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Azure Machine Learning Compute Management client library for JavaScript - version 3.0.0-beta.3

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This package contains an isomorphic SDK (runs both in Node.js and in browsers) for Azure Machine Learning Compute Management client.

These APIs allow end users to operate on Azure Machine Learning Compute resources. They support the following operations:

  • Create or update a cluster
  • Get a cluster
  • Patch a cluster
  • Delete a cluster
  • Get keys for a cluster
  • Check if updates are available for system services in a cluster
  • Update system services in a cluster
  • Get all clusters in a resource group
  • Get all clusters in a subscription

Source code | Package (NPM) | API reference documentation | Samples

Getting started

Currently supported environments

See our support policy for more details.

Prerequisites

Install the @azure/arm-machinelearningcompute package

Install the Azure Machine Learning Compute Management client library for JavaScript with npm:

npm install @azure/arm-machinelearningcompute

Create and authenticate a MachineLearningComputeManagementClient

To create a client object to access the Azure Machine Learning Compute Management API, you will need the endpoint of your Azure Machine Learning Compute Management resource and a credential. The Azure Machine Learning Compute Management client can use Azure Active Directory credentials to authenticate. You can find the endpoint for your Azure Machine Learning Compute Management resource in the Azure Portal.

You can authenticate with Azure Active Directory using a credential from the @azure/identity library or an existing AAD Token.

To use the DefaultAzureCredential provider shown below, or other credential providers provided with the Azure SDK, please install the @azure/identity package:

npm install @azure/identity

You will also need to register a new AAD application and grant access to Azure Machine Learning Compute Management by assigning the suitable role to your service principal (note: roles such as "Owner" will not grant the necessary permissions). Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

For more information about how to create an Azure AD Application check out this guide.

const { MachineLearningComputeManagementClient } = require("@azure/arm-machinelearningcompute");
const { DefaultAzureCredential } = require("@azure/identity");
// For client-side applications running in the browser, use InteractiveBrowserCredential instead of DefaultAzureCredential. See https://aka.ms/azsdk/js/identity/examples for more details.

const subscriptionId = "00000000-0000-0000-0000-000000000000";
const client = new MachineLearningComputeManagementClient(new DefaultAzureCredential(), subscriptionId);

// For client-side applications running in the browser, use this code instead:
// const credential = new InteractiveBrowserCredential({
//   tenantId: "<YOUR_TENANT_ID>",
//   clientId: "<YOUR_CLIENT_ID>"
// });
// const client = new MachineLearningComputeManagementClient(credential, subscriptionId);

JavaScript Bundle

To use this client library in the browser, first you need to use a bundler. For details on how to do this, please refer to our bundling documentation.

Key concepts

MachineLearningComputeManagementClient

MachineLearningComputeManagementClient is the primary interface for developers using the Azure Machine Learning Compute Management client library. Explore the methods on this client object to understand the different features of the Azure Machine Learning Compute Management service that you can access.

Troubleshooting

Logging

Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:

const { setLogLevel } = require("@azure/logger");
setLogLevel("info");

For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.

Next steps

Please take a look at the samples directory for detailed examples on how to use this library.

Contributing

If you'd like to contribute to this library, please read the contributing guide to learn more about how to build and test the code.

Impressions