https://github.com/jason-dai/cvpr2022

https://github.com/jason-dai/cvpr2022

Last synced: 26 days ago
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• Host: GitHub
• URL: https://github.com/jason-dai/cvpr2022
• Owner: jason-dai
• License: apache-2.0
• Created: 2022-04-19T09:33:26.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-07T14:45:21.000Z (over 2 years ago)
• Last Synced: 2023-03-03T19:59:20.196Z (over 1 year ago)
• Language: HTML
• Size: 36.9 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## CVPR 2022 Demo Track

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### Paper

arXiv [link](https://arxiv.org/abs/2204.01715)

### Abstract

Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually entail many manual and error-prone

steps for the data scientists to fully take advantage of the available hardware resources (e.g., SIMD instructions, multi-processing, quantization, memory allocation optimization, data partitioning, distributed computing, etc.). To address this challenge, we have open sourced [BigDL

2.0](https://github.com/intel-analytics/BigDL/) under Apache 2.0 license (combining the original

BigDL and Analytics Zoo projects); using BigDL 2.0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9.6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases). BigDL 2.0 has already been adopted by many real-world users (such as Mastercard, Burger King, Inspur, etc.) in production.

## Demos

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- **Online demo** at [Hugging Face Spaces](https://huggingface.co/spaces/BigDL/bigdl_nano_demo) 

- [Notebooks](https://github.com/jason-dai/cvpr2022/tree/main/demos/FastFace) demostrating **accelerations for PyTorch Lightning** using BigDL-Nano

- *Yout may also refer to a collection of [BigDL notebooks](https://bigdl.readthedocs.io/en/latest/doc/UserGuide/notebooks.html) that can be directly opened and run in Colab*