Azure ML Team Account Management client library for JavaScript - version 2.0.0-beta.3

This package contains an isomorphic SDK (runs both in Node.js and in browsers) for Azure ML Team Account Management client.

These APIs allow end users to operate on Azure Machine Learning Team Account resources. They support CRUD operations for Azure Machine Learning Team Accounts.

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-machinelearningexperimentation package

Install the Azure ML Team Account Management client library for JavaScript with npm:

npm install @azure/arm-machinelearningexperimentation

Create and authenticate a MLTeamAccountManagementClient

To create a client object to access the Azure ML Team Account Management API, you will need the endpoint of your Azure ML Team Account Management resource and a credential. The Azure ML Team Account Management client can use Azure Active Directory credentials to authenticate. You can find the endpoint for your Azure ML Team Account 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 ML Team Account 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 { MLTeamAccountManagementClient } = require("@azure/arm-machinelearningexperimentation");
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 MLTeamAccountManagementClient(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 MLTeamAccountManagementClient(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

MLTeamAccountManagementClient

MLTeamAccountManagementClient is the primary interface for developers using the Azure ML Team Account Management client library. Explore the methods on this client object to understand the different features of the Azure ML Team Account 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