We add many cool features in this release, which requires recent version of Visual Studio and upgrading CUDA / AI frameworks.
Since setting up deep learning and machine learning software as well as their dependencies is not an easy task, we recommend that you use the one-click installer to install them automatically.
- Infuse AI into your apps with Microsoft Cognitive Services to see, hear, speak, understand and interpret your user needs through natural methods of communication.
- Add Cognitive Services via GUI wizard and manage them in Visual Studio.
- Build Cognitive Service apps from pre-defined project templates.
- Train and manage your own Custom Vision model from the generated training project or web portal.
- Build intelligent apps using pre-trained AI models with the Microsoft.ML.Scoring library
- Once training is complete, building intelligent apps in Visual Studio is as easy as putting your trained model in your app just like any other library or resource.
- Visual Studio Tools for AI makes this easy by enabling you to create a Model Inference Library project which automatically optimizes your ONNX/TensorFlow model for serving, as well as placing on optimized ONNX/TensorFlow runtime in the project.
- Moreover, Visual Studio Tools for AI generates a C# stub class to simplify interaction with models in your app.
- These Model Inference Library projects can be further deployed as NuGet packages for convenient distribution.
- Interoperation between different AI frameworks through model file conversion.
- Convert Core ML, TensorFlow, scikit-learn, XGBoost and LIBSVM models to ONNX format.
- Integration with WinMLTools, ONNXMLTools and tf2onnx converters.
- View network architecture and parameters of AI models in your training and inference projects. Please install the latest version of Netron.
- Run remote machine jobs in Docker.
- Log in automatically to your password-based remote machines (e.g. Azure DSVM/DLVM). Please install the latest version of Putty, and set the directory of putty.exe to %Path% environment variable.
- Add PyTorch application template.
- Experimental support for Open Platform for AI (PAI).
- Submit training projects to PAI clusters.
- Manage jobs and files with GUI tools.
- Bug fixes and stability improvements.
- Improve the experience of creating Azure ML projects from existing Visual Studio projects.
- Improve the UI layout and parameter validation of Azure Batch AI cluster creation and job submission to keep consistent with Azure web portal.
- Connect to a remote machine with OpenSSH client.
- Bug fixes and stability improvements.
- Check extension dependencies such as Python Tools, and automatically install them if necessary for VS 2017 extension.
- Refine the job submission experiences of Azure Batch AI, and support NFS servers.
- Bug fixes and stability improvements.
- Initial version.