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Update 2019-05-1-pytorch-adds-new-dev-tools.md
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@@ -29,7 +29,7 @@ Key features of PyTorch v1.1 include:
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- New APIs: Support for Boolean tensors and better support for custom recurrent neural networks.
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- Distributed Training: Improved performance for common models such as CNNs, added support for multi device modules including the ability to split models across GPUs while still using Distributed Data Parallel (DDP) and support for modules where not all parameters are used in every iteration (e.g. control flow, like adaptive softmax, etc). See the latest tutorials [here](https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html).
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We’ve also continued to partner with the community to foster projects and tools aimed at supporting ML engineers for needs ranging from improved model understanding to auto-tuning using AutoML methods. With the release of Ax and BoTorch (below), we will be sharing some of our core algorithms, including meta-learning for efficiently optimizing hyperparameters from based on historical tasks. We are exciting to see this work open-sourced for the community to build on.
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We’ve also continued to partner with the community to foster projects and tools aimed at supporting ML engineers for needs ranging from improved model understanding to auto-tuning using AutoML methods. With the release of Ax and BoTorch (below), we will be sharing some of our core algorithms, including meta-learning for efficiently optimizing hyperparameters from based on historical tasks. We are excited to see this work open-sourced for the community to build on.
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This ecosystem includes open source projects and tools that have been deployed at production scale, as well as products and services from our partnership with industry leaders who share our vision of an open and collaborative AI community. Here are a few of the latest tools:
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