Merge pull request #426 from andresruizfacebook/patch-2

Update 2020-7-20-pytorch-1.6-released.md

Author: andresruizfacebook

_posts/2020-7-20-pytorch-1.6-released.md

@@ -4,67 +4,39 @@ title: 'PyTorch 1.6 released w/ Native AMP Support, Microsoft joins as maintaine
 author: Team PyTorch
 ---
 
-Today, we’re announcing the availability of PyTorch 1.6, along with updated domain libraries. We are also excited to announce the team at Microsoft is now maintaining Windows builds and binaries and will also be supporting the community on GitHub as well as the [PyTorch Windows discussion forums](https://discuss.pytorch.org/c/windows/).
+Maxim Lukiyanov - Principal PM at Microsoft, Emad Barsoum - Group EM at Microsoft, Guoliang Hua - Principal EM at Microsoft, Nikita Shulga - Tech Lead at Facebook, Geeta Chauhan - PE Lead at Facebook, Chris Gottbrath - Technical PM at Facebook, Jiachen Pu [(peterjc123)](https://github.com/peterjc123) - Engineer at Facebook
+
+Along with the PyTorch 1.6 release, we are excited to announce that Microsoft has expanded its participation in the PyTorch community and is taking ownership of the development and maintenance of the PyTorch build for Windows.
+
+According to the latest [Stack Overflow developer survey](https://insights.stackoverflow.com/survey/2020#technology-developers-primary-operating-systems), Windows remains the primary operating system for the developer community (46% Windows vs 28% MacOS). Jiachen Pu (peterjc123) initially made a heroic effort to add support for PyTorch on Windows, but due to limited resources, Windows support for PyTorch has lagged behind other platforms. Lack of test coverage resulted in unexpected issues popping up every now and then. Some of the core tutorials, meant for new users to learn and adopt PyTorch, would fail to run. The installation experience was also not as smooth, with the lack of official PyPI support for PyTorch on Windows. Lastly, some of the PyTorch functionality was simply not available on the Windows platform, such as the TorchAudio domain library and distributed training support. To help alleviate this pain, Microsoft is happy to bring its Windows expertise to the table and bring PyTorch on Windows to its best possible self.
+
+In the PyTorch 1.6 release, we have improved the core quality of the Windows build by bringing test coverage up to par with Linux for core PyTorch and its domain libraries and by automating tutorial testing. Thanks to the broader PyTorch community, which contributed TorchAudio support to Windows, we were able to add test coverage to all three domain libraries: TorchVision, TorchText and TorchAudio. In subsequent releases of PyTorch, we will continue improving the Windows experience based on community feedback and requests. So far, the feedback we received from the community points to distributed training support and a better installation experience using pip as the next areas of improvement. 
+
+In addition to the native Windows experience, Microsoft released a preview adding [GPU compute support to Windows Subsystem for Linux (WSL) 2](https://blogs.windows.com/windowsdeveloper/2020/06/17/gpu-accelerated-ml-training-inside-the-windows-subsystem-for-linux/) distros, with a focus on enabling AI and ML developer workflows. WSL is designed for developers that want to run any Linux based tools directly on Windows. This preview enables valuable scenarios for a variety of frameworks and Python packages that utilize [NVIDIA CUDA](https://blogs.windows.com/windowsdeveloper/2020/06/17/gpu-accelerated-ml-training-inside-the-windows-subsystem-for-linux/) for acceleration and only support Linux. This means WSL customers using the preview can run native Linux based PyTorch applications on Windows unmodified without the need for a traditional virtual machine or a dual boot setup.
+
+## Getting started with PyTorch on Windows
+It's easy to get started with PyTorch on Windows. To install PyTorch using Anaconda with the latest GPU support, run the command below. To install different supported configurations of PyTorch, refer to the installation instructions on [pytorch.org](https://pytorch.org/get-started/locally/).
+
+  conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+
+Once you install PyTorch, learn more by visiting the [PyTorch Tutorials](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) and [documentation](https://pytorch.org/docs/stable/index.html).
 
-## TUTORIALS HOME PAGE UPDATE
-The tutorials home page now provides clear actions that developers can take. For new PyTorch users, there is an easy-to-discover button to take them directly to “A 60 Minute Blitz”. Right next to it, there is a button to view all recipes which are designed to teach specific features quickly with examples.
 
-<div class="text-center">
-  <img src="{{ site.url }}/assets/images/tutorialhomepage.png" width="100%">
-</div>
 
-In addition to the existing left navigation bar, tutorials can now be quickly filtered by multi-select tags. Let’s say you want to view all tutorials related to “Production” and “Quantization”. You can select the “Production” and “Quantization” filters as shown in the image shown below:
 
 <div class="text-center">
-  <img src="{{ site.url }}/assets/images/blockfiltering.png" width="100%">
+  <img src="{{ site.url }}/assets/images/pytorch1.6.png" width="100%">
 </div>
 
-The following additional resources can also be found at the bottom of the Tutorials homepage:
-* [PyTorch Cheat Sheet](https://pytorch.org/tutorials/beginner/ptcheat.html)
-* [PyTorch Examples](https://github.com/pytorch/examples)
-* [Tutorial on GitHub](https://github.com/pytorch/tutorials)
-
-## PYTORCH RECIPES
-Recipes are new bite-sized, actionable examples designed to teach researchers and developers how to use specific PyTorch features. Some notable new recipes include:
-* [Loading Data in PyTorch](https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html)
-* [Model Interpretability Using Captum](https://pytorch.org/tutorials/recipes/recipes/Captum_Recipe.html)
-* [How to Use TensorBoard](https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html)
-
-View the full recipes [here](http://pytorch.org/tutorials/recipes/recipes_index.html).
-
-## LEARNING PYTORCH
-This section includes tutorials designed for users new to PyTorch. Based on community feedback, we have made updates to the current [Deep Learning with PyTorch: A 60 Minute Blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) tutorial, one of our most popular tutorials for beginners. Upon completion, one can understand what PyTorch and neural networks are, and be able to build and train a simple image classification network. Updates include adding explanations to clarify output meanings and linking back to where users can read more in the docs, cleaning up confusing syntax errors, and reconstructing and explaining new concepts for easier readability.
-
-## DEPLOYING MODELS IN PRODUCTION
-This section includes tutorials for developers looking to take their PyTorch models to production. The tutorials include:
-* [Deploying PyTorch in Python via a REST API with Flask](https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html)
-* [Introduction to TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html)
-* [Loading a TorchScript Model in C++](https://pytorch.org/tutorials/advanced/cpp_export.html)
-* [Exploring a Model from PyTorch to ONNX and Running it using ONNX Runtime](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html)
-
-## FRONTEND APIS
-PyTorch provides a number of frontend API features that can help developers to code, debug, and validate their models more efficiently. This section includes tutorials that teach what these features are and how to use them. Some tutorials to highlight:
-* [Introduction to Named Tensors in PyTorch](https://pytorch.org/tutorials/intermediate/named_tensor_tutorial.html)
-* [Using the PyTorch C++ Frontend](https://pytorch.org/tutorials/advanced/cpp_frontend.html)
-* [Extending TorchScript with Custom C++ Operators](https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html)
-* [Extending TorchScript with Custom C++ Classes](https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html)
-* [Autograd in C++ Frontend](https://pytorch.org/tutorials/advanced/cpp_autograd.html)
-
-## MODEL OPTIMIZATION
-Deep learning models often consume large amounts of memory, power, and compute due to their complexity. This section provides tutorials for model optimization:
-* [Pruning](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html)
-* [Dynamic Quantization on BERT](https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html)
-* [Static Quantization with Eager Mode in PyTorch](https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html)
-
-## PARALLEL AND DISTRIBUTED TRAINING
-PyTorch provides features that can accelerate performance in research and production such as native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++. This section includes tutorials on parallel and distributed training:
-* [Single-Machine Model Parallel Best Practices](https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html)
-* [Getting started with Distributed Data Parallel](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)
-* [Getting started with Distributed RPC Framework](https://pytorch.org/tutorials/intermediate/rpc_tutorial.html)
-* [Implementing a Parameter Server Using Distributed RPC Framework](https://pytorch.org/tutorials/intermediate/rpc_param_server_tutorial.html)
-
-Making these improvements are just the first step of improving PyTorch.org for the community. Please submit your suggestions [here](https://github.com/pytorch/tutorials/pulls).
-
-Cheers,
-
-Team PyTorch
+## Getting started with PyTorch on Windows Subsystem for Linux
+The [preview of NVIDIA CUDA support in WSL](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) is now available to Windows Insiders running Build 20150 or higher. In WSL, the command to install PyTorch using Anaconda is the same as the above command for native Windows. If you prefer pip, use the command below.
+  
+  pip install torch torchvision
+
+You can use the same tutorials and documentation inside your WSL environment as on native Windows. This functionality is still in preview so if you run into issues with WSL please share feedback via the WSL [GitHub repo](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) or with NVIDIA CUDA support share via NVIDIA’s [Community Forum for CUDA on WSL](https://forums.developer.nvidia.com/c/accelerated-computing/cuda/cuda-on-windows-subsystem-for-linux/303).
+
+## Feedback
+If you find gaps in the PyTorch experience on Windows, please let us know on the [PyTorch discussion forum](https://discuss.pytorch.org/c/windows/26) or file an issue on [GitHub](https://discuss.pytorch.org/c/windows/26) using #module: windows label.
+
+
+