Class v1beta1.EndpointServiceClient (3.10.1)

const client = longrunning.operationsClient();
await client.cancelOperation({name: ''});

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the Location to create the Endpoint in.
   *  Format: `projects/{project}/locations/{location}`
   */
  // const parent = 'abc123'
  /**
   *  Required. The Endpoint to create.
   */
  // const endpoint = {}
  /**
   *  Immutable. The ID to use for endpoint, which will become the final
   *  component of the endpoint resource name.
   *  If not provided, Vertex AI will generate a value for this ID.
   *  If the first character is a letter, this value may be up to 63 characters,
   *  and valid characters are `[a-z0-9-]`. The last character must be a letter
   *  or number.
   *  If the first character is a number, this value may be up to 9 characters,
   *  and valid characters are `[0-9]` with no leading zeros.
   *  When using HTTP/JSON, this field is populated
   *  based on a query string argument, such as `?endpoint_id=12345`. This is the
   *  fallback for fields that are not included in either the URI or the body.
   */
  // const endpointId = 'abc123'

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callCreateEndpoint() {
    // Construct request
    const request = {
      parent,
      endpoint,
      endpointId,
    };

    // Run request
    const [operation] = await aiplatformClient.createEndpoint(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callCreateEndpoint();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource to be deleted.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const name = 'abc123'

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callDeleteEndpoint() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await aiplatformClient.deleteEndpoint(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeleteEndpoint();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource into which to deploy a Model.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const endpoint = 'abc123'
  /**
   *  Required. The DeployedModel to be created within the Endpoint. Note that
   *  Endpoint.traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split 
   *  must be updated for the DeployedModel to start receiving traffic, either as
   *  part of this call, or via
   *  EndpointService.UpdateEndpoint google.cloud.aiplatform.v1beta1.EndpointService.UpdateEndpoint.
   */
  // const deployedModel = {}
  /**
   *  A map from a DeployedModel's ID to the percentage of this Endpoint's
   *  traffic that should be forwarded to that DeployedModel.
   *  If this field is non-empty, then the Endpoint's
   *  traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split 
   *  will be overwritten with it. To refer to the ID of the just being deployed
   *  Model, a "0" should be used, and the actual ID of the new DeployedModel
   *  will be filled in its place by this method. The traffic percentage values
   *  must add up to 100.
   *  If this field is empty, then the Endpoint's
   *  traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split  is
   *  not updated.
   */
  // const trafficSplit = [1,2,3,4]

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callDeployModel() {
    // Construct request
    const request = {
      endpoint,
      deployedModel,
    };

    // Run request
    const [operation] = await aiplatformClient.deployModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeployModel();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource into which to mutate a
   *  DeployedModel. Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const endpoint = 'abc123'
  /**
   *  Required. The DeployedModel to be mutated within the Endpoint. Only the
   *  following fields can be mutated:
   *  * `min_replica_count` in either
   *  DedicatedResources google.cloud.aiplatform.v1beta1.DedicatedResources  or
   *  AutomaticResources google.cloud.aiplatform.v1beta1.AutomaticResources 
   *  * `max_replica_count` in either
   *  DedicatedResources google.cloud.aiplatform.v1beta1.DedicatedResources  or
   *  AutomaticResources google.cloud.aiplatform.v1beta1.AutomaticResources 
   *  * autoscaling_metric_specs google.cloud.aiplatform.v1beta1.DedicatedResources.autoscaling_metric_specs 
   *  * `disable_container_logging` (v1 only)
   *  * `enable_container_logging` (v1beta1 only)
   */
  // const deployedModel = {}
  /**
   *  Required. The update mask applies to the resource. See
   *  google.protobuf.FieldMask google.protobuf.FieldMask.
   */
  // const updateMask = {}

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callMutateDeployedModel() {
    // Construct request
    const request = {
      endpoint,
      deployedModel,
      updateMask,
    };

    // Run request
    const [operation] = await aiplatformClient.mutateDeployedModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callMutateDeployedModel();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource from which to undeploy a Model.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const endpoint = 'abc123'
  /**
   *  Required. The ID of the DeployedModel to be undeployed from the Endpoint.
   */
  // const deployedModelId = 'abc123'
  /**
   *  If this field is provided, then the Endpoint's
   *  traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split 
   *  will be overwritten with it. If last DeployedModel is being undeployed from
   *  the Endpoint, the Endpoint.traffic_split  will always end up empty when
   *  this call returns. A DeployedModel will be successfully undeployed only if
   *  it doesn't have any traffic assigned to it when this method executes, or if
   *  this field unassigns any traffic to it.
   */
  // const trafficSplit = [1,2,3,4]

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callUndeployModel() {
    // Construct request
    const request = {
      endpoint,
      deployedModelId,
    };

    // Run request
    const [operation] = await aiplatformClient.undeployModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callUndeployModel();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the Location to create the Endpoint in.
   *  Format: `projects/{project}/locations/{location}`
   */
  // const parent = 'abc123'
  /**
   *  Required. The Endpoint to create.
   */
  // const endpoint = {}
  /**
   *  Immutable. The ID to use for endpoint, which will become the final
   *  component of the endpoint resource name.
   *  If not provided, Vertex AI will generate a value for this ID.
   *  If the first character is a letter, this value may be up to 63 characters,
   *  and valid characters are `[a-z0-9-]`. The last character must be a letter
   *  or number.
   *  If the first character is a number, this value may be up to 9 characters,
   *  and valid characters are `[0-9]` with no leading zeros.
   *  When using HTTP/JSON, this field is populated
   *  based on a query string argument, such as `?endpoint_id=12345`. This is the
   *  fallback for fields that are not included in either the URI or the body.
   */
  // const endpointId = 'abc123'

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callCreateEndpoint() {
    // Construct request
    const request = {
      parent,
      endpoint,
      endpointId,
    };

    // Run request
    const [operation] = await aiplatformClient.createEndpoint(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callCreateEndpoint();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource to be deleted.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const name = 'abc123'

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callDeleteEndpoint() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await aiplatformClient.deleteEndpoint(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeleteEndpoint();

const client = longrunning.operationsClient();
await client.deleteOperation({name: ''});

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource into which to deploy a Model.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const endpoint = 'abc123'
  /**
   *  Required. The DeployedModel to be created within the Endpoint. Note that
   *  Endpoint.traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split 
   *  must be updated for the DeployedModel to start receiving traffic, either as
   *  part of this call, or via
   *  EndpointService.UpdateEndpoint google.cloud.aiplatform.v1beta1.EndpointService.UpdateEndpoint.
   */
  // const deployedModel = {}
  /**
   *  A map from a DeployedModel's ID to the percentage of this Endpoint's
   *  traffic that should be forwarded to that DeployedModel.
   *  If this field is non-empty, then the Endpoint's
   *  traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split 
   *  will be overwritten with it. To refer to the ID of the just being deployed
   *  Model, a "0" should be used, and the actual ID of the new DeployedModel
   *  will be filled in its place by this method. The traffic percentage values
   *  must add up to 100.
   *  If this field is empty, then the Endpoint's
   *  traffic_split google.cloud.aiplatform.v1beta1.Endpoint.traffic_split  is
   *  not updated.
   */
  // const trafficSplit = [1,2,3,4]

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callDeployModel() {
    // Construct request
    const request = {
      endpoint,
      deployedModel,
    };

    // Run request
    const [operation] = await aiplatformClient.deployModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeployModel();

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The name of the Endpoint resource.
   *  Format:
   *  `projects/{project}/locations/{location}/endpoints/{endpoint}`
   */
  // const name = 'abc123'

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callGetEndpoint() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const response = await aiplatformClient.getEndpoint(request);
    console.log(response);
  }

  callGetEndpoint();

const [response] = await client.getLocation(request);

const client = longrunning.operationsClient();
const name = '';
const [response] = await client.getOperation({name});
// doThingsWith(response)

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the Location from which to list the
   *  Endpoints. Format: `projects/{project}/locations/{location}`
   */
  // const parent = 'abc123'
  /**
   *  Optional. An expression for filtering the results of the request. For field
   *  names both snake_case and camelCase are supported.
   *    * `endpoint` supports = and !=. `endpoint` represents the Endpoint ID,
   *      i.e. the last segment of the Endpoint's resource
   *      name google.cloud.aiplatform.v1beta1.Endpoint.name.
   *    * `display_name` supports = and, !=
   *    * `labels` supports general map functions that is:
   *      * `labels.key=value` - key:value equality
   *      * `labels.key:* or labels:key - key existence
   *      * A key including a space must be quoted. `labels."a key"`.
   *  Some examples:
   *    * `endpoint=1`
   *    * `displayName="myDisplayName"`
   *    * `labels.myKey="myValue"`
   */
  // const filter = 'abc123'
  /**
   *  Optional. The standard list page size.
   */
  // const pageSize = 1234
  /**
   *  Optional. The standard list page token.
   *  Typically obtained via
   *  ListEndpointsResponse.next_page_token google.cloud.aiplatform.v1beta1.ListEndpointsResponse.next_page_token 
   *  of the previous
   *  EndpointService.ListEndpoints google.cloud.aiplatform.v1beta1.EndpointService.ListEndpoints 
   *  call.
   */
  // const pageToken = 'abc123'
  /**
   *  Optional. Mask specifying which fields to read.
   */
  // const readMask = {}

  // Imports the Aiplatform library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform').v1beta1;

  // Instantiates a client
  const aiplatformClient = new EndpointServiceClient();

  async function callListEndpoints() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await aiplatformClient.listEndpointsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListEndpoints();

const iterable = client.listLocationsAsync(request);
for await (const response of iterable) {
  // process response
}

const client = longrunning.operationsClient();
for await (const response of client.listOperationsAsync(request));
// doThingsWith(response)