
etag	string (bytes format) etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform model updates in order to avoid race conditions: An etag is returned in the response to GetVersion, and systems are expected to put that etag in the request to UpdateVersion to ensure that their change will be applied to the model as intended.
explanationConfig	object (GoogleCloudMlV1__ExplanationConfig) Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
framework	enum Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are TENSORFLOW, SCIKIT_LEARN, XGBOOST. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose SCIKIT_LEARN or XGBOOST, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a custom prediction routine or if you're using a custom container.
Enum type. Can be one of the following:
FRAMEWORK_UNSPECIFIED	Unspecified framework. Assigns a value based on the file suffix.
TENSORFLOW	Tensorflow framework.
SCIKIT_LEARN	Scikit-learn framework.
XGBOOST	XGBoost framework.
isDefault	boolean Output only. If true, this version will be used to handle prediction requests that do not specify a version. You can change the default version by calling projects.methods.versions.setDefault.
labels	map (key: string, value: string) Optional. One or more labels that you can add, to organize your model versions. 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 using labels. Note that this field is not updatable for mls1* models.
lastMigrationModelId	string Output only. The AI Platform (Unified) Model ID for the last model migration.
lastMigrationTime	string (Timestamp format) Output only. The last time this version was successfully migrated to AI Platform (Unified).
lastUseTime	string (Timestamp format) Output only. The time the version was last used for prediction.
machineType	string Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. To learn about valid values for this field, read Choosing a machine type for online prediction. If this field is not specified and you are using a regional endpoint, then the machine type defaults to n1-standard-2. If this field is not specified and you are using the global endpoint (ml.googleapis.com), then the machine type defaults to mls1-c1-m2.
manualScaling	object (GoogleCloudMlV1__ManualScaling) Manually select the number of nodes to use for serving the model. You should generally use auto_scaling with an appropriate min_nodes instead, but this option is available if you want more predictable billing. Beware that latency and error rates will increase if the traffic exceeds that capability of the system to serve it based on the selected number of nodes.
name	string Required. The name specified for the version when it was created. The version name must be unique within the model it is created in.
packageUris[]	string Optional. Cloud Storage paths (gs://…) of packages for custom prediction routines or scikit-learn pipelines with custom code. For a custom prediction routine, one of these packages must contain your Predictor class (see predictionClass). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected runtime version. If you specify this field, you must also set runtimeVersion to 1.4 or greater.
predictionClass	string Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the packageUris field. Specify this field if and only if you are deploying a custom prediction routine (beta). If you specify this field, you must set runtimeVersion to 1.4 or greater and you must set machineType to a legacy (MLS1) machine type. The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about the Predictor interface and custom prediction routines.
pythonVersion	string Required. The version of Python used in prediction. 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.
requestLoggingConfig	object (GoogleCloudMlV1__RequestLoggingConfig) Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
routes	object (GoogleCloudMlV1__RouteMap) Optional. Specifies paths on a custom container's HTTP server where AI Platform Prediction sends certain requests. If you specify this field, then you must also specify the container field. If you specify the container field and do not specify this field, it defaults to the following: json { "predict": "/v1/models/MODEL/versions/VERSION:predict", "health": "/v1/models/MODEL/versions/VERSION" } See RouteMap for more details about these default values.
runtimeVersion	string Required. The AI Platform runtime version to use for this deployment. For more information, see the runtime version list and how to manage runtime versions.
serviceAccount	string Optional. Specifies the service account for resource access control. If you specify this field, then you must also specify either the containerSpec or the predictionClass field. Learn more about using a custom service account.
state	enum Output only. The state of a version.
Enum type. Can be one of the following:
UNKNOWN	The version state is unspecified.
READY	The version is ready for prediction.
CREATING	The version is being created. New UpdateVersion and DeleteVersion requests will fail if a version is in the CREATING state.
FAILED	The version failed to be created, possibly cancelled. error_message should contain the details of the failure.
DELETING	The version is being deleted. New UpdateVersion and DeleteVersion requests will fail if a version is in the DELETING state.
UPDATING	The version is being updated. New UpdateVersion and DeleteVersion requests will fail if a version is in the UPDATING state.

GoogleCloudMlV1__XraiAttribution

Attributes credit by computing the XRAI taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Currently only implemented for models with natural image inputs.

Fields
numIntegralSteps	integer (int32 format) Number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range.

GoogleIamV1__AuditConfig

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:[e-mail]" ] }, { "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:[e-mail]" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts [e-mail] from DATA_READ logging, and [e-mail] from DATA_WRITE logging.

Fields
auditLogConfigs[]	object (GoogleIamV1__AuditLogConfig) The configuration for logging of each type of permission.
service	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.

GoogleIamV1__AuditLogConfig

Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:[e-mail]" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting [e-mail] from DATA_READ logging.

Fields
exemptedMembers[]	string Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
logType	enum The log type that this config enables.
Enum type. Can be one of the following:
LOG_TYPE_UNSPECIFIED	Default case. Should never be this.
ADMIN_READ	Admin reads. Example: CloudIAM getIamPolicy
DATA_WRITE	Data writes. Example: CloudSQL Users create
DATA_READ	Data reads. Example: CloudSQL Users list

GoogleIamV1__Binding

Associates members, or principals, with a role.

Fields
condition	object (GoogleType__Expr) The condition that is associated with this binding. If the condition evaluates to true, then this binding applies to the current request. If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the IAM documentation.
members[]	string Specifies the principals requesting access for a Google Cloud 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. Does not include identities that come from external identity providers (IdPs) through identity federation. * user:{emailid}: An email address that represents a specific Google account. For example, [e-mail] . * serviceAccount:{emailid}: An email address that represents a Google service account. For example, [e-mail]. * serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account. For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. * group:{emailid}: An email address that represents a Google group. For example, [e-mail]. * domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. * deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, [e-mail]?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, [e-mail]?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, [e-mail]?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding.
role	string Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.

GoogleIamV1__Policy

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, or principals, to a single role. Principals 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. For some types of Google Cloud resources, a binding can also 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. To learn which resources support conditions in their IAM policies, see the IAM documentation. JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:[e-mail]", "group:[e-mail]", "domain:google.com", "serviceAccount:[e-mail]" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:[e-mail]" ], "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:[e-mail] - group:[e-mail] - domain:google.com - serviceAccount:[e-mail] role: roles/resourcemanager.organizationAdmin - members: - user:[e-mail] 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.

Fields
auditConfigs[]	object (GoogleIamV1__AuditConfig) Specifies cloud audit logging configuration for this policy.
bindings[]	object (GoogleIamV1__Binding) Associates a list of members, or principals, with 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 principal. The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:[e-mail], and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
etag	string (bytes format) 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	integer (int32 format) 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. To learn which resources support conditions in their IAM policies, see the IAM documentation.

GoogleIamV1__SetIamPolicyRequest

Request message for SetIamPolicy method.

Fields
policy	object (GoogleIamV1__Policy) 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 Google Cloud services (such as Projects) might reject them.
updateMask	string (FieldMask format) 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"

GoogleIamV1__TestIamPermissionsRequest

Request message for TestIamPermissions method.

Fields
permissions[]	string 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.

GoogleIamV1__TestIamPermissionsResponse

Response message for TestIamPermissions method.

Fields
permissions[]	string A subset of TestPermissionsRequest.permissions that the caller is allowed.

GoogleLongrunning__ListOperationsResponse

The response message for Operations.ListOperations.

Fields
nextPageToken	string The standard List next-page token.
operations[]	object (GoogleLongrunning__Operation) A list of operations that matches the specified filter in the request.

GoogleLongrunning__Operation

This resource represents a long-running operation that is the result of a network API call.

Fields
done	boolean If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
error	object (GoogleRpc__Status) The error result of the operation in case of failure or cancellation.
metadata	map (key: string, value: any) Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
name	string The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
response	map (key: string, value: any) The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.

GoogleRpc__Status

The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.

Fields
code	integer (int32 format) The status code, which should be an enum value of google.rpc.Code.
details[]	object A list of messages that carry the error details. There is a common set of message types for APIs to use.
message	string A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

GoogleType__Expr

Represents a textual expression in the Common Expression Language (CEL) 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.

Fields
description	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	string Textual representation of an expression in Common Expression Language syntax.
location	string Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
title	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.