Artwork

Kandungan disediakan oleh PyTorch, Edward Yang, and Team PyTorch. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh PyTorch, Edward Yang, and Team PyTorch atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
Player FM - Aplikasi Podcast
Pergi ke luar talian dengan aplikasi Player FM !

Anatomy of a domain library

16:11
 
Kongsi
 

Manage episode 295783831 series 2921809
Kandungan disediakan oleh PyTorch, Edward Yang, and Team PyTorch. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh PyTorch, Edward Yang, and Team PyTorch atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.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.

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
  • separate repos: external contributors with direct access
    • manual sync to fbcode; a lot easier to land code! less
      motion so lower risk
  • 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)
  • closer to research side of pytorch; francesco also part of papers
  continue reading

82 episod

Artwork

Anatomy of a domain library

PyTorch Developer Podcast

33 subscribers

published

iconKongsi
 
Manage episode 295783831 series 2921809
Kandungan disediakan oleh PyTorch, Edward Yang, and Team PyTorch. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh PyTorch, Edward Yang, and Team PyTorch atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.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.

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
  • separate repos: external contributors with direct access
    • manual sync to fbcode; a lot easier to land code! less
      motion so lower risk
  • 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)
  • closer to research side of pytorch; francesco also part of papers
  continue reading

82 episod

Semua episode

×
 
Loading …

Selamat datang ke Player FM

Player FM mengimbas laman-laman web bagi podcast berkualiti tinggi untuk anda nikmati sekarang. Ia merupakan aplikasi podcast terbaik dan berfungsi untuk Android, iPhone, dan web. Daftar untuk melaraskan langganan merentasi peranti.

 

Panduan Rujukan Pantas

Podcast Teratas