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Manage episode 300204756 series 2921809
Content provided by PyTorch, Edward Yang, and Team PyTorch. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by PyTorch, Edward Yang, and Team PyTorch or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
PyTorch operates on its input data in a batched manner, typically processing multiple batches of an input at once (rather than once at a time, as would be the case in typical programming). In this podcast, we talk a little about the implications of batching operations in this way, and then also about how PyTorch's API is structured for batching (hint: poorly) and how Numpy introduced a concept of ufunc/gufuncs to standardize over broadcasting and batching behavior. There is some overlap between this podcast and previous podcasts about TensorIterator and vmap; you may also be interested in those episodes.
Further reading.
- ufuncs and gufuncs https://numpy.org/doc/stable/reference/ufuncs.html and https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html
- A brief taxonomy of PyTorch operators by shape behavior http://blog.ezyang.com/2020/05/a-brief-taxonomy-of-pytorch-operators-by-shape-behavior/
- Related episodes on TensorIterator and vmap https://pytorch-dev-podcast.simplecast.com/episodes/tensoriterator and https://pytorch-dev-podcast.simplecast.com/episodes/vmap
83 episoade
Distribuie
Manage episode 300204756 series 2921809
Content provided by PyTorch, Edward Yang, and Team PyTorch. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by PyTorch, Edward Yang, and Team PyTorch or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
PyTorch operates on its input data in a batched manner, typically processing multiple batches of an input at once (rather than once at a time, as would be the case in typical programming). In this podcast, we talk a little about the implications of batching operations in this way, and then also about how PyTorch's API is structured for batching (hint: poorly) and how Numpy introduced a concept of ufunc/gufuncs to standardize over broadcasting and batching behavior. There is some overlap between this podcast and previous podcasts about TensorIterator and vmap; you may also be interested in those episodes.
Further reading.
- ufuncs and gufuncs https://numpy.org/doc/stable/reference/ufuncs.html and https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html
- A brief taxonomy of PyTorch operators by shape behavior http://blog.ezyang.com/2020/05/a-brief-taxonomy-of-pytorch-operators-by-shape-behavior/
- Related episodes on TensorIterator and vmap https://pytorch-dev-podcast.simplecast.com/episodes/tensoriterator and https://pytorch-dev-podcast.simplecast.com/episodes/vmap
83 episoade
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Similar cu PyTorch Developer Podcast
Africa-focused technology, digital and innovation ecosystem insight and commentary.
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continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
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continue reading
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continue reading
Welcome to Hands-On Tech, where host Mikah Sargent turns tech troubles into tech triumphs. Each episode zooms in on a specific theme, unpacking listener questions with expert analysis and easy-to-follow advice. From decoding the latest gadgets to simplifying everyday tech, Mikah's got you covered. Submit your tech queries through email at HOT@twit.tv or via TWiT's social media. You might hear your question answered on air! And keep an ear out for guest experts who drop by to share their spec ...
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continue reading
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