DEVELOPING USING MS VISUAL STUDIO INSTALL
DEVELOPING USING MS VISUAL STUDIO CODE

Learn more about creating deep learning projects in Visual Studio You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models.

DEVELOPING USING MS VISUAL STUDIO CODE

Use built-in code editor features like syntax highlighting, IntelliSense and text auto formatting. Use the productivity features of Visual Studio to accelerate AI innovation today. Develop, debug and deploy deep learning models and AI solutions Azure Batch AI support is temporarily removed, will get the support of Batch AI back soon.Moreover, a more proactive feedback channel is built - will prompt user to give feedback when some conditions met. Besides sending feedback in GitHub, now user can send feedback via Email too.And a new all-in-one docker image including all popular AI / DL frameworks is provided. Docker image list is updated for job submission to remote machines.

When creating a cognitive service, users can specify existing resource group or create a new one.

Azure Cognitive Services is organized by subscription in the Server Explorer.

The start page also contains useful AI examples and development resources. We design a new start page to help users build first AI application / train first AI Model within 3 steps.

DEVELOPING USING MS VISUAL STUDIO INSTALL

Users can launch a one-click installer from this page to install required software packages into the default Python environment.

When first starting Tools for AI, an installation page is shown for guiding local AI development environment setup.

Fix an issue that the most recent version of Visual Studio cannot install the extension.

Fix an issue that prevents users from importing certain ONNX model.

You can download a version of this extension compatible with Visual Studio 2015 here. NOTE: This extension is designed to work with Visual Studio 2017. Please visit our homepage for details on installation and usage. Get started with deep learning using Microsoft Cognitive Toolkit (CNTK), Google TensorFlow, PyTorch, Apache MXNet or other frameworks today. Enterprise ready collaboration, allow to securely work on project with other people. Additionally, it provides support for custom metrics and run history tracking, enabling data science reproducibility and auditing. It seamlessly integrates with Cloud AI services such as Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets. Visual Studio Tools for AI is an extension to build, test, and deploy Deep Learning / AI solutions.

Developing using ms visual studio