An API to enable creating and using machine learning models.

Defines the root interface for all clients that generate credentials for calling Google APIs. All clients should implement this interface.

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.

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.

The request message for the AddTrialMeasurement service method.

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

Options for automatically scaling a model.

Represents output related to a built-in algorithm Job.

Request message for the CancelJob method.

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

The request message for the CheckTrialEarlyStoppingState service method.

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

The request message for the CompleteTrial service method.

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

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

Represents the config of disk options.

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

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

Request for explanations to be issued against a trained model.

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.

Returns service account information associated with a project.

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.

Represents a set of hyperparameters to optimize.

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

Represents a training or prediction job.

Response message for the ListJobs method.

Response message for the ListModels method.

The request message for the ListTrials service method.

The response message for the ListOptimalTrials method.

The response message for the ListTrials method.

Response message for the ListVersions method.

Options for manually scaling a model.

A message representing a measurement.

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

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.

Represents the metadata of the long-running operation.

Represents a single hyperparameter to optimize.

Represents input parameters for a prediction job.

Represents results of a prediction job.

Request for predictions to be issued against a trained model.

Represents the configuration for a replica in a cluster.

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.

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.

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.

All parameters related to scheduling of training jobs.

Request message for the SetDefaultVersion request.

A message representing a Study.

Represents configuration of a study.

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

The request message for the SuggestTrial service method.

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

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.

Represents results of a training job. Output only.

A message representing a trial.

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.

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.

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.

An observed value of a metric.

A message representing a metric in the measurement.

Represents a metric to optimize.

Represents a single parameter to optimize.

Represents the spec to match categorical values from parent parameter.

Represents the spec to match discrete values from parent parameter.

Represents the spec to match integer values from parent parameter.

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

Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs. If there are AuditConfigs for both allServices and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted. Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts jose@example.com from DATA_READ logging, and aliya@example.com from DATA_WRITE logging.

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

Associates members, or principals, with a role.

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:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: ``` bindings: - members:

• user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 ``` For a description of IAM and its features, see the IAM documentation.

Request message for SetIamPolicy method.

Request message for TestIamPermissions method.

Response message for TestIamPermissions method.

The response message for Operations.ListOperations.

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

A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }

The 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.

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.

Additional options for ml#projectsJobsGetIamPolicy.

Additional options for ml#projectsJobsList.

Additional options for ml#projectsJobsPatch.

Additional options for ml#projectsLocationsList.

Additional options for ml#projectsLocationsStudiesCreate.

Additional options for ml#projectsModelsGetIamPolicy.

Additional options for ml#projectsModelsList.

Additional options for ml#projectsModelsPatch.

Additional options for ml#projectsModelsVersionsList.

Additional options for ml#projectsModelsVersionsPatch.

Additional options for ml#projectsOperationsList.