Class v1beta1.EndpointServiceClient (1.14.1)

  /**
   * 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.
   *  This value should be 1-10 characters, and valid characters are /[0-9]/.
   *  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();

  /**
   * 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();

  /**
   * 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 = 1234

  // 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();

  /**
   * 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 = 1234

  // 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();

  /**
   * 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.
   *  This value should be 1-10 characters, and valid characters are /[0-9]/.
   *  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();

  /**
   * 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();

  /**
   * 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 = 1234

  // 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();

  /**
   * 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();

  /**
   * 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();