[−][src]Crate google_ml1

This documentation was generated from Cloud Machine Learning Engine crate version 1.0.14+20200703, where 20200703 is the exact revision of the ml:v1 schema built by the mako code generator v1.0.14.

Everything else about the Cloud Machine Learning Engine v1 API can be found at the official documentation site. The original source code is on github.

Features

Handle the following Resources with ease from the central hub ...

projects
explain, get config, jobs cancel, jobs create, jobs get, jobs get iam policy, jobs list, jobs patch, jobs set iam policy, jobs test iam permissions, locations get, locations list, locations operations cancel, locations operations get, locations studies create, locations studies delete, locations studies get, locations studies list, locations studies trials add measurement, locations studies trials check early stopping state, locations studies trials complete, locations studies trials create, locations studies trials delete, locations studies trials get, locations studies trials list, locations studies trials stop, locations studies trials suggest, models create, models delete, models get, models get iam policy, models list, models patch, models set iam policy, models test iam permissions, models versions create, models versions delete, models versions get, models versions list, models versions patch, models versions set default, operations cancel, operations get, operations list and predict

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Structure of this Library

The API is structured into the following primary items:

Hub
- a central object to maintain state and allow accessing all Activities
- creates Method Builders which in turn allow access to individual Call Builders
Resources
- primary types that you can apply Activities to
- a collection of properties and Parts
- Parts
  - a collection of properties
  - never directly used in Activities
Activities
- operations to apply to Resources

All structures are marked with applicable traits to further categorize them and ease browsing.

Generally speaking, you can invoke Activities like this:

let r = hub.resource().activity(...).doit()

Or specifically ...

let r = hub.projects().locations_studies_trials_suggest(...).doit()
let r = hub.projects().models_versions_create(...).doit()
let r = hub.projects().models_versions_patch(...).doit()
let r = hub.projects().operations_get(...).doit()
let r = hub.projects().models_versions_delete(...).doit()
let r = hub.projects().locations_operations_get(...).doit()
let r = hub.projects().locations_studies_trials_check_early_stopping_state(...).doit()
let r = hub.projects().models_delete(...).doit()
let r = hub.projects().models_patch(...).doit()

The resource() and activity(...) calls create builders. The second one dealing with Activities supports various methods to configure the impending operation (not shown here). It is made such that all required arguments have to be specified right away (i.e. (...)), whereas all optional ones can be build up as desired. The doit() method performs the actual communication with the server and returns the respective result.

Usage

Setting up your Project

To use this library, you would put the following lines into your Cargo.toml file:

[dependencies]
google-ml1 = "*"
# This project intentionally uses an old version of Hyper. See
# https://github.com/Byron/google-apis-rs/issues/173 for more
# information.
hyper = "^0.10"
hyper-rustls = "^0.6"
serde = "^1.0"
serde_json = "^1.0"
yup-oauth2 = "^1.0"

A complete example

extern crate hyper;
extern crate hyper_rustls;
extern crate yup_oauth2 as oauth2;
extern crate google_ml1 as ml1;
use ml1::GoogleCloudMlV1__Version;
use ml1::{Result, Error};
use std::default::Default;
use oauth2::{Authenticator, DefaultAuthenticatorDelegate, ApplicationSecret, MemoryStorage};
use ml1::CloudMachineLearningEngine;
 
// Get an ApplicationSecret instance by some means. It contains the `client_id` and 
// `client_secret`, among other things.
let secret: ApplicationSecret = Default::default();
// Instantiate the authenticator. It will choose a suitable authentication flow for you, 
// unless you replace  `None` with the desired Flow.
// Provide your own `AuthenticatorDelegate` to adjust the way it operates and get feedback about 
// what's going on. You probably want to bring in your own `TokenStorage` to persist tokens and
// retrieve them from storage.
let auth = Authenticator::new(&secret, DefaultAuthenticatorDelegate,
                              hyper::Client::with_connector(hyper::net::HttpsConnector::new(hyper_rustls::TlsClient::new())),
                              <MemoryStorage as Default>::default(), None);
let mut hub = CloudMachineLearningEngine::new(hyper::Client::with_connector(hyper::net::HttpsConnector::new(hyper_rustls::TlsClient::new())), auth);
// As the method needs a request, you would usually fill it with the desired information
// into the respective structure. Some of the parts shown here might not be applicable !
// Values shown here are possibly random and not representative !
let mut req = GoogleCloudMlV1__Version::default();
 
// You can configure optional parameters by calling the respective setters at will, and
// execute the final call using `doit()`.
// Values shown here are possibly random and not representative !
let result = hub.projects().models_versions_patch(req, "name")
             .update_mask("sed")
             .doit();
 
match result {
    Err(e) => match e {
        // The Error enum provides details about what exactly happened.
        // You can also just use its `Debug`, `Display` or `Error` traits
         Error::HttpError(_)
        |Error::MissingAPIKey
        |Error::MissingToken(_)
        |Error::Cancelled
        |Error::UploadSizeLimitExceeded(_, _)
        |Error::Failure(_)
        |Error::BadRequest(_)
        |Error::FieldClash(_)
        |Error::JsonDecodeError(_, _) => println!("{}", e),
    },
    Ok(res) => println!("Success: {:?}", res),
}

Handling Errors

All errors produced by the system are provided either as Result enumeration as return value of the doit() methods, or handed as possibly intermediate results to either the Hub Delegate, or the Authenticator Delegate.

When delegates handle errors or intermediate values, they may have a chance to instruct the system to retry. This makes the system potentially resilient to all kinds of errors.

Uploads and Downloads

If a method supports downloads, the response body, which is part of the Result, should be read by you to obtain the media. If such a method also supports a Response Result, it will return that by default. You can see it as meta-data for the actual media. To trigger a media download, you will have to set up the builder by making this call: .param("alt", "media").

Methods supporting uploads can do so using up to 2 different protocols: simple and resumable. The distinctiveness of each is represented by customized doit(...) methods, which are then named upload(...) and upload_resumable(...) respectively.

Customization and Callbacks

You may alter the way an doit() method is called by providing a delegate to the Method Builder before making the final doit() call. Respective methods will be called to provide progress information, as well as determine whether the system should retry on failure.

The delegate trait is default-implemented, allowing you to customize it with minimal effort.

Optional Parts in Server-Requests

All structures provided by this library are made to be encodable and decodable via json. Optionals are used to indicate that partial requests are responses are valid. Most optionals are are considered Parts which are identifiable by name, which will be sent to the server to indicate either the set parts of the request or the desired parts in the response.

Builder Arguments

Using method builders, you are able to prepare an action call by repeatedly calling it's methods. These will always take a single argument, for which the following statements are true.

PODs are handed by copy
strings are passed as &str
request values are moved

Arguments will always be copied or cloned into the builder, to make them independent of their original life times.

Structs

Chunk
CloudMachineLearningEngine	Central instance to access all CloudMachineLearningEngine related resource activities
ContentRange	Implements the Content-Range header, for serialization only
DefaultDelegate	A delegate with a conservative default implementation, which is used if no other delegate is set.
DummyNetworkStream
ErrorResponse	A utility to represent detailed errors we might see in case there are BadRequests. The latter happen if the sent parameters or request structures are unsound
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.
GoogleCloudMlV1__BuiltInAlgorithmOutput	Represents output related to a built-in algorithm Job.
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
GoogleCloudMlV1__ExplainRequest	Request for explanations to be issued against a trained model.
GoogleCloudMlV1__SetDefaultVersionRequest	Request message for the SetDefaultVersion request.
GoogleCloudMlV1__Measurement	A message representing a measurement.
GoogleCloudMlV1__ReplicaConfig	Represents the configuration for a replica in a cluster.
GoogleCloudMlV1_StudyConfig_MetricSpec	Represents a metric to optimize.
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.
GoogleCloudMlV1__StopTrialRequest	There is no detailed description.
GoogleCloudMlV1__PredictionOutput	Represents results of a prediction job.
GoogleCloudMlV1_StudyConfigParameterSpec_CategoricalValueSpec	There is no detailed description.
GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentIntValueSpec	Represents the spec to match integer values from parent parameter.
GoogleCloudMlV1_StudyConfigParameterSpec_IntegerValueSpec	There is no detailed description.
GoogleCloudMlV1__ParameterSpec	Represents a single hyperparameter to optimize.
GoogleCloudMlV1__CompleteTrialRequest	The request message for the CompleteTrial service method.
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.
GoogleCloudMlV1__ManualScaling	Options for manually scaling a model.
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.
GoogleCloudMlV1__ExplanationConfig	Message holding configuration options for explaining model predictions. There are two feature attribution methods supported for TensorFlow models: integrated gradients and sampled Shapley. Learn more about feature attributions.
GoogleCloudMlV1__CancelJobRequest	Request message for the CancelJob method.
GoogleCloudMlV1__EncryptionConfig	Represents a custom encryption key configuration that can be applied to a resource.
GoogleCloudMlV1__TrainingOutput	Represents results of a training job. Output only.
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.
GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentDiscreteValueSpec	Represents the spec to match discrete values from parent parameter.
GoogleCloudMlV1_AutomatedStoppingConfig_DecayCurveAutomatedStoppingConfig	There is no detailed description.
GoogleCloudMlV1_StudyConfig_ParameterSpec	Represents a single parameter to optimize.
GoogleCloudMlV1__PredictionInput	Represents input parameters for a prediction job.
GoogleCloudMlV1__Version	Represents a version of the model.
GoogleCloudMlV1__PredictRequest	Request for predictions to be issued against a trained model.
GoogleCloudMlV1__AutoScaling	Options for automatically scaling a model.
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.
GoogleCloudMlV1__ListJobsResponse	Response message for the ListJobs method.
GoogleCloudMlV1__Config	There is no detailed description.
GoogleCloudMlV1__ListVersionsResponse	Response message for the ListVersions method.
GoogleCloudMlV1__Location	There is no detailed description.
GoogleCloudMlV1__StudyConfig	Represents configuration of a study.
GoogleCloudMlV1_StudyConfigParameterSpec_MatchingParentCategoricalValueSpec	Represents the spec to match categorical values from parent parameter.
GoogleCloudMlV1__HyperparameterSpec	Represents a set of hyperparameters to optimize.
GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric	An observed value of a metric.
GoogleCloudMlV1_StudyConfigParameterSpec_DoubleValueSpec	There is no detailed description.
GoogleCloudMlV1__Job	Represents a training or prediction job.
GoogleCloudMlV1__Scheduling	All parameters related to scheduling of training jobs.
GoogleCloudMlV1__Capability	There is no detailed description.
GoogleCloudMlV1__ListModelsResponse	Response message for the ListModels method.
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.
GoogleCloudMlV1_StudyConfigParameterSpec_DiscreteValueSpec	There is no detailed description.
GoogleCloudMlV1__ListTrialsResponse	The response message for the ListTrials method.
GoogleCloudMlV1_Measurement_Metric	A message representing a metric in the measurement.
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.
GoogleCloudMlV1__AutomatedStoppingConfig	Configuration for Automated Early Stopping of Trials. If no implementation_config is set, automated early stopping will not be run.
GoogleCloudMlV1__CheckTrialEarlyStoppingStateRequest	The request message for the CheckTrialEarlyStoppingState service method.
GoogleCloudMlV1__Trial	A message representing a trial.
GoogleCloudMlV1__ListStudiesResponse	There is no detailed description.
GoogleCloudMlV1__AddTrialMeasurementRequest	The request message for the AddTrialMeasurement service method.
GoogleCloudMlV1__Model	Represents a machine learning solution.
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__ListLocationsResponse	There is no detailed description.
GoogleCloudMlV1__Study	A message representing a Study.
GoogleCloudMlV1__GetConfigResponse	Returns service account information associated with a project.
GoogleCloudMlV1__SuggestTrialsRequest	The request message for the SuggestTrial service method.
GoogleIamV1__SetIamPolicyRequest	Request message for `SetIamPolicy` method.
GoogleIamV1__Binding	Associates `members` with a `role`.
GoogleIamV1__Policy	An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.
GoogleIamV1__TestIamPermissionsRequest	Request message for `TestIamPermissions` method.
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.
GoogleIamV1__AuditLogConfig	Provides the configuration for logging a type of permissions. Example:
GoogleIamV1__TestIamPermissionsResponse	Response message for `TestIamPermissions` method.
GoogleLongrunning__ListOperationsResponse	The response message for Operations.ListOperations.
GoogleLongrunning__Operation	This resource represents a long-running operation that is the result of a network API call.
GoogleProtobuf__Empty	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:
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.
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.
JsonServerError	A utility type which can decode a server response that indicates error
MethodInfo	Contains information about an API request.
MultiPartReader	Provides a `Read` interface that converts multiple parts into the protocol identified by RFC2387. Note: This implementation is just as rich as it needs to be to perform uploads to google APIs, and might not be a fully-featured implementation.
ProjectExplainCall	Performs explanation on the data in the request.
ProjectGetConfigCall	Get the service account information associated with your project. You need this information in order to grant the service account permissions for the Google Cloud Storage location where you put your model training code for training the model with Google Cloud Machine Learning.
ProjectJobCancelCall	Cancels a running job.
ProjectJobCreateCall	Creates a training or a batch prediction job.
ProjectJobGetCall	Describes a job.
ProjectJobGetIamPolicyCall	Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.
ProjectJobListCall	Lists the jobs in the project.
ProjectJobPatchCall	Updates a specific job resource.
ProjectJobSetIamPolicyCall	Sets the access control policy on the specified resource. Replaces any existing policy.
ProjectJobTestIamPermissionCall	Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a `NOT_FOUND` error.
ProjectLocationGetCall	Get the complete list of CMLE capabilities in a location, along with their location-specific properties.
ProjectLocationListCall	List all locations that provides at least one type of CMLE capability.
ProjectLocationOperationCancelCall	Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`.
ProjectLocationOperationGetCall	Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
ProjectLocationStudyCreateCall	Creates a study.
ProjectLocationStudyDeleteCall	Deletes a study.
ProjectLocationStudyGetCall	Gets a study.
ProjectLocationStudyListCall	Lists all the studies in a region for an associated project.
ProjectLocationStudyTrialAddMeasurementCall	Adds a measurement of the objective metrics to a trial. This measurement is assumed to have been taken before the trial is complete.
ProjectLocationStudyTrialCheckEarlyStoppingStateCall	Checks whether a trial should stop or not. Returns a long-running operation. When the operation is successful, it will contain a CheckTrialEarlyStoppingStateResponse.
ProjectLocationStudyTrialCompleteCall	Marks a trial as complete.
ProjectLocationStudyTrialCreateCall	Adds a user provided trial to a study.
ProjectLocationStudyTrialDeleteCall	Deletes a trial.
ProjectLocationStudyTrialGetCall	Gets a trial.
ProjectLocationStudyTrialListCall	Lists the trials associated with a study.
ProjectLocationStudyTrialStopCall	Stops a trial.
ProjectLocationStudyTrialSuggestCall	Adds one or more trials to a study, with parameter values suggested by AI Platform Optimizer. Returns a long-running operation associated with the generation of trial suggestions. When this long-running operation succeeds, it will contain a SuggestTrialsResponse.
ProjectMethods	A builder providing access to all methods supported on project resources. It is not used directly, but through the `CloudMachineLearningEngine` hub.
ProjectModelCreateCall	Creates a model which will later contain one or more versions.
ProjectModelDeleteCall	Deletes a model.
ProjectModelGetCall	Gets information about a model, including its name, the description (if set), and the default version (if at least one version of the model has been deployed).
ProjectModelGetIamPolicyCall	Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.
ProjectModelListCall	Lists the models in a project.
ProjectModelPatchCall	Updates a specific model resource.
ProjectModelSetIamPolicyCall	Sets the access control policy on the specified resource. Replaces any existing policy.
ProjectModelTestIamPermissionCall	Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a `NOT_FOUND` error.
ProjectModelVersionCreateCall	Creates a new version of a model from a trained TensorFlow model.
ProjectModelVersionDeleteCall	Deletes a model version.
ProjectModelVersionGetCall	Gets information about a model version.
ProjectModelVersionListCall	Gets basic information about all the versions of a model.
ProjectModelVersionPatchCall	Updates the specified Version resource.
ProjectModelVersionSetDefaultCall	Designates a version to be the default for the model.
ProjectOperationCancelCall	Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`.
ProjectOperationGetCall	Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
ProjectOperationListCall	Lists operations that match the specified filter in the request. If the server doesn't support this method, it returns `UNIMPLEMENTED`.
ProjectPredictCall	Performs online prediction on the data in the request.
RangeResponseHeader
ResumableUploadHelper	A utility type to perform a resumable upload from start to end.
ServerError
ServerMessage
XUploadContentType	The `X-Upload-Content-Type` header.

Enums

Error
Scope	Identifies the an OAuth2 authorization scope. A scope is needed when requesting an authorization token.

Traits

CallBuilder	Identifies types which represent builders for a particular resource method
Delegate	A trait specifying functionality to help controlling any request performed by the API. The trait has a conservative default implementation.
Hub	Identifies the Hub. There is only one per library, this trait is supposed to make intended use more explicit. The hub allows to access all resource methods more easily.
MethodsBuilder	Identifies types for building methods of a particular resource type
NestedType	Identifies types which are only used by other types internally. They have no special meaning, this trait just marks them for completeness.
Part	Identifies types which are only used as part of other types, which usually are carrying the `Resource` trait.
ReadSeek	A utility to specify reader types which provide seeking capabilities too
RequestValue	Identifies types which are used in API requests.
Resource	Identifies types which can be inserted and deleted. Types with this trait are most commonly used by clients of this API.
ResponseResult	Identifies types which are used in API responses.
ToParts	A trait for all types that can convert themselves into a parts string
UnusedType	Identifies types which are not actually used by the API This might be a bug within the google API schema.

Functions

remove_json_null_values

Type Definitions

Result

A universal result type used as return for all calls.