d9b5d71148
* added doughnut chart component * Changing chart to doughnutChart * reverting to genreic chart component * adding more chart supoort * fix minor errors * resolve some PR comments * native chartjs, keyboard navigation and chart options * fix build errors * fix chart.js/auto error * resolve PR comments * modify chartdataset API * Refactoring (#24327) * working - displaying chart data with convert * working - introduced typed properties * working, added BarChartConfiguration to type param * removed ChartProperties type param * Adding doughnut support * Correcting number vs. point issue * including the right changes this time * commenting out no-longer-used labels prop * remove hardcoded canvasID, enabled Scatterplot config * Moved graph testing to sample extension * Reorganizing types; adding test back to assessment dialog * Adding example for bubble chart * Polar area working * cleanup * adding draw when options isn't set * Moving chart example configs to other file * some cleanup * added some docstrings * add multiple datasets to test scatter plot * update scatter plot example in sample * Adding height/width support * swapping to `as` cast * title working * Settling chart title and legend display * Adding comments * updating data working * Updating samples * Typo in comment * Reverting changes made for development * Elaborating on color in docstrings * Separating Data and Options in component payloads * Removing chartId as an exposed property * Changing chartType property to TChartType * Fleshing out types file comments * fixing scoping of chart component properties; renaming chart canvas ID prop * correct internal chart options typing * removing commented-out code * removing unused ChartClickEvent type until data selection eventing is implemented * renaming function * deleted commented-out code * Adding options setters that went missing after splitting Config to Data + Options * adding type predicates for data conversion * Adding back type setting (dropped when chart type conversion moved) * Narrowing type for 'type' * Fixing typos in docstring --------- Co-authored-by: Deepak Saini <deepaksaini@microsoft.com> Co-authored-by: Charles Gagnon <chgagnon@microsoft.com> Co-authored-by: Aasim Khan <aaskhan@microsoft.com> Co-authored-by: Deepak Saini <deepak.saini1996@gmail.com>
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
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License
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Source EULA.