
PyTorch, Edward Yang, and Team PyTorch에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 PyTorch, Edward Yang, and Team PyTorch 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Anatomy of a domain library
Manage episode 295783831 series 2921809
PyTorch, Edward Yang, and Team PyTorch에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 PyTorch, Edward Yang, and Team PyTorch 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
What's a domain library? Why do they exist? What do they do for you? What should you know about developing in PyTorch main library versus in a domain library? How coupled are they with PyTorch as a whole? What's cool about working on domain libraries?
Further reading.
- The classic trio of domain libraries is https://pytorch.org/audio/stable/index.html https://pytorch.org/text/stable/index.html and https://pytorch.org/vision/stable/index.html
Line notes.
- why do domain libraries exist? lots of domains specific gadgets,
inappropriate for PyTorch - what does a domain library do
- operator implementations (old days: pure python, not anymore)
- with autograd support and cuda acceleration
- esp encoding/decoding, e.g., for domain file formats
- torchbind for custom objects
- takes care of getting the dependencies for you
- esp transformations, e.g., for data augmentation
- models, esp pretrained weights
- datasets
- reference scripts
- full wheel/conda packaging like pytorch
- mobile compatibility
- operator implementations (old days: pure python, not anymore)
- separate repos: external contributors with direct access
- manual sync to fbcode; a lot easier to land code! less
motion so lower risk
- manual sync to fbcode; a lot easier to land code! less
- coupling with pytorch? CI typically runs on nightlies
- pytorch itself tests against torchvision, canary against
extensibility mechanisms - mostly not using internal tools (e.g., TensorIterator),
too unstable (this would be good to fix)
- pytorch itself tests against torchvision, canary against
- closer to research side of pytorch; francesco also part of papers
83 에피소드
Manage episode 295783831 series 2921809
PyTorch, Edward Yang, and Team PyTorch에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 PyTorch, Edward Yang, and Team PyTorch 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
What's a domain library? Why do they exist? What do they do for you? What should you know about developing in PyTorch main library versus in a domain library? How coupled are they with PyTorch as a whole? What's cool about working on domain libraries?
Further reading.
- The classic trio of domain libraries is https://pytorch.org/audio/stable/index.html https://pytorch.org/text/stable/index.html and https://pytorch.org/vision/stable/index.html
Line notes.
- why do domain libraries exist? lots of domains specific gadgets,
inappropriate for PyTorch - what does a domain library do
- operator implementations (old days: pure python, not anymore)
- with autograd support and cuda acceleration
- esp encoding/decoding, e.g., for domain file formats
- torchbind for custom objects
- takes care of getting the dependencies for you
- esp transformations, e.g., for data augmentation
- models, esp pretrained weights
- datasets
- reference scripts
- full wheel/conda packaging like pytorch
- mobile compatibility
- operator implementations (old days: pure python, not anymore)
- separate repos: external contributors with direct access
- manual sync to fbcode; a lot easier to land code! less
motion so lower risk
- manual sync to fbcode; a lot easier to land code! less
- coupling with pytorch? CI typically runs on nightlies
- pytorch itself tests against torchvision, canary against
extensibility mechanisms - mostly not using internal tools (e.g., TensorIterator),
too unstable (this would be good to fix)
- pytorch itself tests against torchvision, canary against
- closer to research side of pytorch; francesco also part of papers
83 에피소드
모든 에피소드
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