Struct TensorboardService

Source

pub struct TensorboardService { /* private fields */ }

Available on crate feature tensorboard-service only.

Expand description

Implements a client for the Vertex AI API.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
    let client = TensorboardService::builder().build().await?;
    let parent = "parent_value";
    let mut list = client.list_tensorboards()
        .set_parent(parent)
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }

§Service Description

TensorboardService

§Configuration

To configure TensorboardService use the with_* methods in the type returned by builder(). The default configuration should work for most applications. Common configuration changes include

with_endpoint(): by default this client uses the global default endpoint (https://aiplatform.googleapis.com). Applications using regional endpoints or running in restricted networks (e.g. a network configured override this default.
with_credentials(): by default this client uses Application Default Credentials. Applications using custom authentication may need to override this default.

§Pooling and Cloning

TensorboardService holds a connection pool internally, it is advised to create one and reuse it. You do not need to wrap TensorboardService in an Rc or Arc to reuse it, because it already uses an Arc internally.

Implementations§

Source §

impl TensorboardService

Source

pub fn builder() -> ClientBuilder

Returns a builder for TensorboardService.

let client = TensorboardService::builder().build().await?;

Source

pub fn from_stub<T>(stub: T) -> Self
where T: TensorboardService + 'static,

Creates a new client from the provided stub.

The most common case for calling this function is in tests mocking the client’s behavior.

Source

pub fn create_tensorboard(&self) -> CreateTensorboard

Creates a Tensorboard.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_aiplatform_v1::model::Tensorboard;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let response = client.create_tensorboard()
        .set_parent(parent)
        .set_tensorboard(
            Tensorboard::new()/* set fields */
        )
        .poller().until_done().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn get_tensorboard(&self) -> GetTensorboard

Gets a Tensorboard.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.get_tensorboard()
        .set_name(name)
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn update_tensorboard(&self) -> UpdateTensorboard

Updates a Tensorboard.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_wkt::FieldMask;
use google_cloud_aiplatform_v1::model::Tensorboard;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.update_tensorboard()
        .set_tensorboard(
            Tensorboard::new().set_name(name)/* set fields */
        )
        .set_update_mask(FieldMask::default().set_paths(["updated.field.path1", "updated.field.path2"]))
        .poller().until_done().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn list_tensorboards(&self) -> ListTensorboards

Lists Tensorboards in a Location.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let mut list = client.list_tensorboards()
        .set_parent(parent)
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn delete_tensorboard(&self) -> DeleteTensorboard

Deletes a Tensorboard.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    client.delete_tensorboard()
        .set_name(name)
        .poller().until_done().await?;
    Ok(())
}

Source

pub fn read_tensorboard_usage(&self) -> ReadTensorboardUsage

Returns a list of monthly active users for a given TensorBoard instance.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.read_tensorboard_usage()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn read_tensorboard_size(&self) -> ReadTensorboardSize

Returns the storage size for a given TensorBoard instance.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.read_tensorboard_size()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn create_tensorboard_experiment(&self) -> CreateTensorboardExperiment

Creates a TensorboardExperiment.

§Example

use google_cloud_aiplatform_v1::model::TensorboardExperiment;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let response = client.create_tensorboard_experiment()
        .set_parent(parent)
        .set_tensorboard_experiment(
            TensorboardExperiment::new()/* set fields */
        )
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn get_tensorboard_experiment(&self) -> GetTensorboardExperiment

Gets a TensorboardExperiment.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.get_tensorboard_experiment()
        .set_name(name)
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn update_tensorboard_experiment(&self) -> UpdateTensorboardExperiment

Updates a TensorboardExperiment.

§Example

use google_cloud_wkt::FieldMask;
use google_cloud_aiplatform_v1::model::TensorboardExperiment;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.update_tensorboard_experiment()
        .set_tensorboard_experiment(
            TensorboardExperiment::new().set_name(name)/* set fields */
        )
        .set_update_mask(FieldMask::default().set_paths(["updated.field.path1", "updated.field.path2"]))
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn list_tensorboard_experiments(&self) -> ListTensorboardExperiments

Lists TensorboardExperiments in a Location.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let mut list = client.list_tensorboard_experiments()
        .set_parent(parent)
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn delete_tensorboard_experiment(&self) -> DeleteTensorboardExperiment

Deletes a TensorboardExperiment.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    client.delete_tensorboard_experiment()
        .set_name(name)
        .poller().until_done().await?;
    Ok(())
}

Source

pub fn create_tensorboard_run(&self) -> CreateTensorboardRun

Creates a TensorboardRun.

§Example

use google_cloud_aiplatform_v1::model::TensorboardRun;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let response = client.create_tensorboard_run()
        .set_parent(parent)
        .set_tensorboard_run(
            TensorboardRun::new()/* set fields */
        )
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn batch_create_tensorboard_runs(&self) -> BatchCreateTensorboardRuns

Batch create TensorboardRuns.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.batch_create_tensorboard_runs()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn get_tensorboard_run(&self) -> GetTensorboardRun

Gets a TensorboardRun.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.get_tensorboard_run()
        .set_name(name)
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn update_tensorboard_run(&self) -> UpdateTensorboardRun

Updates a TensorboardRun.

§Example

use google_cloud_wkt::FieldMask;
use google_cloud_aiplatform_v1::model::TensorboardRun;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.update_tensorboard_run()
        .set_tensorboard_run(
            TensorboardRun::new().set_name(name)/* set fields */
        )
        .set_update_mask(FieldMask::default().set_paths(["updated.field.path1", "updated.field.path2"]))
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn list_tensorboard_runs(&self) -> ListTensorboardRuns

Lists TensorboardRuns in a Location.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let mut list = client.list_tensorboard_runs()
        .set_parent(parent)
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn delete_tensorboard_run(&self) -> DeleteTensorboardRun

Deletes a TensorboardRun.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    client.delete_tensorboard_run()
        .set_name(name)
        .poller().until_done().await?;
    Ok(())
}

Source

pub fn batch_create_tensorboard_time_series( &self, ) -> BatchCreateTensorboardTimeSeries

Batch create TensorboardTimeSeries that belong to a TensorboardExperiment.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.batch_create_tensorboard_time_series()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn create_tensorboard_time_series(&self) -> CreateTensorboardTimeSeries

Creates a TensorboardTimeSeries.

§Example

use google_cloud_aiplatform_v1::model::TensorboardTimeSeries;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let response = client.create_tensorboard_time_series()
        .set_parent(parent)
        .set_tensorboard_time_series(
            TensorboardTimeSeries::new()/* set fields */
        )
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn get_tensorboard_time_series(&self) -> GetTensorboardTimeSeries

Gets a TensorboardTimeSeries.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.get_tensorboard_time_series()
        .set_name(name)
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn update_tensorboard_time_series(&self) -> UpdateTensorboardTimeSeries

Updates a TensorboardTimeSeries.

§Example

use google_cloud_wkt::FieldMask;
use google_cloud_aiplatform_v1::model::TensorboardTimeSeries;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    let response = client.update_tensorboard_time_series()
        .set_tensorboard_time_series(
            TensorboardTimeSeries::new().set_name(name)/* set fields */
        )
        .set_update_mask(FieldMask::default().set_paths(["updated.field.path1", "updated.field.path2"]))
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn list_tensorboard_time_series(&self) -> ListTensorboardTimeSeries

Lists TensorboardTimeSeries in a Location.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, parent: &str
) -> Result<()> {
    let mut list = client.list_tensorboard_time_series()
        .set_parent(parent)
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn delete_tensorboard_time_series(&self) -> DeleteTensorboardTimeSeries

Deletes a TensorboardTimeSeries.

§Long running operations

This method is used to start, and/or poll a long-running Operation. The Working with long-running operations chapter in the user guide covers these operations in detail.

§Example

use google_cloud_lro::Poller;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService, name: &str
) -> Result<()> {
    client.delete_tensorboard_time_series()
        .set_name(name)
        .poller().until_done().await?;
    Ok(())
}

Source

pub fn batch_read_tensorboard_time_series_data( &self, ) -> BatchReadTensorboardTimeSeriesData

Reads multiple TensorboardTimeSeries’ data. The data point number limit is 1000 for scalars, 100 for tensors and blob references. If the number of data points stored is less than the limit, all data is returned. Otherwise, the number limit of data points is randomly selected from this time series and returned.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.batch_read_tensorboard_time_series_data()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn read_tensorboard_time_series_data(&self) -> ReadTensorboardTimeSeriesData

Reads a TensorboardTimeSeries’ data. By default, if the number of data points stored is less than 1000, all data is returned. Otherwise, 1000 data points is randomly selected from this time series and returned. This value can be changed by changing max_data_points, which can’t be greater than 10k.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.read_tensorboard_time_series_data()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

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pub fn write_tensorboard_experiment_data( &self, ) -> WriteTensorboardExperimentData

Write time series data points of multiple TensorboardTimeSeries in multiple TensorboardRun’s. If any data fail to be ingested, an error is returned.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.write_tensorboard_experiment_data()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn write_tensorboard_run_data(&self) -> WriteTensorboardRunData

Write time series data points into multiple TensorboardTimeSeries under a TensorboardRun. If any data fail to be ingested, an error is returned.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.write_tensorboard_run_data()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn export_tensorboard_time_series_data( &self, ) -> ExportTensorboardTimeSeriesData

Exports a TensorboardTimeSeries’ data. Data is returned in paginated responses.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let mut list = client.export_tensorboard_time_series_data()
        /* set fields */
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn list_locations(&self) -> ListLocations

Lists information about the supported locations for this service.

§Example

use google_cloud_gax::paginator::ItemPaginator as _;
use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let mut list = client.list_locations()
        /* set fields */
        .by_item();
    while let Some(item) = list.next().await.transpose()? {
        println!("{:?}", item);
    }
    Ok(())
}

Source

pub fn get_location(&self) -> GetLocation

Gets information about a location.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.get_location()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn set_iam_policy(&self) -> SetIamPolicy

Sets the access control policy on the specified resource. Replaces any existing policy.

Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.set_iam_policy()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn get_iam_policy(&self) -> GetIamPolicy

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

§Example

use google_cloud_aiplatform_v1::Result;
async fn sample(
   client: &TensorboardService
) -> Result<()> {
    let response = client.get_iam_policy()
        /* set fields */
        .send().await?;
    println!("response {:?}", response);
    Ok(())
}

Source

pub fn test_iam_permissions(&self) -> TestIamPermissions

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.

Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may “fail open” without warning.