Struct google_api_proto::google::cloud::aiplatform::v1beta1::pipeline_service_client::PipelineServiceClient

pub struct PipelineServiceClient<T> { /* private fields */ }

A service for creating and managing Vertex AI’s pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

Implementations

impl<T> PipelineServiceClient<T>

where T: GrpcService<BoxBody>, T::Error: Into<StdError>, T::ResponseBody: Body<Data = Bytes> + Send + 'static, <T::ResponseBody as Body>::Error: Into<StdError> + Send,

pub fn new(inner: T) -> Self

pub fn with_origin(inner: T, origin: Uri) -> Self

pub fn with_interceptor<F>( inner: T, interceptor: F, ) -> PipelineServiceClient<InterceptedService<T, F>>

where F: Interceptor, T::ResponseBody: Default, T: Service<Request<BoxBody>, Response = Response<<T as GrpcService<BoxBody>>::ResponseBody>>, <T as Service<Request<BoxBody>>>::Error: Into<StdError> + Send + Sync,

pub fn send_compressed(self, encoding: CompressionEncoding) -> Self

Compress requests with the given encoding.

This requires the server to support it otherwise it might respond with an error.

pub fn accept_compressed(self, encoding: CompressionEncoding) -> Self

Enable decompressing responses.

pub fn max_decoding_message_size(self, limit: usize) -> Self

Limits the maximum size of a decoded message.

Default: 4MB

pub fn max_encoding_message_size(self, limit: usize) -> Self

Limits the maximum size of an encoded message.

Default: usize::MAX

pub async fn create_training_pipeline( &mut self, request: impl IntoRequest<CreateTrainingPipelineRequest>, ) -> Result<Response<TrainingPipeline>, Status>

Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run.

pub async fn get_training_pipeline( &mut self, request: impl IntoRequest<GetTrainingPipelineRequest>, ) -> Result<Response<TrainingPipeline>, Status>

Gets a TrainingPipeline.

pub async fn list_training_pipelines( &mut self, request: impl IntoRequest<ListTrainingPipelinesRequest>, ) -> Result<Response<ListTrainingPipelinesResponse>, Status>

Lists TrainingPipelines in a Location.

pub async fn delete_training_pipeline( &mut self, request: impl IntoRequest<DeleteTrainingPipelineRequest>, ) -> Result<Response<Operation>, Status>

Deletes a TrainingPipeline.

pub async fn cancel_training_pipeline( &mut self, request: impl IntoRequest<CancelTrainingPipelineRequest>, ) -> Result<Response<()>, Status>

Cancels a TrainingPipeline. Starts asynchronous cancellation on the TrainingPipeline. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use [PipelineService.GetTrainingPipeline][google.cloud.aiplatform.v1beta1.PipelineService.GetTrainingPipeline] or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the TrainingPipeline is not deleted; instead it becomes a pipeline with a [TrainingPipeline.error][google.cloud.aiplatform.v1beta1.TrainingPipeline.error] value with a [google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and [TrainingPipeline.state][google.cloud.aiplatform.v1beta1.TrainingPipeline.state] is set to CANCELLED.

pub async fn create_pipeline_job( &mut self, request: impl IntoRequest<CreatePipelineJobRequest>, ) -> Result<Response<PipelineJob>, Status>

Creates a PipelineJob. A PipelineJob will run immediately when created.

pub async fn get_pipeline_job( &mut self, request: impl IntoRequest<GetPipelineJobRequest>, ) -> Result<Response<PipelineJob>, Status>

Gets a PipelineJob.

pub async fn list_pipeline_jobs( &mut self, request: impl IntoRequest<ListPipelineJobsRequest>, ) -> Result<Response<ListPipelineJobsResponse>, Status>

Lists PipelineJobs in a Location.

pub async fn delete_pipeline_job( &mut self, request: impl IntoRequest<DeletePipelineJobRequest>, ) -> Result<Response<Operation>, Status>

Deletes a PipelineJob.

pub async fn batch_delete_pipeline_jobs( &mut self, request: impl IntoRequest<BatchDeletePipelineJobsRequest>, ) -> Result<Response<Operation>, Status>

Batch deletes PipelineJobs The Operation is atomic. If it fails, none of the PipelineJobs are deleted. If it succeeds, all of the PipelineJobs are deleted.

pub async fn cancel_pipeline_job( &mut self, request: impl IntoRequest<CancelPipelineJobRequest>, ) -> Result<Response<()>, Status>

Cancels a PipelineJob. Starts asynchronous cancellation on the PipelineJob. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use [PipelineService.GetPipelineJob][google.cloud.aiplatform.v1beta1.PipelineService.GetPipelineJob] or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the PipelineJob is not deleted; instead it becomes a pipeline with a [PipelineJob.error][google.cloud.aiplatform.v1beta1.PipelineJob.error] value with a [google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and [PipelineJob.state][google.cloud.aiplatform.v1beta1.PipelineJob.state] is set to CANCELLED.

pub async fn batch_cancel_pipeline_jobs( &mut self, request: impl IntoRequest<BatchCancelPipelineJobsRequest>, ) -> Result<Response<Operation>, Status>

Batch cancel PipelineJobs. Firstly the server will check if all the jobs are in non-terminal states, and skip the jobs that are already terminated. If the operation failed, none of the pipeline jobs are cancelled. The server will poll the states of all the pipeline jobs periodically to check the cancellation status. This operation will return an LRO.

Trait Implementations

impl<T: Clone> Clone for PipelineServiceClient<T>

fn clone(&self) -> PipelineServiceClient<T>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug> Debug for PipelineServiceClient<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.