TensorboardService v1
Package
@google-cloud/aiplatformConstructors
(constructor)(opts)
constructor(opts?: ClientOptions);
Construct an instance of TensorboardServiceClient.
Name | Description |
opts |
ClientOptions
|
Properties
apiEndpoint
static get apiEndpoint(): string;
The DNS address for this API service - same as servicePath(), exists for compatibility reasons.
auth
auth: gax.GoogleAuth;
descriptors
descriptors: Descriptors;
innerApiCalls
innerApiCalls: {
[name: string]: Function;
};
operationsClient
operationsClient: gax.OperationsClient;
pathTemplates
pathTemplates: {
[name: string]: gax.PathTemplate;
};
port
static get port(): number;
The port for this API service.
scopes
static get scopes(): string[];
The scopes needed to make gRPC calls for every method defined in this service.
servicePath
static get servicePath(): string;
The DNS address for this API service.
tensorboardServiceStub
tensorboardServiceStub?: Promise<{
[name: string]: Function;
}>;
warn
warn: (code: string, message: string, warnType?: string) => void;
Methods
annotationPath(project, location, dataset, dataItem, annotation)
annotationPath(project: string, location: string, dataset: string, dataItem: string, annotation: string): string;
Return a fully-qualified annotation resource name string.
Name | Description |
project |
string
|
location |
string
|
dataset |
string
|
dataItem |
string
|
annotation |
string
|
Type | Description |
string | {string} Resource name string. |
annotationSpecPath(project, location, dataset, annotationSpec)
annotationSpecPath(project: string, location: string, dataset: string, annotationSpec: string): string;
Return a fully-qualified annotationSpec resource name string.
Name | Description |
project |
string
|
location |
string
|
dataset |
string
|
annotationSpec |
string
|
Type | Description |
string | {string} Resource name string. |
artifactPath(project, location, metadataStore, artifact)
artifactPath(project: string, location: string, metadataStore: string, artifact: string): string;
Return a fully-qualified artifact resource name string.
Name | Description |
project |
string
|
location |
string
|
metadataStore |
string
|
artifact |
string
|
Type | Description |
string | {string} Resource name string. |
batchCreateTensorboardRuns(request, options)
batchCreateTensorboardRuns(request?: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse,
(protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | undefined),
{} | undefined
]>;
Batch create TensorboardRuns.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse, (protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [BatchCreateTensorboardRunsResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardExperiment to create the
* TensorboardRuns in. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
* The parent field in the CreateTensorboardRunRequest messages must match
* this field.
*/
// const parent = 'abc123'
/**
* Required. The request message specifying the TensorboardRuns to create.
* A maximum of 1000 TensorboardRuns can be created in a batch.
*/
// const requests = 1234
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callBatchCreateTensorboardRuns() {
// Construct request
const request = {
parent,
requests,
};
// Run request
const response = await aiplatformClient.batchCreateTensorboardRuns(request);
console.log(response);
}
callBatchCreateTensorboardRuns();
batchCreateTensorboardRuns(request, options, callback)
batchCreateTensorboardRuns(request: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
batchCreateTensorboardRuns(request, callback)
batchCreateTensorboardRuns(request: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardRunsRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
batchCreateTensorboardTimeSeries(request, options)
batchCreateTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse,
(protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | undefined),
{} | undefined
]>;
Batch create TensorboardTimeSeries that belong to a TensorboardExperiment.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse, (protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [BatchCreateTensorboardTimeSeriesResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardExperiment to create the
* TensorboardTimeSeries in.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
* The TensorboardRuns referenced by the parent fields in the
* CreateTensorboardTimeSeriesRequest messages must be sub resources of this
* TensorboardExperiment.
*/
// const parent = 'abc123'
/**
* Required. The request message specifying the TensorboardTimeSeries to create.
* A maximum of 1000 TensorboardTimeSeries can be created in a batch.
*/
// const requests = 1234
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callBatchCreateTensorboardTimeSeries() {
// Construct request
const request = {
parent,
requests,
};
// Run request
const response = await aiplatformClient.batchCreateTensorboardTimeSeries(request);
console.log(response);
}
callBatchCreateTensorboardTimeSeries();
batchCreateTensorboardTimeSeries(request, options, callback)
batchCreateTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
batchCreateTensorboardTimeSeries(request, callback)
batchCreateTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesResponse, protos.google.cloud.aiplatform.v1.IBatchCreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
batchPredictionJobPath(project, location, batchPredictionJob)
batchPredictionJobPath(project: string, location: string, batchPredictionJob: string): string;
Return a fully-qualified batchPredictionJob resource name string.
Name | Description |
project |
string
|
location |
string
|
batchPredictionJob |
string
|
Type | Description |
string | {string} Resource name string. |
batchReadTensorboardTimeSeriesData(request, options)
batchReadTensorboardTimeSeriesData(request?: protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse,
(protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | undefined),
{} | undefined
]>;
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 will be returned. Otherwise, that limit number of data points will be randomly selected from this time series and returned.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse, (protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [BatchReadTensorboardTimeSeriesDataResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Tensorboard containing TensorboardTimeSeries to
* read data from. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`.
* The TensorboardTimeSeries referenced by time_series google.cloud.aiplatform.v1.BatchReadTensorboardTimeSeriesDataRequest.time_series must be sub
* resources of this Tensorboard.
*/
// const tensorboard = 'abc123'
/**
* Required. The resource names of the TensorboardTimeSeries to read data from. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const timeSeries = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callBatchReadTensorboardTimeSeriesData() {
// Construct request
const request = {
tensorboard,
timeSeries,
};
// Run request
const response = await aiplatformClient.batchReadTensorboardTimeSeriesData(request);
console.log(response);
}
callBatchReadTensorboardTimeSeriesData();
batchReadTensorboardTimeSeriesData(request, options, callback)
batchReadTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
batchReadTensorboardTimeSeriesData(request, callback)
batchReadTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IBatchReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
checkCreateTensorboardProgress(name)
checkCreateTensorboardProgress(name: string): Promise<LROperation<protos.google.cloud.aiplatform.v1.Tensorboard, protos.google.cloud.aiplatform.v1.CreateTensorboardOperationMetadata>>;
Check the status of the long running operation returned by createTensorboard()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.cloud.aiplatform.v1.Tensorboard, protos.google.cloud.aiplatform.v1.CreateTensorboardOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Location to create the Tensorboard in.
* Format: `projects/{project}/locations/{location}`
*/
// const parent = 'abc123'
/**
* Required. The Tensorboard to create.
*/
// const tensorboard = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callCreateTensorboard() {
// Construct request
const request = {
parent,
tensorboard,
};
// Run request
const [operation] = await aiplatformClient.createTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callCreateTensorboard();
checkDeleteTensorboardExperimentProgress(name)
checkDeleteTensorboardExperimentProgress(name: string): Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>>;
Check the status of the long running operation returned by deleteTensorboardExperiment()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardExperiment to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardExperiment() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardExperiment(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardExperiment();
checkDeleteTensorboardProgress(name)
checkDeleteTensorboardProgress(name: string): Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>>;
Check the status of the long running operation returned by deleteTensorboard()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the Tensorboard to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboard() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboard();
checkDeleteTensorboardRunProgress(name)
checkDeleteTensorboardRunProgress(name: string): Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>>;
Check the status of the long running operation returned by deleteTensorboardRun()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardRun to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardRun() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardRun(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardRun();
checkDeleteTensorboardTimeSeriesProgress(name)
checkDeleteTensorboardTimeSeriesProgress(name: string): Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>>;
Check the status of the long running operation returned by deleteTensorboardTimeSeries()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.protobuf.Empty, protos.google.cloud.aiplatform.v1.DeleteOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardTimeSeries to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardTimeSeries() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardTimeSeries(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardTimeSeries();
checkUpdateTensorboardProgress(name)
checkUpdateTensorboardProgress(name: string): Promise<LROperation<protos.google.cloud.aiplatform.v1.Tensorboard, protos.google.cloud.aiplatform.v1.UpdateTensorboardOperationMetadata>>;
Check the status of the long running operation returned by updateTensorboard()
.
Name | Description |
name |
string
The operation name that will be passed. |
Type | Description |
Promise<LROperation<protos.google.cloud.aiplatform.v1.Tensorboard, protos.google.cloud.aiplatform.v1.UpdateTensorboardOperationMetadata>> | {Promise} - The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#long-running-operations) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. Field mask is used to specify the fields to be overwritten in the
* Tensorboard resource by the update.
* The fields specified in the update_mask are relative to the resource, not
* the full request. A field will be overwritten if it is in the mask. If the
* user does not provide a mask then all fields will be overwritten if new
* values are specified.
*/
// const updateMask = {}
/**
* Required. The Tensorboard's `name` field is used to identify the
* Tensorboard to be updated. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const tensorboard = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callUpdateTensorboard() {
// Construct request
const request = {
updateMask,
tensorboard,
};
// Run request
const [operation] = await aiplatformClient.updateTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callUpdateTensorboard();
close()
close(): Promise<void>;
Terminate the gRPC channel and close the client.
The client will no longer be usable and all future behavior is undefined.
Type | Description |
Promise<void> | {Promise} A promise that resolves when the client is closed. |
contextPath(project, location, metadataStore, context)
contextPath(project: string, location: string, metadataStore: string, context: string): string;
Return a fully-qualified context resource name string.
Name | Description |
project |
string
|
location |
string
|
metadataStore |
string
|
context |
string
|
Type | Description |
string | {string} Resource name string. |
createTensorboard(request, options)
createTensorboard(request?: protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest, options?: CallOptions): Promise<[
LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Creates a Tensorboard.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Location to create the Tensorboard in.
* Format: `projects/{project}/locations/{location}`
*/
// const parent = 'abc123'
/**
* Required. The Tensorboard to create.
*/
// const tensorboard = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callCreateTensorboard() {
// Construct request
const request = {
parent,
tensorboard,
};
// Run request
const [operation] = await aiplatformClient.createTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callCreateTensorboard();
createTensorboard(request, options, callback)
createTensorboard(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest, options: CallOptions, callback: Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboard(request, callback)
createTensorboard(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest, callback: Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRequest
|
callback |
Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.ICreateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardExperiment(request, options)
createTensorboardExperiment(request?: protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardExperiment,
(protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | undefined),
{} | undefined
]>;
Creates a TensorboardExperiment.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardExperiment, (protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardExperiment]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Tensorboard to create the TensorboardExperiment
* in. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const parent = 'abc123'
/**
* The TensorboardExperiment to create.
*/
// const tensorboardExperiment = {}
/**
* Required. The ID to use for the Tensorboard experiment, which will become the final
* component of the Tensorboard experiment's resource name.
* This value should be 1-128 characters, and valid characters
* are /[a-z][0-9]-/.
*/
// const tensorboardExperimentId = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callCreateTensorboardExperiment() {
// Construct request
const request = {
parent,
tensorboardExperimentId,
};
// Run request
const response = await aiplatformClient.createTensorboardExperiment(request);
console.log(response);
}
callCreateTensorboardExperiment();
createTensorboardExperiment(request, options, callback)
createTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardExperiment(request, callback)
createTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.ICreateTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardRun(request, options)
createTensorboardRun(request?: protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardRun,
(protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | undefined),
{} | undefined
]>;
Creates a TensorboardRun.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardRun, (protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardRun]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardExperiment to create the TensorboardRun
* in. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const parent = 'abc123'
/**
* Required. The TensorboardRun to create.
*/
// const tensorboardRun = {}
/**
* Required. The ID to use for the Tensorboard run, which will become the final
* component of the Tensorboard run's resource name.
* This value should be 1-128 characters, and valid characters
* are /[a-z][0-9]-/.
*/
// const tensorboardRunId = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callCreateTensorboardRun() {
// Construct request
const request = {
parent,
tensorboardRun,
tensorboardRunId,
};
// Run request
const response = await aiplatformClient.createTensorboardRun(request);
console.log(response);
}
callCreateTensorboardRun();
createTensorboardRun(request, options, callback)
createTensorboardRun(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardRun(request, callback)
createTensorboardRun(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.ICreateTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardTimeSeries(request, options)
createTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries,
(protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | undefined),
{} | undefined
]>;
Creates a TensorboardTimeSeries.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, (protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardTimeSeries]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardRun to create the
* TensorboardTimeSeries in.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const parent = 'abc123'
/**
* Optional. The user specified unique ID to use for the TensorboardTimeSeries, which
* will become the final component of the TensorboardTimeSeries's resource
* name.
* This value should match "[a-z0-9][a-z0-9-]{0, 127}"
*/
// const tensorboardTimeSeriesId = 'abc123'
/**
* Required. The TensorboardTimeSeries to create.
*/
// const tensorboardTimeSeries = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callCreateTensorboardTimeSeries() {
// Construct request
const request = {
parent,
tensorboardTimeSeries,
};
// Run request
const response = await aiplatformClient.createTensorboardTimeSeries(request);
console.log(response);
}
callCreateTensorboardTimeSeries();
createTensorboardTimeSeries(request, options, callback)
createTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
createTensorboardTimeSeries(request, callback)
createTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.ICreateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
customJobPath(project, location, customJob)
customJobPath(project: string, location: string, customJob: string): string;
Return a fully-qualified customJob resource name string.
Name | Description |
project |
string
|
location |
string
|
customJob |
string
|
Type | Description |
string | {string} Resource name string. |
dataItemPath(project, location, dataset, dataItem)
dataItemPath(project: string, location: string, dataset: string, dataItem: string): string;
Return a fully-qualified dataItem resource name string.
Name | Description |
project |
string
|
location |
string
|
dataset |
string
|
dataItem |
string
|
Type | Description |
string | {string} Resource name string. |
dataLabelingJobPath(project, location, dataLabelingJob)
dataLabelingJobPath(project: string, location: string, dataLabelingJob: string): string;
Return a fully-qualified dataLabelingJob resource name string.
Name | Description |
project |
string
|
location |
string
|
dataLabelingJob |
string
|
Type | Description |
string | {string} Resource name string. |
datasetPath(project, location, dataset)
datasetPath(project: string, location: string, dataset: string): string;
Return a fully-qualified dataset resource name string.
Name | Description |
project |
string
|
location |
string
|
dataset |
string
|
Type | Description |
string | {string} Resource name string. |
deleteTensorboard(request, options)
deleteTensorboard(request?: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest, options?: CallOptions): Promise<[
LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Deletes a Tensorboard.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the Tensorboard to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboard() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboard();
deleteTensorboard(request, options, callback)
deleteTensorboard(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest, options: CallOptions, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboard(request, callback)
deleteTensorboard(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRequest
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardExperiment(request, options)
deleteTensorboardExperiment(request?: protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest, options?: CallOptions): Promise<[
LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Deletes a TensorboardExperiment.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardExperiment to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardExperiment() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardExperiment(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardExperiment();
deleteTensorboardExperiment(request, options, callback)
deleteTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest, options: CallOptions, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardExperiment(request, callback)
deleteTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardExperimentRequest
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardRun(request, options)
deleteTensorboardRun(request?: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest, options?: CallOptions): Promise<[
LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Deletes a TensorboardRun.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardRun to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardRun() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardRun(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardRun();
deleteTensorboardRun(request, options, callback)
deleteTensorboardRun(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest, options: CallOptions, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardRun(request, callback)
deleteTensorboardRun(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardRunRequest
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardTimeSeries(request, options)
deleteTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Deletes a TensorboardTimeSeries.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardTimeSeries to be deleted.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callDeleteTensorboardTimeSeries() {
// Construct request
const request = {
name,
};
// Run request
const [operation] = await aiplatformClient.deleteTensorboardTimeSeries(request);
const [response] = await operation.promise();
console.log(response);
}
callDeleteTensorboardTimeSeries();
deleteTensorboardTimeSeries(request, options, callback)
deleteTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest, options: CallOptions, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
deleteTensorboardTimeSeries(request, callback)
deleteTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest, callback: Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IDeleteTensorboardTimeSeriesRequest
|
callback |
Callback<LROperation<protos.google.protobuf.IEmpty, protos.google.cloud.aiplatform.v1.IDeleteOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
endpointPath(project, location, endpoint)
endpointPath(project: string, location: string, endpoint: string): string;
Return a fully-qualified endpoint resource name string.
Name | Description |
project |
string
|
location |
string
|
endpoint |
string
|
Type | Description |
string | {string} Resource name string. |
entityTypePath(project, location, featurestore, entityType)
entityTypePath(project: string, location: string, featurestore: string, entityType: string): string;
Return a fully-qualified entityType resource name string.
Name | Description |
project |
string
|
location |
string
|
featurestore |
string
|
entityType |
string
|
Type | Description |
string | {string} Resource name string. |
executionPath(project, location, metadataStore, execution)
executionPath(project: string, location: string, metadataStore: string, execution: string): string;
Return a fully-qualified execution resource name string.
Name | Description |
project |
string
|
location |
string
|
metadataStore |
string
|
execution |
string
|
Type | Description |
string | {string} Resource name string. |
exportTensorboardTimeSeriesData(request, options)
exportTensorboardTimeSeriesData(request?: protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint[],
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest | null,
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse
]>;
Exports a TensorboardTimeSeries' data. Data is returned in paginated responses.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint[], protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest | null, protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse ]> | {Promise} - The promise which resolves to an array. The first element of the array is Array of [TimeSeriesDataPoint]. The client library will perform auto-pagination by default: it will call the API as many times as needed and will merge results from all the pages into this array. Note that it can affect your quota. We recommend using |
exportTensorboardTimeSeriesData(request, options, callback)
exportTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, options: CallOptions, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest
|
options |
CallOptions
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint>
|
Type | Description |
void |
exportTensorboardTimeSeriesData(request, callback)
exportTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint>
|
Type | Description |
void |
exportTensorboardTimeSeriesDataAsync(request, options)
exportTensorboardTimeSeriesDataAsync(request?: protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, options?: CallOptions): AsyncIterable<protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint>;
Equivalent to exportTensorboardTimeSeriesData
, but returns an iterable object.
for
-await
-of
syntax is used with the iterable to get response elements on-demand.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
AsyncIterable<protos.google.cloud.aiplatform.v1.ITimeSeriesDataPoint> | {Object} An iterable Object that allows [async iteration](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). When you iterate the returned iterable, each element will be an object representing [TimeSeriesDataPoint]. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#auto-pagination) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardTimeSeries to export data from.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const tensorboardTimeSeries = 'abc123'
/**
* Exports the TensorboardTimeSeries' data that match the filter expression.
*/
// const filter = 'abc123'
/**
* The maximum number of data points to return per page.
* The default page_size will be 1000. Values must be between 1 and 10000.
* Values above 10000 will be coerced to 10000.
*/
// const pageSize = 1234
/**
* A page token, received from a previous
* TensorboardService.ExportTensorboardTimeSeries call.
* Provide this to retrieve the subsequent page.
* When paginating, all other parameters provided to
* TensorboardService.ExportTensorboardTimeSeries must
* match the call that provided the page token.
*/
// const pageToken = 'abc123'
/**
* Field to use to sort the TensorboardTimeSeries' data.
* By default, TensorboardTimeSeries' data will be returned in a pseudo random
* order.
*/
// const orderBy = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callExportTensorboardTimeSeriesData() {
// Construct request
const request = {
tensorboardTimeSeries,
};
// Run request
const iterable = await aiplatformClient.exportTensorboardTimeSeriesDataAsync(request);
for await (const response of iterable) {
console.log(response);
}
}
callExportTensorboardTimeSeriesData();
exportTensorboardTimeSeriesDataStream(request, options)
exportTensorboardTimeSeriesDataStream(request?: protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest, options?: CallOptions): Transform;
Equivalent to method.name.toCamelCase()
, but returns a NodeJS Stream object.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IExportTensorboardTimeSeriesDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Transform | {Stream} An object stream which emits an object representing [TimeSeriesDataPoint] on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using |
featurePath(project, location, featurestore, entityType, feature)
featurePath(project: string, location: string, featurestore: string, entityType: string, feature: string): string;
Return a fully-qualified feature resource name string.
Name | Description |
project |
string
|
location |
string
|
featurestore |
string
|
entityType |
string
|
feature |
string
|
Type | Description |
string | {string} Resource name string. |
featurestorePath(project, location, featurestore)
featurestorePath(project: string, location: string, featurestore: string): string;
Return a fully-qualified featurestore resource name string.
Name | Description |
project |
string
|
location |
string
|
featurestore |
string
|
Type | Description |
string | {string} Resource name string. |
getProjectId()
getProjectId(): Promise<string>;
Type | Description |
Promise<string> |
getProjectId(callback)
getProjectId(callback: Callback<string, undefined, undefined>): void;
Name | Description |
callback |
Callback<string, undefined, undefined>
|
Type | Description |
void |
getTensorboard(request, options)
getTensorboard(request?: protos.google.cloud.aiplatform.v1.IGetTensorboardRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboard,
protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | undefined,
{} | undefined
]>;
Gets a Tensorboard.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [Tensorboard]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the Tensorboard resource.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callGetTensorboard() {
// Construct request
const request = {
name,
};
// Run request
const response = await aiplatformClient.getTensorboard(request);
console.log(response);
}
callGetTensorboard();
getTensorboard(request, options, callback)
getTensorboard(request: protos.google.cloud.aiplatform.v1.IGetTensorboardRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboard(request, callback)
getTensorboard(request: protos.google.cloud.aiplatform.v1.IGetTensorboardRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IGetTensorboardRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardExperiment(request, options)
getTensorboardExperiment(request?: protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardExperiment,
(protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | undefined),
{} | undefined
]>;
Gets a TensorboardExperiment.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardExperiment, (protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardExperiment]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardExperiment resource.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callGetTensorboardExperiment() {
// Construct request
const request = {
name,
};
// Run request
const response = await aiplatformClient.getTensorboardExperiment(request);
console.log(response);
}
callGetTensorboardExperiment();
getTensorboardExperiment(request, options, callback)
getTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardExperiment(request, callback)
getTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IGetTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardRun(request, options)
getTensorboardRun(request?: protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardRun,
protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | undefined,
{} | undefined
]>;
Gets a TensorboardRun.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardRun]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardRun resource.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callGetTensorboardRun() {
// Construct request
const request = {
name,
};
// Run request
const response = await aiplatformClient.getTensorboardRun(request);
console.log(response);
}
callGetTensorboardRun();
getTensorboardRun(request, options, callback)
getTensorboardRun(request: protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardRun(request, callback)
getTensorboardRun(request: protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IGetTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardTimeSeries(request, options)
getTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries,
(protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | undefined),
{} | undefined
]>;
Gets a TensorboardTimeSeries.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, (protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardTimeSeries]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The name of the TensorboardTimeSeries resource.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const name = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callGetTensorboardTimeSeries() {
// Construct request
const request = {
name,
};
// Run request
const response = await aiplatformClient.getTensorboardTimeSeries(request);
console.log(response);
}
callGetTensorboardTimeSeries();
getTensorboardTimeSeries(request, options, callback)
getTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
getTensorboardTimeSeries(request, callback)
getTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IGetTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
hyperparameterTuningJobPath(project, location, hyperparameterTuningJob)
hyperparameterTuningJobPath(project: string, location: string, hyperparameterTuningJob: string): string;
Return a fully-qualified hyperparameterTuningJob resource name string.
Name | Description |
project |
string
|
location |
string
|
hyperparameterTuningJob |
string
|
Type | Description |
string | {string} Resource name string. |
indexEndpointPath(project, location, indexEndpoint)
indexEndpointPath(project: string, location: string, indexEndpoint: string): string;
Return a fully-qualified indexEndpoint resource name string.
Name | Description |
project |
string
|
location |
string
|
indexEndpoint |
string
|
Type | Description |
string | {string} Resource name string. |
indexPath(project, location, index)
indexPath(project: string, location: string, index: string): string;
Return a fully-qualified index resource name string.
Name | Description |
project |
string
|
location |
string
|
index |
string
|
Type | Description |
string | {string} Resource name string. |
initialize()
initialize(): Promise<{
[name: string]: Function;
}>;
Initialize the client. Performs asynchronous operations (such as authentication) and prepares the client. This function will be called automatically when any class method is called for the first time, but if you need to initialize it before calling an actual method, feel free to call initialize() directly.
You can await on this method if you want to make sure the client is initialized.
Type | Description |
Promise<{ [name: string]: Function; }> | {Promise} A promise that resolves to an authenticated service stub. |
listTensorboardExperiments(request, options)
listTensorboardExperiments(request?: protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardExperiment[],
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest | null,
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse
]>;
Lists TensorboardExperiments in a Location.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardExperiment[], protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest | null, protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse ]> | {Promise} - The promise which resolves to an array. The first element of the array is Array of [TensorboardExperiment]. The client library will perform auto-pagination by default: it will call the API as many times as needed and will merge results from all the pages into this array. Note that it can affect your quota. We recommend using |
listTensorboardExperiments(request, options, callback)
listTensorboardExperiments(request: protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, options: CallOptions, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardExperiment>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest
|
options |
CallOptions
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardExperiment>
|
Type | Description |
void |
listTensorboardExperiments(request, callback)
listTensorboardExperiments(request: protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardExperiment>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardExperiment>
|
Type | Description |
void |
listTensorboardExperimentsAsync(request, options)
listTensorboardExperimentsAsync(request?: protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, options?: CallOptions): AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardExperiment>;
Equivalent to listTensorboardExperiments
, but returns an iterable object.
for
-await
-of
syntax is used with the iterable to get response elements on-demand.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardExperiment> | {Object} An iterable Object that allows [async iteration](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). When you iterate the returned iterable, each element will be an object representing [TensorboardExperiment]. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#auto-pagination) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Tensorboard to list TensorboardExperiments.
* Format:
* 'projects/{project}/locations/{location}/tensorboards/{tensorboard}'
*/
// const parent = 'abc123'
/**
* Lists the TensorboardExperiments that match the filter expression.
*/
// const filter = 'abc123'
/**
* The maximum number of TensorboardExperiments to return. The service may
* return fewer than this value. If unspecified, at most 50
* TensorboardExperiments will be returned. The maximum value is 1000; values
* above 1000 will be coerced to 1000.
*/
// const pageSize = 1234
/**
* A page token, received from a previous
* TensorboardService.ListTensorboardExperiments google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments call.
* Provide this to retrieve the subsequent page.
* When paginating, all other parameters provided to
* TensorboardService.ListTensorboardExperiments google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments must
* match the call that provided the page token.
*/
// const pageToken = 'abc123'
/**
* Field to use to sort the list.
*/
// const orderBy = 'abc123'
/**
* Mask specifying which fields to read.
*/
// const readMask = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callListTensorboardExperiments() {
// Construct request
const request = {
parent,
};
// Run request
const iterable = await aiplatformClient.listTensorboardExperimentsAsync(request);
for await (const response of iterable) {
console.log(response);
}
}
callListTensorboardExperiments();
listTensorboardExperimentsStream(request, options)
listTensorboardExperimentsStream(request?: protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest, options?: CallOptions): Transform;
Equivalent to method.name.toCamelCase()
, but returns a NodeJS Stream object.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardExperimentsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Transform | {Stream} An object stream which emits an object representing [TensorboardExperiment] on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using |
listTensorboardRuns(request, options)
listTensorboardRuns(request?: protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardRun[],
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest | null,
protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse
]>;
Lists TensorboardRuns in a Location.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardRun[], protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest | null, protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse ]> | {Promise} - The promise which resolves to an array. The first element of the array is Array of [TensorboardRun]. The client library will perform auto-pagination by default: it will call the API as many times as needed and will merge results from all the pages into this array. Note that it can affect your quota. We recommend using |
listTensorboardRuns(request, options, callback)
listTensorboardRuns(request: protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, options: CallOptions, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardRun>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest
|
options |
CallOptions
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardRun>
|
Type | Description |
void |
listTensorboardRuns(request, callback)
listTensorboardRuns(request: protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardRun>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardRunsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardRun>
|
Type | Description |
void |
listTensorboardRunsAsync(request, options)
listTensorboardRunsAsync(request?: protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, options?: CallOptions): AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardRun>;
Equivalent to listTensorboardRuns
, but returns an iterable object.
for
-await
-of
syntax is used with the iterable to get response elements on-demand.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardRun> | {Object} An iterable Object that allows [async iteration](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). When you iterate the returned iterable, each element will be an object representing [TensorboardRun]. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#auto-pagination) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardExperiment to list TensorboardRuns.
* Format:
* 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}'
*/
// const parent = 'abc123'
/**
* Lists the TensorboardRuns that match the filter expression.
*/
// const filter = 'abc123'
/**
* The maximum number of TensorboardRuns to return. The service may return
* fewer than this value. If unspecified, at most 50 TensorboardRuns will be
* returned. The maximum value is 1000; values above 1000 will be coerced to
* 1000.
*/
// const pageSize = 1234
/**
* A page token, received from a previous
* TensorboardService.ListTensorboardRuns google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns call.
* Provide this to retrieve the subsequent page.
* When paginating, all other parameters provided to
* TensorboardService.ListTensorboardRuns google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns must
* match the call that provided the page token.
*/
// const pageToken = 'abc123'
/**
* Field to use to sort the list.
*/
// const orderBy = 'abc123'
/**
* Mask specifying which fields to read.
*/
// const readMask = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callListTensorboardRuns() {
// Construct request
const request = {
parent,
};
// Run request
const iterable = await aiplatformClient.listTensorboardRunsAsync(request);
for await (const response of iterable) {
console.log(response);
}
}
callListTensorboardRuns();
listTensorboardRunsStream(request, options)
listTensorboardRunsStream(request?: protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest, options?: CallOptions): Transform;
Equivalent to method.name.toCamelCase()
, but returns a NodeJS Stream object.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardRunsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Transform | {Stream} An object stream which emits an object representing [TensorboardRun] on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using |
listTensorboards(request, options)
listTensorboards(request?: protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboard[],
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest | null,
protos.google.cloud.aiplatform.v1.IListTensorboardsResponse
]>;
Lists Tensorboards in a Location.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboard[], protos.google.cloud.aiplatform.v1.IListTensorboardsRequest | null, protos.google.cloud.aiplatform.v1.IListTensorboardsResponse ]> | {Promise} - The promise which resolves to an array. The first element of the array is Array of [Tensorboard]. The client library will perform auto-pagination by default: it will call the API as many times as needed and will merge results from all the pages into this array. Note that it can affect your quota. We recommend using |
listTensorboards(request, options, callback)
listTensorboards(request: protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, options: CallOptions, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboard>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest
|
options |
CallOptions
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboard>
|
Type | Description |
void |
listTensorboards(request, callback)
listTensorboards(request: protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboard>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, protos.google.cloud.aiplatform.v1.IListTensorboardsResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboard>
|
Type | Description |
void |
listTensorboardsAsync(request, options)
listTensorboardsAsync(request?: protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, options?: CallOptions): AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboard>;
Equivalent to listTensorboards
, but returns an iterable object.
for
-await
-of
syntax is used with the iterable to get response elements on-demand.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboard> | {Object} An iterable Object that allows [async iteration](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). When you iterate the returned iterable, each element will be an object representing [Tensorboard]. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#auto-pagination) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the Location to list Tensorboards.
* Format:
* `projects/{project}/locations/{location}`
*/
// const parent = 'abc123'
/**
* Lists the Tensorboards that match the filter expression.
*/
// const filter = 'abc123'
/**
* The maximum number of Tensorboards to return. The service may return
* fewer than this value. If unspecified, at most 100 Tensorboards will be
* returned. The maximum value is 100; values above 100 will be coerced to
* 100.
*/
// const pageSize = 1234
/**
* A page token, received from a previous
* TensorboardService.ListTensorboards google.cloud.aiplatform.v1.TensorboardService.ListTensorboards call.
* Provide this to retrieve the subsequent page.
* When paginating, all other parameters provided to
* TensorboardService.ListTensorboards google.cloud.aiplatform.v1.TensorboardService.ListTensorboards must
* match the call that provided the page token.
*/
// const pageToken = 'abc123'
/**
* Field to use to sort the list.
*/
// const orderBy = 'abc123'
/**
* Mask specifying which fields to read.
*/
// const readMask = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callListTensorboards() {
// Construct request
const request = {
parent,
};
// Run request
const iterable = await aiplatformClient.listTensorboardsAsync(request);
for await (const response of iterable) {
console.log(response);
}
}
callListTensorboards();
listTensorboardsStream(request, options)
listTensorboardsStream(request?: protos.google.cloud.aiplatform.v1.IListTensorboardsRequest, options?: CallOptions): Transform;
Equivalent to method.name.toCamelCase()
, but returns a NodeJS Stream object.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardsRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Transform | {Stream} An object stream which emits an object representing [Tensorboard] on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using |
listTensorboardTimeSeries(request, options)
listTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries[],
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest | null,
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse
]>;
Lists TensorboardTimeSeries in a Location.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries[], protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest | null, protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse ]> | {Promise} - The promise which resolves to an array. The first element of the array is Array of [TensorboardTimeSeries]. The client library will perform auto-pagination by default: it will call the API as many times as needed and will merge results from all the pages into this array. Note that it can affect your quota. We recommend using |
listTensorboardTimeSeries(request, options, callback)
listTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, options: CallOptions, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries>
|
Type | Description |
void |
listTensorboardTimeSeries(request, callback)
listTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, callback: PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest
|
callback |
PaginationCallback<protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesResponse | null | undefined, protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries>
|
Type | Description |
void |
listTensorboardTimeSeriesAsync(request, options)
listTensorboardTimeSeriesAsync(request?: protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, options?: CallOptions): AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries>;
Equivalent to listTensorboardTimeSeries
, but returns an iterable object.
for
-await
-of
syntax is used with the iterable to get response elements on-demand.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
AsyncIterable<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries> | {Object} An iterable Object that allows [async iteration](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). When you iterate the returned iterable, each element will be an object representing [TensorboardTimeSeries]. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#auto-pagination) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardRun to list TensorboardTimeSeries.
* Format:
* 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}'
*/
// const parent = 'abc123'
/**
* Lists the TensorboardTimeSeries that match the filter expression.
*/
// const filter = 'abc123'
/**
* The maximum number of TensorboardTimeSeries to return. The service may
* return fewer than this value. If unspecified, at most 50
* TensorboardTimeSeries will be returned. The maximum value is 1000; values
* above 1000 will be coerced to 1000.
*/
// const pageSize = 1234
/**
* A page token, received from a previous
* TensorboardService.ListTensorboardTimeSeries google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries call.
* Provide this to retrieve the subsequent page.
* When paginating, all other parameters provided to
* TensorboardService.ListTensorboardTimeSeries google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries must
* match the call that provided the page token.
*/
// const pageToken = 'abc123'
/**
* Field to use to sort the list.
*/
// const orderBy = 'abc123'
/**
* Mask specifying which fields to read.
*/
// const readMask = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callListTensorboardTimeSeries() {
// Construct request
const request = {
parent,
};
// Run request
const iterable = await aiplatformClient.listTensorboardTimeSeriesAsync(request);
for await (const response of iterable) {
console.log(response);
}
}
callListTensorboardTimeSeries();
listTensorboardTimeSeriesStream(request, options)
listTensorboardTimeSeriesStream(request?: protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest, options?: CallOptions): Transform;
Equivalent to method.name.toCamelCase()
, but returns a NodeJS Stream object.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IListTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Transform | {Stream} An object stream which emits an object representing [TensorboardTimeSeries] on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using |
locationPath(project, location)
locationPath(project: string, location: string): string;
Return a fully-qualified location resource name string.
Name | Description |
project |
string
|
location |
string
|
Type | Description |
string | {string} Resource name string. |
matchAnnotationFromAnnotationName(annotationName)
matchAnnotationFromAnnotationName(annotationName: string): string | number;
Parse the annotation from Annotation resource.
Name | Description |
annotationName |
string
A fully-qualified path representing Annotation resource. |
Type | Description |
string | number | {string} A string representing the annotation. |
matchAnnotationSpecFromAnnotationSpecName(annotationSpecName)
matchAnnotationSpecFromAnnotationSpecName(annotationSpecName: string): string | number;
Parse the annotation_spec from AnnotationSpec resource.
Name | Description |
annotationSpecName |
string
A fully-qualified path representing AnnotationSpec resource. |
Type | Description |
string | number | {string} A string representing the annotation_spec. |
matchArtifactFromArtifactName(artifactName)
matchArtifactFromArtifactName(artifactName: string): string | number;
Parse the artifact from Artifact resource.
Name | Description |
artifactName |
string
A fully-qualified path representing Artifact resource. |
Type | Description |
string | number | {string} A string representing the artifact. |
matchBatchPredictionJobFromBatchPredictionJobName(batchPredictionJobName)
matchBatchPredictionJobFromBatchPredictionJobName(batchPredictionJobName: string): string | number;
Parse the batch_prediction_job from BatchPredictionJob resource.
Name | Description |
batchPredictionJobName |
string
A fully-qualified path representing BatchPredictionJob resource. |
Type | Description |
string | number | {string} A string representing the batch_prediction_job. |
matchContextFromContextName(contextName)
matchContextFromContextName(contextName: string): string | number;
Parse the context from Context resource.
Name | Description |
contextName |
string
A fully-qualified path representing Context resource. |
Type | Description |
string | number | {string} A string representing the context. |
matchCustomJobFromCustomJobName(customJobName)
matchCustomJobFromCustomJobName(customJobName: string): string | number;
Parse the custom_job from CustomJob resource.
Name | Description |
customJobName |
string
A fully-qualified path representing CustomJob resource. |
Type | Description |
string | number | {string} A string representing the custom_job. |
matchDataItemFromAnnotationName(annotationName)
matchDataItemFromAnnotationName(annotationName: string): string | number;
Parse the data_item from Annotation resource.
Name | Description |
annotationName |
string
A fully-qualified path representing Annotation resource. |
Type | Description |
string | number | {string} A string representing the data_item. |
matchDataItemFromDataItemName(dataItemName)
matchDataItemFromDataItemName(dataItemName: string): string | number;
Parse the data_item from DataItem resource.
Name | Description |
dataItemName |
string
A fully-qualified path representing DataItem resource. |
Type | Description |
string | number | {string} A string representing the data_item. |
matchDataLabelingJobFromDataLabelingJobName(dataLabelingJobName)
matchDataLabelingJobFromDataLabelingJobName(dataLabelingJobName: string): string | number;
Parse the data_labeling_job from DataLabelingJob resource.
Name | Description |
dataLabelingJobName |
string
A fully-qualified path representing DataLabelingJob resource. |
Type | Description |
string | number | {string} A string representing the data_labeling_job. |
matchDatasetFromAnnotationName(annotationName)
matchDatasetFromAnnotationName(annotationName: string): string | number;
Parse the dataset from Annotation resource.
Name | Description |
annotationName |
string
A fully-qualified path representing Annotation resource. |
Type | Description |
string | number | {string} A string representing the dataset. |
matchDatasetFromAnnotationSpecName(annotationSpecName)
matchDatasetFromAnnotationSpecName(annotationSpecName: string): string | number;
Parse the dataset from AnnotationSpec resource.
Name | Description |
annotationSpecName |
string
A fully-qualified path representing AnnotationSpec resource. |
Type | Description |
string | number | {string} A string representing the dataset. |
matchDatasetFromDataItemName(dataItemName)
matchDatasetFromDataItemName(dataItemName: string): string | number;
Parse the dataset from DataItem resource.
Name | Description |
dataItemName |
string
A fully-qualified path representing DataItem resource. |
Type | Description |
string | number | {string} A string representing the dataset. |
matchDatasetFromDatasetName(datasetName)
matchDatasetFromDatasetName(datasetName: string): string | number;
Parse the dataset from Dataset resource.
Name | Description |
datasetName |
string
A fully-qualified path representing Dataset resource. |
Type | Description |
string | number | {string} A string representing the dataset. |
matchEndpointFromEndpointName(endpointName)
matchEndpointFromEndpointName(endpointName: string): string | number;
Parse the endpoint from Endpoint resource.
Name | Description |
endpointName |
string
A fully-qualified path representing Endpoint resource. |
Type | Description |
string | number | {string} A string representing the endpoint. |
matchEntityTypeFromEntityTypeName(entityTypeName)
matchEntityTypeFromEntityTypeName(entityTypeName: string): string | number;
Parse the entity_type from EntityType resource.
Name | Description |
entityTypeName |
string
A fully-qualified path representing EntityType resource. |
Type | Description |
string | number | {string} A string representing the entity_type. |
matchEntityTypeFromFeatureName(featureName)
matchEntityTypeFromFeatureName(featureName: string): string | number;
Parse the entity_type from Feature resource.
Name | Description |
featureName |
string
A fully-qualified path representing Feature resource. |
Type | Description |
string | number | {string} A string representing the entity_type. |
matchEvaluationFromModelEvaluationName(modelEvaluationName)
matchEvaluationFromModelEvaluationName(modelEvaluationName: string): string | number;
Parse the evaluation from ModelEvaluation resource.
Name | Description |
modelEvaluationName |
string
A fully-qualified path representing ModelEvaluation resource. |
Type | Description |
string | number | {string} A string representing the evaluation. |
matchEvaluationFromModelEvaluationSliceName(modelEvaluationSliceName)
matchEvaluationFromModelEvaluationSliceName(modelEvaluationSliceName: string): string | number;
Parse the evaluation from ModelEvaluationSlice resource.
Name | Description |
modelEvaluationSliceName |
string
A fully-qualified path representing ModelEvaluationSlice resource. |
Type | Description |
string | number | {string} A string representing the evaluation. |
matchExecutionFromExecutionName(executionName)
matchExecutionFromExecutionName(executionName: string): string | number;
Parse the execution from Execution resource.
Name | Description |
executionName |
string
A fully-qualified path representing Execution resource. |
Type | Description |
string | number | {string} A string representing the execution. |
matchExperimentFromTensorboardExperimentName(tensorboardExperimentName)
matchExperimentFromTensorboardExperimentName(tensorboardExperimentName: string): string | number;
Parse the experiment from TensorboardExperiment resource.
Name | Description |
tensorboardExperimentName |
string
A fully-qualified path representing TensorboardExperiment resource. |
Type | Description |
string | number | {string} A string representing the experiment. |
matchExperimentFromTensorboardRunName(tensorboardRunName)
matchExperimentFromTensorboardRunName(tensorboardRunName: string): string | number;
Parse the experiment from TensorboardRun resource.
Name | Description |
tensorboardRunName |
string
A fully-qualified path representing TensorboardRun resource. |
Type | Description |
string | number | {string} A string representing the experiment. |
matchExperimentFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchExperimentFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the experiment from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the experiment. |
matchFeatureFromFeatureName(featureName)
matchFeatureFromFeatureName(featureName: string): string | number;
Parse the feature from Feature resource.
Name | Description |
featureName |
string
A fully-qualified path representing Feature resource. |
Type | Description |
string | number | {string} A string representing the feature. |
matchFeaturestoreFromEntityTypeName(entityTypeName)
matchFeaturestoreFromEntityTypeName(entityTypeName: string): string | number;
Parse the featurestore from EntityType resource.
Name | Description |
entityTypeName |
string
A fully-qualified path representing EntityType resource. |
Type | Description |
string | number | {string} A string representing the featurestore. |
matchFeaturestoreFromFeatureName(featureName)
matchFeaturestoreFromFeatureName(featureName: string): string | number;
Parse the featurestore from Feature resource.
Name | Description |
featureName |
string
A fully-qualified path representing Feature resource. |
Type | Description |
string | number | {string} A string representing the featurestore. |
matchFeaturestoreFromFeaturestoreName(featurestoreName)
matchFeaturestoreFromFeaturestoreName(featurestoreName: string): string | number;
Parse the featurestore from Featurestore resource.
Name | Description |
featurestoreName |
string
A fully-qualified path representing Featurestore resource. |
Type | Description |
string | number | {string} A string representing the featurestore. |
matchHyperparameterTuningJobFromHyperparameterTuningJobName(hyperparameterTuningJobName)
matchHyperparameterTuningJobFromHyperparameterTuningJobName(hyperparameterTuningJobName: string): string | number;
Parse the hyperparameter_tuning_job from HyperparameterTuningJob resource.
Name | Description |
hyperparameterTuningJobName |
string
A fully-qualified path representing HyperparameterTuningJob resource. |
Type | Description |
string | number | {string} A string representing the hyperparameter_tuning_job. |
matchIndexEndpointFromIndexEndpointName(indexEndpointName)
matchIndexEndpointFromIndexEndpointName(indexEndpointName: string): string | number;
Parse the index_endpoint from IndexEndpoint resource.
Name | Description |
indexEndpointName |
string
A fully-qualified path representing IndexEndpoint resource. |
Type | Description |
string | number | {string} A string representing the index_endpoint. |
matchIndexFromIndexName(indexName)
matchIndexFromIndexName(indexName: string): string | number;
Parse the index from Index resource.
Name | Description |
indexName |
string
A fully-qualified path representing Index resource. |
Type | Description |
string | number | {string} A string representing the index. |
matchLocationFromAnnotationName(annotationName)
matchLocationFromAnnotationName(annotationName: string): string | number;
Parse the location from Annotation resource.
Name | Description |
annotationName |
string
A fully-qualified path representing Annotation resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromAnnotationSpecName(annotationSpecName)
matchLocationFromAnnotationSpecName(annotationSpecName: string): string | number;
Parse the location from AnnotationSpec resource.
Name | Description |
annotationSpecName |
string
A fully-qualified path representing AnnotationSpec resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromArtifactName(artifactName)
matchLocationFromArtifactName(artifactName: string): string | number;
Parse the location from Artifact resource.
Name | Description |
artifactName |
string
A fully-qualified path representing Artifact resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromBatchPredictionJobName(batchPredictionJobName)
matchLocationFromBatchPredictionJobName(batchPredictionJobName: string): string | number;
Parse the location from BatchPredictionJob resource.
Name | Description |
batchPredictionJobName |
string
A fully-qualified path representing BatchPredictionJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromContextName(contextName)
matchLocationFromContextName(contextName: string): string | number;
Parse the location from Context resource.
Name | Description |
contextName |
string
A fully-qualified path representing Context resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromCustomJobName(customJobName)
matchLocationFromCustomJobName(customJobName: string): string | number;
Parse the location from CustomJob resource.
Name | Description |
customJobName |
string
A fully-qualified path representing CustomJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromDataItemName(dataItemName)
matchLocationFromDataItemName(dataItemName: string): string | number;
Parse the location from DataItem resource.
Name | Description |
dataItemName |
string
A fully-qualified path representing DataItem resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromDataLabelingJobName(dataLabelingJobName)
matchLocationFromDataLabelingJobName(dataLabelingJobName: string): string | number;
Parse the location from DataLabelingJob resource.
Name | Description |
dataLabelingJobName |
string
A fully-qualified path representing DataLabelingJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromDatasetName(datasetName)
matchLocationFromDatasetName(datasetName: string): string | number;
Parse the location from Dataset resource.
Name | Description |
datasetName |
string
A fully-qualified path representing Dataset resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromEndpointName(endpointName)
matchLocationFromEndpointName(endpointName: string): string | number;
Parse the location from Endpoint resource.
Name | Description |
endpointName |
string
A fully-qualified path representing Endpoint resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromEntityTypeName(entityTypeName)
matchLocationFromEntityTypeName(entityTypeName: string): string | number;
Parse the location from EntityType resource.
Name | Description |
entityTypeName |
string
A fully-qualified path representing EntityType resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromExecutionName(executionName)
matchLocationFromExecutionName(executionName: string): string | number;
Parse the location from Execution resource.
Name | Description |
executionName |
string
A fully-qualified path representing Execution resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromFeatureName(featureName)
matchLocationFromFeatureName(featureName: string): string | number;
Parse the location from Feature resource.
Name | Description |
featureName |
string
A fully-qualified path representing Feature resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromFeaturestoreName(featurestoreName)
matchLocationFromFeaturestoreName(featurestoreName: string): string | number;
Parse the location from Featurestore resource.
Name | Description |
featurestoreName |
string
A fully-qualified path representing Featurestore resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromHyperparameterTuningJobName(hyperparameterTuningJobName)
matchLocationFromHyperparameterTuningJobName(hyperparameterTuningJobName: string): string | number;
Parse the location from HyperparameterTuningJob resource.
Name | Description |
hyperparameterTuningJobName |
string
A fully-qualified path representing HyperparameterTuningJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromIndexEndpointName(indexEndpointName)
matchLocationFromIndexEndpointName(indexEndpointName: string): string | number;
Parse the location from IndexEndpoint resource.
Name | Description |
indexEndpointName |
string
A fully-qualified path representing IndexEndpoint resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromIndexName(indexName)
matchLocationFromIndexName(indexName: string): string | number;
Parse the location from Index resource.
Name | Description |
indexName |
string
A fully-qualified path representing Index resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromLocationName(locationName)
matchLocationFromLocationName(locationName: string): string | number;
Parse the location from Location resource.
Name | Description |
locationName |
string
A fully-qualified path representing Location resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromMetadataSchemaName(metadataSchemaName)
matchLocationFromMetadataSchemaName(metadataSchemaName: string): string | number;
Parse the location from MetadataSchema resource.
Name | Description |
metadataSchemaName |
string
A fully-qualified path representing MetadataSchema resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromMetadataStoreName(metadataStoreName)
matchLocationFromMetadataStoreName(metadataStoreName: string): string | number;
Parse the location from MetadataStore resource.
Name | Description |
metadataStoreName |
string
A fully-qualified path representing MetadataStore resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName)
matchLocationFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName: string): string | number;
Parse the location from ModelDeploymentMonitoringJob resource.
Name | Description |
modelDeploymentMonitoringJobName |
string
A fully-qualified path representing ModelDeploymentMonitoringJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromModelEvaluationName(modelEvaluationName)
matchLocationFromModelEvaluationName(modelEvaluationName: string): string | number;
Parse the location from ModelEvaluation resource.
Name | Description |
modelEvaluationName |
string
A fully-qualified path representing ModelEvaluation resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromModelEvaluationSliceName(modelEvaluationSliceName)
matchLocationFromModelEvaluationSliceName(modelEvaluationSliceName: string): string | number;
Parse the location from ModelEvaluationSlice resource.
Name | Description |
modelEvaluationSliceName |
string
A fully-qualified path representing ModelEvaluationSlice resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromModelName(modelName)
matchLocationFromModelName(modelName: string): string | number;
Parse the location from Model resource.
Name | Description |
modelName |
string
A fully-qualified path representing Model resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromPipelineJobName(pipelineJobName)
matchLocationFromPipelineJobName(pipelineJobName: string): string | number;
Parse the location from PipelineJob resource.
Name | Description |
pipelineJobName |
string
A fully-qualified path representing PipelineJob resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromSpecialistPoolName(specialistPoolName)
matchLocationFromSpecialistPoolName(specialistPoolName: string): string | number;
Parse the location from SpecialistPool resource.
Name | Description |
specialistPoolName |
string
A fully-qualified path representing SpecialistPool resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromStudyName(studyName)
matchLocationFromStudyName(studyName: string): string | number;
Parse the location from Study resource.
Name | Description |
studyName |
string
A fully-qualified path representing Study resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTensorboardExperimentName(tensorboardExperimentName)
matchLocationFromTensorboardExperimentName(tensorboardExperimentName: string): string | number;
Parse the location from TensorboardExperiment resource.
Name | Description |
tensorboardExperimentName |
string
A fully-qualified path representing TensorboardExperiment resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTensorboardName(tensorboardName)
matchLocationFromTensorboardName(tensorboardName: string): string | number;
Parse the location from Tensorboard resource.
Name | Description |
tensorboardName |
string
A fully-qualified path representing Tensorboard resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTensorboardRunName(tensorboardRunName)
matchLocationFromTensorboardRunName(tensorboardRunName: string): string | number;
Parse the location from TensorboardRun resource.
Name | Description |
tensorboardRunName |
string
A fully-qualified path representing TensorboardRun resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchLocationFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the location from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTrainingPipelineName(trainingPipelineName)
matchLocationFromTrainingPipelineName(trainingPipelineName: string): string | number;
Parse the location from TrainingPipeline resource.
Name | Description |
trainingPipelineName |
string
A fully-qualified path representing TrainingPipeline resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchLocationFromTrialName(trialName)
matchLocationFromTrialName(trialName: string): string | number;
Parse the location from Trial resource.
Name | Description |
trialName |
string
A fully-qualified path representing Trial resource. |
Type | Description |
string | number | {string} A string representing the location. |
matchMetadataSchemaFromMetadataSchemaName(metadataSchemaName)
matchMetadataSchemaFromMetadataSchemaName(metadataSchemaName: string): string | number;
Parse the metadata_schema from MetadataSchema resource.
Name | Description |
metadataSchemaName |
string
A fully-qualified path representing MetadataSchema resource. |
Type | Description |
string | number | {string} A string representing the metadata_schema. |
matchMetadataStoreFromArtifactName(artifactName)
matchMetadataStoreFromArtifactName(artifactName: string): string | number;
Parse the metadata_store from Artifact resource.
Name | Description |
artifactName |
string
A fully-qualified path representing Artifact resource. |
Type | Description |
string | number | {string} A string representing the metadata_store. |
matchMetadataStoreFromContextName(contextName)
matchMetadataStoreFromContextName(contextName: string): string | number;
Parse the metadata_store from Context resource.
Name | Description |
contextName |
string
A fully-qualified path representing Context resource. |
Type | Description |
string | number | {string} A string representing the metadata_store. |
matchMetadataStoreFromExecutionName(executionName)
matchMetadataStoreFromExecutionName(executionName: string): string | number;
Parse the metadata_store from Execution resource.
Name | Description |
executionName |
string
A fully-qualified path representing Execution resource. |
Type | Description |
string | number | {string} A string representing the metadata_store. |
matchMetadataStoreFromMetadataSchemaName(metadataSchemaName)
matchMetadataStoreFromMetadataSchemaName(metadataSchemaName: string): string | number;
Parse the metadata_store from MetadataSchema resource.
Name | Description |
metadataSchemaName |
string
A fully-qualified path representing MetadataSchema resource. |
Type | Description |
string | number | {string} A string representing the metadata_store. |
matchMetadataStoreFromMetadataStoreName(metadataStoreName)
matchMetadataStoreFromMetadataStoreName(metadataStoreName: string): string | number;
Parse the metadata_store from MetadataStore resource.
Name | Description |
metadataStoreName |
string
A fully-qualified path representing MetadataStore resource. |
Type | Description |
string | number | {string} A string representing the metadata_store. |
matchModelDeploymentMonitoringJobFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName)
matchModelDeploymentMonitoringJobFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName: string): string | number;
Parse the model_deployment_monitoring_job from ModelDeploymentMonitoringJob resource.
Name | Description |
modelDeploymentMonitoringJobName |
string
A fully-qualified path representing ModelDeploymentMonitoringJob resource. |
Type | Description |
string | number | {string} A string representing the model_deployment_monitoring_job. |
matchModelFromModelEvaluationName(modelEvaluationName)
matchModelFromModelEvaluationName(modelEvaluationName: string): string | number;
Parse the model from ModelEvaluation resource.
Name | Description |
modelEvaluationName |
string
A fully-qualified path representing ModelEvaluation resource. |
Type | Description |
string | number | {string} A string representing the model. |
matchModelFromModelEvaluationSliceName(modelEvaluationSliceName)
matchModelFromModelEvaluationSliceName(modelEvaluationSliceName: string): string | number;
Parse the model from ModelEvaluationSlice resource.
Name | Description |
modelEvaluationSliceName |
string
A fully-qualified path representing ModelEvaluationSlice resource. |
Type | Description |
string | number | {string} A string representing the model. |
matchModelFromModelName(modelName)
matchModelFromModelName(modelName: string): string | number;
Parse the model from Model resource.
Name | Description |
modelName |
string
A fully-qualified path representing Model resource. |
Type | Description |
string | number | {string} A string representing the model. |
matchPipelineJobFromPipelineJobName(pipelineJobName)
matchPipelineJobFromPipelineJobName(pipelineJobName: string): string | number;
Parse the pipeline_job from PipelineJob resource.
Name | Description |
pipelineJobName |
string
A fully-qualified path representing PipelineJob resource. |
Type | Description |
string | number | {string} A string representing the pipeline_job. |
matchProjectFromAnnotationName(annotationName)
matchProjectFromAnnotationName(annotationName: string): string | number;
Parse the project from Annotation resource.
Name | Description |
annotationName |
string
A fully-qualified path representing Annotation resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromAnnotationSpecName(annotationSpecName)
matchProjectFromAnnotationSpecName(annotationSpecName: string): string | number;
Parse the project from AnnotationSpec resource.
Name | Description |
annotationSpecName |
string
A fully-qualified path representing AnnotationSpec resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromArtifactName(artifactName)
matchProjectFromArtifactName(artifactName: string): string | number;
Parse the project from Artifact resource.
Name | Description |
artifactName |
string
A fully-qualified path representing Artifact resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromBatchPredictionJobName(batchPredictionJobName)
matchProjectFromBatchPredictionJobName(batchPredictionJobName: string): string | number;
Parse the project from BatchPredictionJob resource.
Name | Description |
batchPredictionJobName |
string
A fully-qualified path representing BatchPredictionJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromContextName(contextName)
matchProjectFromContextName(contextName: string): string | number;
Parse the project from Context resource.
Name | Description |
contextName |
string
A fully-qualified path representing Context resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromCustomJobName(customJobName)
matchProjectFromCustomJobName(customJobName: string): string | number;
Parse the project from CustomJob resource.
Name | Description |
customJobName |
string
A fully-qualified path representing CustomJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromDataItemName(dataItemName)
matchProjectFromDataItemName(dataItemName: string): string | number;
Parse the project from DataItem resource.
Name | Description |
dataItemName |
string
A fully-qualified path representing DataItem resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromDataLabelingJobName(dataLabelingJobName)
matchProjectFromDataLabelingJobName(dataLabelingJobName: string): string | number;
Parse the project from DataLabelingJob resource.
Name | Description |
dataLabelingJobName |
string
A fully-qualified path representing DataLabelingJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromDatasetName(datasetName)
matchProjectFromDatasetName(datasetName: string): string | number;
Parse the project from Dataset resource.
Name | Description |
datasetName |
string
A fully-qualified path representing Dataset resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromEndpointName(endpointName)
matchProjectFromEndpointName(endpointName: string): string | number;
Parse the project from Endpoint resource.
Name | Description |
endpointName |
string
A fully-qualified path representing Endpoint resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromEntityTypeName(entityTypeName)
matchProjectFromEntityTypeName(entityTypeName: string): string | number;
Parse the project from EntityType resource.
Name | Description |
entityTypeName |
string
A fully-qualified path representing EntityType resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromExecutionName(executionName)
matchProjectFromExecutionName(executionName: string): string | number;
Parse the project from Execution resource.
Name | Description |
executionName |
string
A fully-qualified path representing Execution resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromFeatureName(featureName)
matchProjectFromFeatureName(featureName: string): string | number;
Parse the project from Feature resource.
Name | Description |
featureName |
string
A fully-qualified path representing Feature resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromFeaturestoreName(featurestoreName)
matchProjectFromFeaturestoreName(featurestoreName: string): string | number;
Parse the project from Featurestore resource.
Name | Description |
featurestoreName |
string
A fully-qualified path representing Featurestore resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromHyperparameterTuningJobName(hyperparameterTuningJobName)
matchProjectFromHyperparameterTuningJobName(hyperparameterTuningJobName: string): string | number;
Parse the project from HyperparameterTuningJob resource.
Name | Description |
hyperparameterTuningJobName |
string
A fully-qualified path representing HyperparameterTuningJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromIndexEndpointName(indexEndpointName)
matchProjectFromIndexEndpointName(indexEndpointName: string): string | number;
Parse the project from IndexEndpoint resource.
Name | Description |
indexEndpointName |
string
A fully-qualified path representing IndexEndpoint resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromIndexName(indexName)
matchProjectFromIndexName(indexName: string): string | number;
Parse the project from Index resource.
Name | Description |
indexName |
string
A fully-qualified path representing Index resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromLocationName(locationName)
matchProjectFromLocationName(locationName: string): string | number;
Parse the project from Location resource.
Name | Description |
locationName |
string
A fully-qualified path representing Location resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromMetadataSchemaName(metadataSchemaName)
matchProjectFromMetadataSchemaName(metadataSchemaName: string): string | number;
Parse the project from MetadataSchema resource.
Name | Description |
metadataSchemaName |
string
A fully-qualified path representing MetadataSchema resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromMetadataStoreName(metadataStoreName)
matchProjectFromMetadataStoreName(metadataStoreName: string): string | number;
Parse the project from MetadataStore resource.
Name | Description |
metadataStoreName |
string
A fully-qualified path representing MetadataStore resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName)
matchProjectFromModelDeploymentMonitoringJobName(modelDeploymentMonitoringJobName: string): string | number;
Parse the project from ModelDeploymentMonitoringJob resource.
Name | Description |
modelDeploymentMonitoringJobName |
string
A fully-qualified path representing ModelDeploymentMonitoringJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromModelEvaluationName(modelEvaluationName)
matchProjectFromModelEvaluationName(modelEvaluationName: string): string | number;
Parse the project from ModelEvaluation resource.
Name | Description |
modelEvaluationName |
string
A fully-qualified path representing ModelEvaluation resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromModelEvaluationSliceName(modelEvaluationSliceName)
matchProjectFromModelEvaluationSliceName(modelEvaluationSliceName: string): string | number;
Parse the project from ModelEvaluationSlice resource.
Name | Description |
modelEvaluationSliceName |
string
A fully-qualified path representing ModelEvaluationSlice resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromModelName(modelName)
matchProjectFromModelName(modelName: string): string | number;
Parse the project from Model resource.
Name | Description |
modelName |
string
A fully-qualified path representing Model resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromPipelineJobName(pipelineJobName)
matchProjectFromPipelineJobName(pipelineJobName: string): string | number;
Parse the project from PipelineJob resource.
Name | Description |
pipelineJobName |
string
A fully-qualified path representing PipelineJob resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromProjectName(projectName)
matchProjectFromProjectName(projectName: string): string | number;
Parse the project from Project resource.
Name | Description |
projectName |
string
A fully-qualified path representing Project resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromSpecialistPoolName(specialistPoolName)
matchProjectFromSpecialistPoolName(specialistPoolName: string): string | number;
Parse the project from SpecialistPool resource.
Name | Description |
specialistPoolName |
string
A fully-qualified path representing SpecialistPool resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromStudyName(studyName)
matchProjectFromStudyName(studyName: string): string | number;
Parse the project from Study resource.
Name | Description |
studyName |
string
A fully-qualified path representing Study resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTensorboardExperimentName(tensorboardExperimentName)
matchProjectFromTensorboardExperimentName(tensorboardExperimentName: string): string | number;
Parse the project from TensorboardExperiment resource.
Name | Description |
tensorboardExperimentName |
string
A fully-qualified path representing TensorboardExperiment resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTensorboardName(tensorboardName)
matchProjectFromTensorboardName(tensorboardName: string): string | number;
Parse the project from Tensorboard resource.
Name | Description |
tensorboardName |
string
A fully-qualified path representing Tensorboard resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTensorboardRunName(tensorboardRunName)
matchProjectFromTensorboardRunName(tensorboardRunName: string): string | number;
Parse the project from TensorboardRun resource.
Name | Description |
tensorboardRunName |
string
A fully-qualified path representing TensorboardRun resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchProjectFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the project from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTrainingPipelineName(trainingPipelineName)
matchProjectFromTrainingPipelineName(trainingPipelineName: string): string | number;
Parse the project from TrainingPipeline resource.
Name | Description |
trainingPipelineName |
string
A fully-qualified path representing TrainingPipeline resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchProjectFromTrialName(trialName)
matchProjectFromTrialName(trialName: string): string | number;
Parse the project from Trial resource.
Name | Description |
trialName |
string
A fully-qualified path representing Trial resource. |
Type | Description |
string | number | {string} A string representing the project. |
matchRunFromTensorboardRunName(tensorboardRunName)
matchRunFromTensorboardRunName(tensorboardRunName: string): string | number;
Parse the run from TensorboardRun resource.
Name | Description |
tensorboardRunName |
string
A fully-qualified path representing TensorboardRun resource. |
Type | Description |
string | number | {string} A string representing the run. |
matchRunFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchRunFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the run from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the run. |
matchSliceFromModelEvaluationSliceName(modelEvaluationSliceName)
matchSliceFromModelEvaluationSliceName(modelEvaluationSliceName: string): string | number;
Parse the slice from ModelEvaluationSlice resource.
Name | Description |
modelEvaluationSliceName |
string
A fully-qualified path representing ModelEvaluationSlice resource. |
Type | Description |
string | number | {string} A string representing the slice. |
matchSpecialistPoolFromSpecialistPoolName(specialistPoolName)
matchSpecialistPoolFromSpecialistPoolName(specialistPoolName: string): string | number;
Parse the specialist_pool from SpecialistPool resource.
Name | Description |
specialistPoolName |
string
A fully-qualified path representing SpecialistPool resource. |
Type | Description |
string | number | {string} A string representing the specialist_pool. |
matchStudyFromStudyName(studyName)
matchStudyFromStudyName(studyName: string): string | number;
Parse the study from Study resource.
Name | Description |
studyName |
string
A fully-qualified path representing Study resource. |
Type | Description |
string | number | {string} A string representing the study. |
matchStudyFromTrialName(trialName)
matchStudyFromTrialName(trialName: string): string | number;
Parse the study from Trial resource.
Name | Description |
trialName |
string
A fully-qualified path representing Trial resource. |
Type | Description |
string | number | {string} A string representing the study. |
matchTensorboardFromTensorboardExperimentName(tensorboardExperimentName)
matchTensorboardFromTensorboardExperimentName(tensorboardExperimentName: string): string | number;
Parse the tensorboard from TensorboardExperiment resource.
Name | Description |
tensorboardExperimentName |
string
A fully-qualified path representing TensorboardExperiment resource. |
Type | Description |
string | number | {string} A string representing the tensorboard. |
matchTensorboardFromTensorboardName(tensorboardName)
matchTensorboardFromTensorboardName(tensorboardName: string): string | number;
Parse the tensorboard from Tensorboard resource.
Name | Description |
tensorboardName |
string
A fully-qualified path representing Tensorboard resource. |
Type | Description |
string | number | {string} A string representing the tensorboard. |
matchTensorboardFromTensorboardRunName(tensorboardRunName)
matchTensorboardFromTensorboardRunName(tensorboardRunName: string): string | number;
Parse the tensorboard from TensorboardRun resource.
Name | Description |
tensorboardRunName |
string
A fully-qualified path representing TensorboardRun resource. |
Type | Description |
string | number | {string} A string representing the tensorboard. |
matchTensorboardFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchTensorboardFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the tensorboard from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the tensorboard. |
matchTimeSeriesFromTensorboardTimeSeriesName(tensorboardTimeSeriesName)
matchTimeSeriesFromTensorboardTimeSeriesName(tensorboardTimeSeriesName: string): string | number;
Parse the time_series from TensorboardTimeSeries resource.
Name | Description |
tensorboardTimeSeriesName |
string
A fully-qualified path representing TensorboardTimeSeries resource. |
Type | Description |
string | number | {string} A string representing the time_series. |
matchTrainingPipelineFromTrainingPipelineName(trainingPipelineName)
matchTrainingPipelineFromTrainingPipelineName(trainingPipelineName: string): string | number;
Parse the training_pipeline from TrainingPipeline resource.
Name | Description |
trainingPipelineName |
string
A fully-qualified path representing TrainingPipeline resource. |
Type | Description |
string | number | {string} A string representing the training_pipeline. |
matchTrialFromTrialName(trialName)
matchTrialFromTrialName(trialName: string): string | number;
Parse the trial from Trial resource.
Name | Description |
trialName |
string
A fully-qualified path representing Trial resource. |
Type | Description |
string | number | {string} A string representing the trial. |
metadataSchemaPath(project, location, metadataStore, metadataSchema)
metadataSchemaPath(project: string, location: string, metadataStore: string, metadataSchema: string): string;
Return a fully-qualified metadataSchema resource name string.
Name | Description |
project |
string
|
location |
string
|
metadataStore |
string
|
metadataSchema |
string
|
Type | Description |
string | {string} Resource name string. |
metadataStorePath(project, location, metadataStore)
metadataStorePath(project: string, location: string, metadataStore: string): string;
Return a fully-qualified metadataStore resource name string.
Name | Description |
project |
string
|
location |
string
|
metadataStore |
string
|
Type | Description |
string | {string} Resource name string. |
modelDeploymentMonitoringJobPath(project, location, modelDeploymentMonitoringJob)
modelDeploymentMonitoringJobPath(project: string, location: string, modelDeploymentMonitoringJob: string): string;
Return a fully-qualified modelDeploymentMonitoringJob resource name string.
Name | Description |
project |
string
|
location |
string
|
modelDeploymentMonitoringJob |
string
|
Type | Description |
string | {string} Resource name string. |
modelEvaluationPath(project, location, model, evaluation)
modelEvaluationPath(project: string, location: string, model: string, evaluation: string): string;
Return a fully-qualified modelEvaluation resource name string.
Name | Description |
project |
string
|
location |
string
|
model |
string
|
evaluation |
string
|
Type | Description |
string | {string} Resource name string. |
modelEvaluationSlicePath(project, location, model, evaluation, slice)
modelEvaluationSlicePath(project: string, location: string, model: string, evaluation: string, slice: string): string;
Return a fully-qualified modelEvaluationSlice resource name string.
Name | Description |
project |
string
|
location |
string
|
model |
string
|
evaluation |
string
|
slice |
string
|
Type | Description |
string | {string} Resource name string. |
modelPath(project, location, model)
modelPath(project: string, location: string, model: string): string;
Return a fully-qualified model resource name string.
Name | Description |
project |
string
|
location |
string
|
model |
string
|
Type | Description |
string | {string} Resource name string. |
pipelineJobPath(project, location, pipelineJob)
pipelineJobPath(project: string, location: string, pipelineJob: string): string;
Return a fully-qualified pipelineJob resource name string.
Name | Description |
project |
string
|
location |
string
|
pipelineJob |
string
|
Type | Description |
string | {string} Resource name string. |
projectPath(project)
projectPath(project: string): string;
Return a fully-qualified project resource name string.
Name | Description |
project |
string
|
Type | Description |
string | {string} Resource name string. |
readTensorboardBlobData(request, options)
readTensorboardBlobData(request?: protos.google.cloud.aiplatform.v1.IReadTensorboardBlobDataRequest, options?: CallOptions): gax.CancellableStream;
Gets bytes of TensorboardBlobs. This is to allow reading blob data stored in consumer project's Cloud Storage bucket without users having to obtain Cloud Storage access permission.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IReadTensorboardBlobDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
gax.CancellableStream | {Stream} An object stream which emits [ReadTensorboardBlobDataResponse] on 'data' event. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#server-streaming) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardTimeSeries to list Blobs.
* Format:
* 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}'
*/
// const timeSeries = 'abc123'
/**
* IDs of the blobs to read.
*/
// const blobIds = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callReadTensorboardBlobData() {
// Construct request
const request = {
timeSeries,
};
// Run request
const stream = await aiplatformClient.readTensorboardBlobData(request);
stream.on('data', (response) => { console.log(response) });
stream.on('error', (err) => { throw(err) });
stream.on('end', () => { /* API call completed */ });
}
callReadTensorboardBlobData();
readTensorboardTimeSeriesData(request, options)
readTensorboardTimeSeriesData(request?: protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse,
(protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | undefined),
{} | undefined
]>;
Reads a TensorboardTimeSeries' data. By default, if the number of data points stored is less than 1000, all data will be returned. Otherwise, 1000 data points will be 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.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse, (protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [ReadTensorboardTimeSeriesDataResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardTimeSeries to read data from.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const tensorboardTimeSeries = 'abc123'
/**
* The maximum number of TensorboardTimeSeries' data to return.
* This value should be a positive integer.
* This value can be set to -1 to return all data.
*/
// const maxDataPoints = 1234
/**
* Reads the TensorboardTimeSeries' data that match the filter expression.
*/
// const filter = 'abc123'
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callReadTensorboardTimeSeriesData() {
// Construct request
const request = {
tensorboardTimeSeries,
};
// Run request
const response = await aiplatformClient.readTensorboardTimeSeriesData(request);
console.log(response);
}
callReadTensorboardTimeSeriesData();
readTensorboardTimeSeriesData(request, options, callback)
readTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
readTensorboardTimeSeriesData(request, callback)
readTensorboardTimeSeriesData(request: protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataResponse, protos.google.cloud.aiplatform.v1.IReadTensorboardTimeSeriesDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
specialistPoolPath(project, location, specialistPool)
specialistPoolPath(project: string, location: string, specialistPool: string): string;
Return a fully-qualified specialistPool resource name string.
Name | Description |
project |
string
|
location |
string
|
specialistPool |
string
|
Type | Description |
string | {string} Resource name string. |
studyPath(project, location, study)
studyPath(project: string, location: string, study: string): string;
Return a fully-qualified study resource name string.
Name | Description |
project |
string
|
location |
string
|
study |
string
|
Type | Description |
string | {string} Resource name string. |
tensorboardExperimentPath(project, location, tensorboard, experiment)
tensorboardExperimentPath(project: string, location: string, tensorboard: string, experiment: string): string;
Return a fully-qualified tensorboardExperiment resource name string.
Name | Description |
project |
string
|
location |
string
|
tensorboard |
string
|
experiment |
string
|
Type | Description |
string | {string} Resource name string. |
tensorboardPath(project, location, tensorboard)
tensorboardPath(project: string, location: string, tensorboard: string): string;
Return a fully-qualified tensorboard resource name string.
Name | Description |
project |
string
|
location |
string
|
tensorboard |
string
|
Type | Description |
string | {string} Resource name string. |
tensorboardRunPath(project, location, tensorboard, experiment, run)
tensorboardRunPath(project: string, location: string, tensorboard: string, experiment: string, run: string): string;
Return a fully-qualified tensorboardRun resource name string.
Name | Description |
project |
string
|
location |
string
|
tensorboard |
string
|
experiment |
string
|
run |
string
|
Type | Description |
string | {string} Resource name string. |
tensorboardTimeSeriesPath(project, location, tensorboard, experiment, run, timeSeries)
tensorboardTimeSeriesPath(project: string, location: string, tensorboard: string, experiment: string, run: string, timeSeries: string): string;
Return a fully-qualified tensorboardTimeSeries resource name string.
Name | Description |
project |
string
|
location |
string
|
tensorboard |
string
|
experiment |
string
|
run |
string
|
timeSeries |
string
|
Type | Description |
string | {string} Resource name string. |
trainingPipelinePath(project, location, trainingPipeline)
trainingPipelinePath(project: string, location: string, trainingPipeline: string): string;
Return a fully-qualified trainingPipeline resource name string.
Name | Description |
project |
string
|
location |
string
|
trainingPipeline |
string
|
Type | Description |
string | {string} Resource name string. |
trialPath(project, location, study, trial)
trialPath(project: string, location: string, study: string, trial: string): string;
Return a fully-qualified trial resource name string.
Name | Description |
project |
string
|
location |
string
|
study |
string
|
trial |
string
|
Type | Description |
string | {string} Resource name string. |
updateTensorboard(request, options)
updateTensorboard(request?: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest, options?: CallOptions): Promise<[
LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>,
protos.google.longrunning.IOperation | undefined,
{} | undefined
]>;
Updates a Tensorboard.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | undefined, {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing a long running operation. Its |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. Field mask is used to specify the fields to be overwritten in the
* Tensorboard resource by the update.
* The fields specified in the update_mask are relative to the resource, not
* the full request. A field will be overwritten if it is in the mask. If the
* user does not provide a mask then all fields will be overwritten if new
* values are specified.
*/
// const updateMask = {}
/**
* Required. The Tensorboard's `name` field is used to identify the
* Tensorboard to be updated. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
*/
// const tensorboard = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callUpdateTensorboard() {
// Construct request
const request = {
updateMask,
tensorboard,
};
// Run request
const [operation] = await aiplatformClient.updateTensorboard(request);
const [response] = await operation.promise();
console.log(response);
}
callUpdateTensorboard();
updateTensorboard(request, options, callback)
updateTensorboard(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest, options: CallOptions, callback: Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest
|
options |
CallOptions
|
callback |
Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboard(request, callback)
updateTensorboard(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest, callback: Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRequest
|
callback |
Callback<LROperation<protos.google.cloud.aiplatform.v1.ITensorboard, protos.google.cloud.aiplatform.v1.IUpdateTensorboardOperationMetadata>, protos.google.longrunning.IOperation | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardExperiment(request, options)
updateTensorboardExperiment(request?: protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardExperiment,
(protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | undefined),
{} | undefined
]>;
Updates a TensorboardExperiment.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardExperiment, (protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardExperiment]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. Field mask is used to specify the fields to be overwritten in the
* TensorboardExperiment resource by the update.
* The fields specified in the update_mask are relative to the resource, not
* the full request. A field will be overwritten if it is in the mask. If the
* user does not provide a mask then all fields will be overwritten if new
* values are specified.
*/
// const updateMask = {}
/**
* Required. The TensorboardExperiment's `name` field is used to identify the
* TensorboardExperiment to be updated. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const tensorboardExperiment = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callUpdateTensorboardExperiment() {
// Construct request
const request = {
updateMask,
tensorboardExperiment,
};
// Run request
const response = await aiplatformClient.updateTensorboardExperiment(request);
console.log(response);
}
callUpdateTensorboardExperiment();
updateTensorboardExperiment(request, options, callback)
updateTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardExperiment(request, callback)
updateTensorboardExperiment(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardExperiment, protos.google.cloud.aiplatform.v1.IUpdateTensorboardExperimentRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardRun(request, options)
updateTensorboardRun(request?: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardRun,
(protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | undefined),
{} | undefined
]>;
Updates a TensorboardRun.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardRun, (protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardRun]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. Field mask is used to specify the fields to be overwritten in the
* TensorboardRun resource by the update.
* The fields specified in the update_mask are relative to the resource, not
* the full request. A field will be overwritten if it is in the mask. If the
* user does not provide a mask then all fields will be overwritten if new
* values are specified.
*/
// const updateMask = {}
/**
* Required. The TensorboardRun's `name` field is used to identify the TensorboardRun to
* be updated. Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const tensorboardRun = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callUpdateTensorboardRun() {
// Construct request
const request = {
updateMask,
tensorboardRun,
};
// Run request
const response = await aiplatformClient.updateTensorboardRun(request);
console.log(response);
}
callUpdateTensorboardRun();
updateTensorboardRun(request, options, callback)
updateTensorboardRun(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardRun(request, callback)
updateTensorboardRun(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardRun, protos.google.cloud.aiplatform.v1.IUpdateTensorboardRunRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardTimeSeries(request, options)
updateTensorboardTimeSeries(request?: protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries,
(protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | undefined),
{} | undefined
]>;
Updates a TensorboardTimeSeries.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, (protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [TensorboardTimeSeries]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. Field mask is used to specify the fields to be overwritten in the
* TensorboardTimeSeries resource by the update.
* The fields specified in the update_mask are relative to the resource, not
* the full request. A field will be overwritten if it is in the mask. If the
* user does not provide a mask then all fields will be overwritten if new
* values are specified.
*/
// const updateMask = {}
/**
* Required. The TensorboardTimeSeries' `name` field is used to identify the
* TensorboardTimeSeries to be updated.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}`
*/
// const tensorboardTimeSeries = {}
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callUpdateTensorboardTimeSeries() {
// Construct request
const request = {
updateMask,
tensorboardTimeSeries,
};
// Run request
const response = await aiplatformClient.updateTensorboardTimeSeries(request);
console.log(response);
}
callUpdateTensorboardTimeSeries();
updateTensorboardTimeSeries(request, options, callback)
updateTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
updateTensorboardTimeSeries(request, callback)
updateTensorboardTimeSeries(request: protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest, callback: Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.ITensorboardTimeSeries, protos.google.cloud.aiplatform.v1.IUpdateTensorboardTimeSeriesRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
writeTensorboardExperimentData(request, options)
writeTensorboardExperimentData(request?: protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse,
(protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | undefined),
{} | undefined
]>;
Write time series data points of multiple TensorboardTimeSeries in multiple TensorboardRun's. If any data fail to be ingested, an error will be returned.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse, (protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [WriteTensorboardExperimentDataResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardExperiment to write data to.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}`
*/
// const tensorboardExperiment = 'abc123'
/**
* Required. Requests containing per-run TensorboardTimeSeries data to write.
*/
// const writeRunDataRequests = 1234
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callWriteTensorboardExperimentData() {
// Construct request
const request = {
tensorboardExperiment,
writeRunDataRequests,
};
// Run request
const response = await aiplatformClient.writeTensorboardExperimentData(request);
console.log(response);
}
callWriteTensorboardExperimentData();
writeTensorboardExperimentData(request, options, callback)
writeTensorboardExperimentData(request: protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
writeTensorboardExperimentData(request, callback)
writeTensorboardExperimentData(request: protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardExperimentDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
writeTensorboardRunData(request, options)
writeTensorboardRunData(request?: protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest, options?: CallOptions): Promise<[
protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse,
(protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | undefined),
{} | undefined
]>;
Write time series data points into multiple TensorboardTimeSeries under a TensorboardRun. If any data fail to be ingested, an error will be returned.
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest
The request object that will be sent. |
options |
CallOptions
Call options. See CallOptions for more details. |
Type | Description |
Promise<[ protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse, (protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | undefined), {} | undefined ]> | {Promise} - The promise which resolves to an array. The first element of the array is an object representing [WriteTensorboardRunDataResponse]. Please see the [documentation](https://github.com/googleapis/gax-nodejs/blob/master/client-libraries.md#regular-methods) for more details and examples. |
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
/**
* Required. The resource name of the TensorboardRun to write data to.
* Format:
* `projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}`
*/
// const tensorboardRun = 'abc123'
/**
* Required. The TensorboardTimeSeries data to write.
* Values with in a time series are indexed by their step value.
* Repeated writes to the same step will overwrite the existing value for that
* step.
* The upper limit of data points per write request is 5000.
*/
// const timeSeriesData = 1234
// Imports the Aiplatform library
const {TensorboardServiceClient} = require('@google-cloud/aiplatform').v1;
// Instantiates a client
const aiplatformClient = new TensorboardServiceClient();
async function callWriteTensorboardRunData() {
// Construct request
const request = {
tensorboardRun,
timeSeriesData,
};
// Run request
const response = await aiplatformClient.writeTensorboardRunData(request);
console.log(response);
}
callWriteTensorboardRunData();
writeTensorboardRunData(request, options, callback)
writeTensorboardRunData(request: protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest, options: CallOptions, callback: Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest
|
options |
CallOptions
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |
writeTensorboardRunData(request, callback)
writeTensorboardRunData(request: protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest, callback: Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | null | undefined, {} | null | undefined>): void;
Name | Description |
request |
protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest
|
callback |
Callback<protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataResponse, protos.google.cloud.aiplatform.v1.IWriteTensorboardRunDataRequest | null | undefined, {} | null | undefined>
|
Type | Description |
void |