googleapis.ml.v1 library

Classes

GoogleApiHttpBody
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.
GoogleCloudMlV1AcceleratorConfig
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.
GoogleCloudMlV1AddTrialMeasurementRequest
The request message for the AddTrialMeasurement service method.
GoogleCloudMlV1AutomatedStoppingConfig
Configuration for Automated Early Stopping of Trials. If no implementation_config is set, automated early stopping will not be run.
GoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfig
GoogleCloudMlV1AutomatedStoppingConfigMedianAutomatedStoppingConfig
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.
GoogleCloudMlV1AutoScaling
Options for automatically scaling a model.
GoogleCloudMlV1BuiltInAlgorithmOutput
Represents output related to a built-in algorithm Job.
GoogleCloudMlV1CancelJobRequest
Request message for the CancelJob method.
GoogleCloudMlV1Capability
GoogleCloudMlV1CheckTrialEarlyStoppingStateMetatdata
This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.
GoogleCloudMlV1CheckTrialEarlyStoppingStateRequest
The request message for the CheckTrialEarlyStoppingState service method.
GoogleCloudMlV1CheckTrialEarlyStoppingStateResponse
The message will be placed in the response field of a completed google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.
GoogleCloudMlV1CompleteTrialRequest
The request message for the CompleteTrial service method.
GoogleCloudMlV1Config
GoogleCloudMlV1ContainerPort
ContainerPort represents a network port in a single container.
GoogleCloudMlV1ContainerSpec
Specify a custom container to deploy. Our ContainerSpec is a subset of the Kubernetes Container specification. https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#container-v1-core
GoogleCloudMlV1EncryptionConfig
Represents a custom encryption key configuration that can be applied to a resource.
GoogleCloudMlV1EnvVar
EnvVar represents an environment variable present in a Container.
GoogleCloudMlV1ExplainRequest
Request for explanations to be issued against a trained model.
GoogleCloudMlV1ExplanationConfig
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.
GoogleCloudMlV1GetConfigResponse
Returns service account information associated with a project.
GoogleCloudMlV1HyperparameterOutput
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.
GoogleCloudMlV1HyperparameterOutputHyperparameterMetric
An observed value of a metric.
GoogleCloudMlV1HyperparameterSpec
Represents a set of hyperparameters to optimize.
GoogleCloudMlV1IntegratedGradientsAttribution
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
GoogleCloudMlV1Job
Represents a training or prediction job.
GoogleCloudMlV1ListJobsResponse
Response message for the ListJobs method.
GoogleCloudMlV1ListLocationsResponse
GoogleCloudMlV1ListModelsResponse
Response message for the ListModels method.
GoogleCloudMlV1ListStudiesResponse
GoogleCloudMlV1ListTrialsResponse
The response message for the ListTrials method.
GoogleCloudMlV1ListVersionsResponse
Response message for the ListVersions method.
GoogleCloudMlV1Location
GoogleCloudMlV1ManualScaling
Options for manually scaling a model.
GoogleCloudMlV1Measurement
A message representing a measurement.
GoogleCloudMlV1MeasurementMetric
A message representing a metric in the measurement.
GoogleCloudMlV1Model
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.
GoogleCloudMlV1OperationMetadata
Represents the metadata of the long-running operation.
GoogleCloudMlV1ParameterSpec
Represents a single hyperparameter to optimize.
GoogleCloudMlV1PredictionInput
Represents input parameters for a prediction job.
GoogleCloudMlV1PredictionOutput
Represents results of a prediction job.
GoogleCloudMlV1PredictRequest
Request for predictions to be issued against a trained model.
GoogleCloudMlV1ReplicaConfig
Represents the configuration for a replica in a cluster.
GoogleCloudMlV1RequestLoggingConfig
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.
GoogleCloudMlV1RouteMap
RouteMap is used to override HTTP paths sent to a Custom Container. If specified, the HTTP server implemented in the ContainerSpec must support the route. If unspecified, standard HTTP paths will be used.
GoogleCloudMlV1SampledShapleyAttribution
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.
GoogleCloudMlV1Scheduling
All parameters related to scheduling of training jobs.
GoogleCloudMlV1SetDefaultVersionRequest
Request message for the SetDefaultVersion request.
GoogleCloudMlV1StopTrialRequest
GoogleCloudMlV1Study
A message representing a Study.
GoogleCloudMlV1StudyConfig
Represents configuration of a study.
GoogleCloudMlV1StudyConfigMetricSpec
Represents a metric to optimize.
GoogleCloudMlV1StudyConfigParameterSpec
Represents a single parameter to optimize.
GoogleCloudMlV1StudyConfigParameterSpecCategoricalValueSpec
GoogleCloudMlV1StudyConfigParameterSpecDiscreteValueSpec
GoogleCloudMlV1StudyConfigParameterSpecDoubleValueSpec
GoogleCloudMlV1StudyConfigParameterSpecIntegerValueSpec
GoogleCloudMlV1StudyConfigParameterSpecMatchingParentCategoricalValueSpec
Represents the spec to match categorical values from parent parameter.
GoogleCloudMlV1StudyConfigParameterSpecMatchingParentDiscreteValueSpec
Represents the spec to match discrete values from parent parameter.
GoogleCloudMlV1StudyConfigParameterSpecMatchingParentIntValueSpec
Represents the spec to match integer values from parent parameter.
GoogleCloudMlV1SuggestTrialsMetadata
Metadata field of a google.longrunning.Operation associated with a SuggestTrialsRequest.
GoogleCloudMlV1SuggestTrialsRequest
The request message for the SuggestTrial service method.
GoogleCloudMlV1SuggestTrialsResponse
This message will be placed in the response field of a completed google.longrunning.Operation associated with a SuggestTrials request.
GoogleCloudMlV1TrainingInput
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.
GoogleCloudMlV1TrainingOutput
Represents results of a training job. Output only.
GoogleCloudMlV1Trial
A message representing a trial.
GoogleCloudMlV1TrialParameter
A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
GoogleCloudMlV1Version
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.
GoogleCloudMlV1XraiAttribution
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.
GoogleIamV1AuditConfig
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.
GoogleIamV1AuditLogConfig
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.
GoogleIamV1Binding
Associates members with a role.
GoogleIamV1Policy
An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role. 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.
GoogleIamV1SetIamPolicyRequest
Request message for SetIamPolicy method.
GoogleIamV1TestIamPermissionsRequest
Request message for TestIamPermissions method.
GoogleIamV1TestIamPermissionsResponse
Response message for TestIamPermissions method.
GoogleLongrunningListOperationsResponse
The response message for Operations.ListOperations.
GoogleLongrunningOperation
This resource represents a long-running operation that is the result of a network API call.
GoogleProtobufEmpty
A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.
GoogleRpcStatus
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.
GoogleTypeExpr
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.
MlApi
An API to enable creating and using machine learning models.
ProjectsJobsResourceApi
ProjectsLocationsOperationsResourceApi
ProjectsLocationsResourceApi
ProjectsLocationsStudiesResourceApi
ProjectsLocationsStudiesTrialsResourceApi
ProjectsModelsResourceApi
ProjectsModelsVersionsResourceApi
ProjectsOperationsResourceApi
ProjectsResourceApi

Constants

USER_AGENT → const String
'dart-api-client ml/v1'

Exceptions / Errors

ApiRequestError
Represents a general error reported by the API endpoint.
DetailedApiRequestError
Represents a specific error reported by the API endpoint.