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* Added categories and search based filtering to the resource dialog. (#12658) * added filtering to the resource type along with a new component. * -Added caching of cards -Removed unused component props -localized tags -limited the scope of list items * Made some changes in the PR * - Added Iot Category to SQL edge - Moved category names to constants - Moved localization strings to localized constants - Made filtering logic more concise - Changed how category list is generated --Category list can now be ordered -Added back event generation for selectedCard * Fixed bugs, and some additional changes -Fixed radiogroup height to avoid the movement of options below it -Restoring the focus back to the search and listview components - Added focus behaviour for listview - Fixed a typo in comment * Made categories an Enum * Added localized string * localized category string converted categories to enum. * made the filtering logic more concise. * returning string if no localized string formed removed unnecessary returns * fixed the filtering tag logic resetting search when category is changed * removing the iot tag from sql edge deployment * made filtering logic more concise made enum const * added vscode list * some cleanup * Some PR changes - Made PR camelcase - added comments to SQL - removed unnecessary export * -Some PR related changes -Removing unsupported style property -scoping down css and removing unused ones. * Fixed a comment text * Fixed typings for listview event * Adding tags to azure sql deployment
Machine Learning extension for Azure Data Studio
The Machine Learning extension for Azure Data Studio enables you to manage packages, import machine learning models, make predictions, and create notebooks to run experiments for SQL databases.
For more information, see the Machine Learning extension documentation.
Installation
Find the Machine Learning extension in Azure Data Studio and install the latest available version.
The following prerequisites need to be installed on the computer you run Azure Data Studio on:
- Python 3. Specify the local path to a preexisting Python installation under Settings. If you have used a Python kernel notebook in Azure Data Studio, the extension will use the path from the notebook by default.
- Microsoft ODBC driver 17 for SQL Server for Windows, macOS, or Linux.
- R 3.5 (optional). Enable R and specify the local path to a preexisting R installation under Settings. This is only required if you want to manage R packages in your database.
For more information on how to install and configure the prerequisites, see the Machine Learning extension documentation.
Manage packages
You can install and uninstall Python and R packages in your SQL database with Azure Data Studio. The packages you install can be used in Python or R scripts running in-database using the sp_execute_external_script T-SQL statement. This feature is currently limited to work with SQL Server Machine Learning Services.
Click on Manage packages in database to install or uninstall a Python or R package. For more information, see how to manage packages with the Machine Learning extension.
Make predictions
With the extension, you can use an ONNX model to make predictions. The model can either be an existing model stored in your database or an imported model. This feature is currently limited to work with Azure SQL Edge.
Click on Make predictions and choose between importing an ONNX model or use an existing model stored in your database. A T-SQL script will then be generated, which you can use to make predictions. For more information, see how to make predictions with the Machine Learning extension.
Import models
The Machine Learning extension can import ONNX models into your database. You can then use these models to make predictions. This feature is currently limited to work with Azure SQL Edge.
Click on Import models and choose between importing a model from a file or from Azure Machine Learning. For more information, see how to import models with the Machine Learning extension.
Create notebook
You can run experiments and create models in Python with a notebook in Azure Data Studio. You can also run T-SQL code, and run Python and R with SQL Server Machine Learning Services, in a notebook.
Click on Create notebook to create a new notebook in Azure Data Studio. For more information, see how to create a notebook with the Machine Learning extension.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Privacy Statement
The Microsoft Enterprise and Developer Privacy Statement describes the privacy statement of this software.
License
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Source EULA.