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TensorLake npm SDK Compromised in ChainDrop Shai-Hulud Credential-Stealing Attack
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@azure/arm-machinelearning
Advanced tools
A generated SDK for AzureMachineLearningServicesManagementClient.
This package contains an isomorphic SDK (runs both in Node.js and in browsers) for AzureMachineLearningServicesManagement client.
These APIs allow end users to operate on Azure Machine Learning Workspace resources.
Source code | Package (NPM) | API reference documentation | Samples
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@azure/arm-machinelearning packageInstall the AzureMachineLearningServicesManagement client library for JavaScript with npm:
npm install @azure/arm-machinelearning
AzureMachineLearningServicesManagementClientTo create a client object to access the AzureMachineLearningServicesManagement API, you will need the endpoint of your AzureMachineLearningServicesManagement resource and a credential. The AzureMachineLearningServicesManagement client can use Azure Active Directory credentials to authenticate.
You can find the endpoint for your AzureMachineLearningServicesManagement 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 AzureMachineLearningServicesManagement 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 { AzureMachineLearningServicesManagementClient } = require("@azure/arm-machinelearning");
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 AzureMachineLearningServicesManagementClient(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 AzureMachineLearningServicesManagementClient(credential, subscriptionId);
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.
AzureMachineLearningServicesManagementClient is the primary interface for developers using the AzureMachineLearningServicesManagement client library. Explore the methods on this client object to understand the different features of the AzureMachineLearningServicesManagement service that you can access.
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.
Please take a look at the samples directory for detailed examples on how to use this library.
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.

FAQs
A generated SDK for AzureMachineLearningServicesManagementClient.
The npm package @azure/arm-machinelearning receives a total of 3,918 weekly downloads. As such, @azure/arm-machinelearning popularity was classified as popular.
We found that @azure/arm-machinelearning demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.

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