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Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
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Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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.

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Manage episode 300204756 series 2921809
Innhold levert av PyTorch, Edward Yang, and Team PyTorch. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av PyTorch, Edward Yang, and Team PyTorch eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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.

  continue reading

83 episoder

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