PyTorch a framework for research of dynamic deep learning models - Adam Paszke скачать в хорошем качестве

PyTorch a framework for research of dynamic deep learning models - Adam Paszke

PyTorch: a framework for research of dynamic deep learning models. How, why, and what's next? Adam Paszke Description PyTorch is one of deep learning frameworks that can natively support models without a predefined structure, and is now widely used at companies and universities all around the world. The talk aims to introduce its basic tensor library, automatic differentiation package, a set of helpers for constructing novel machine learning models, and discuss the future of the library. Abstract So, why do we need dynamical computational graphs again? Recently, there's been a rising interest in frameworks that can support dynamical computational graphs natively and there's a reason for that. This section will introduce the concept and discuss pros and cons compared to static graph frameworks. Ok, show me the code This is meant to showcase the different levels of abstraction at which PyTorch can be used. Here I'll start with the lowest one, which is a NumPy-like tensor library. No fancy features; Just a good old imperative paradigm sprinkled with some linear algebra (on GPUs too!). Automatic differentiation to the rescue! While having a NumPy alternative with a fast GPU backend is great, it still doesn't allow you to easily experiment with machine learning models. This section introduces torch.autograd - a package allowing to wrap tensors and automatically track their usage so as to compute derivatives of expressions automatically. High-level helpers Automatic differentiation is a huge leap forward, but writing out more complicated models can get pretty verbose. torch.nn has been created just for this purpose. It features over a hundred commonly used building blocks (not only for neural networks), just so that you can compose your model in a handful of lines. Extending the library So far so good, but we're talking about a research library. It's not always sufficient to rely on what has been provided for you. Also, there's no point in having the whole Python package ecosystem available to you, if you can't mix and match different libraries. No need to worry, we've thought about this too. In this section I'll show that it's straightforward to extend PyTorch with your own code, that will seamlessly integrate with already implemented modules. Next steps Finally, I'll discuss recently added features (distributed training) and ones that are under active development (just-in-time compiler for those really complicated models). www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...