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Copy file name to clipboardExpand all lines: docs/ai/about-ai-tools.md
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Visual Studio Tools for AI is an extension to build, test, and deploy Deep Learning / AI solutions. It seamlessly integrates with Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets. Additionally, it provides support for custom metrics and run history tracking, enabling data science reproducibility and auditing. Enterprise ready collaboration, allow to securely work on project with other people.
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Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http://www.microsoft.com/en-us/cognitive-toolkit), [Google TensorFlow](https://www.tensorflow.org), or other deep-learning frameworks today.
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Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/cognitive-toolkit/), [Google TensorFlow](https://www.tensorflow.org), or other deep-learning frameworks today.
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## Develop, debug, and deploy deep learning models and AI solutions
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Use the productivity features of Visual Studio to accelerate AI innovation today. Use built-in code editor features like syntax highlighting, IntelliSense, and text auto formatting. You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models.
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Currently this extension supports Windows 64-bit operating systems.
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## Support
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Support for this extension is provided on our [GitHub Issue Tracker](http://github.com/Microsoft/vs-tools-for-ai/issues). You can submit a bug report or feature suggestion and participate in discussions.
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Support for this extension is provided on our [GitHub Issue Tracker](https://github.com/Microsoft/vs-tools-for-ai/issues). You can submit a bug report or feature suggestion and participate in discussions.
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# Create an AI project from the Azure Machine Learning Gallery in Visual Studio
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Azure Machine Learning is integrated with Visual Studio Tools for AI. You can use it to submit machine learning jobs to remote compute targets like Azure virtual machines, Spark clusters, and more. Learn more about [Azure Machine Learning Experimentation](https://docs.microsoft.com/azure/machine-learning/preview/experimentation-service-configuration)
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Azure Machine Learning is integrated with Visual Studio Tools for AI. You can use it to submit machine learning jobs to remote compute targets like Azure virtual machines, Spark clusters, and more.
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Once you've [installed Visual Studio Tools for AI](installation.md), it's easy to create a new Python project using pre-made recipes in the Azure Machine Learning Sample Gallery.
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7. Run in a Docker container or on your local machine
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### NVIDIA GPU driver
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Deep learning frameworks take advantage of NVIDIA GPU to let machines learn at a speed, accuracy, and scale towards true artificial intelligence. If your computer has NVIDIA GPU cards, see [NVIDIA Driver Downloads](http://www.nvidia.com/Download/index.aspx) or try an operating system update to install the latest driver.
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Deep learning frameworks take advantage of NVIDIA GPU to let machines learn at a speed, accuracy, and scale towards true artificial intelligence. If your computer has NVIDIA GPU cards, see [NVIDIA Driver Downloads](https://www.nvidia.com/Download/index.aspx) or try an operating system update to install the latest driver.
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### CUDA
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### PyTorch
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[PyTorch](http://pytorch.org/) is a python package that provides two high-level features:
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[PyTorch](https://pytorch.org/) is a python package that provides two high-level features:
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- Tensor computation (like numpy) with strong GPU acceleration
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- Deep Neural Networks built on a tape-based autograd system
> Job history submitted to Linux VMs is stored on the VM in the /tmp directory. Therefore, whenever it is rebooted the job history is cleared. For a permanent record of your job history, please configure your VM as a compute context in Azure Machine learning, then Submit Job to Azure Machine Learning (selecting your VM as the compute context).
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> Job history submitted to Linux VMs is stored on the VM in the /tmp directory. Therefore, whenever it is rebooted the job history is cleared. For a permanent record of your job history, please configure your VM as a compute context in Azure Machine learning, then Submit Job to Azure Machine Learning (selecting your VM as the compute context).
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> [Microsoft Azure Storage Explorer](/azure/vs-azure-tools-storage-manage-with-storage-explorer) is a free, standalone app from Microsoft. You can use it to work visually with Azure Storage data on Windows, macOS, and Linux.
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