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https://github.com/fbsamples/pytorch-quantization-workshop

Code for a workshop hosted at the MLOps World Summit '22
https://github.com/fbsamples/pytorch-quantization-workshop

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Code for a workshop hosted at the MLOps World Summit '22

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# pytorch-quantization-workshop

This repo holds the files for the PyTorch Quantization Workshop conducted by [Suraj Subramanian](https://twitter.com/subramen) at the MLOpsWorld Conference on June 8 2022.

## Notebooks
#### [Quant_101.ipynb](Quant_101.ipynb)
Learn the fundamentals of quantization in pure Python code.

#### [Quant_API.ipynb](Quant_API.ipynb)
Learn about quantization schemes, when some are better than others, and using QConfigs in PyTorch

#### [Quant_Workflow.ipynb](Quant_Workflow.ipynb)
The number of available options can be overwhelming. Choosing the correct quantization technique and scheme is an empirical process; this notebook contains a workflow that aids choosing the most suitable option to quantize your FP32 model.

## Requirements
* An x86 or ARM CPU
* PyTorch 1.10.0+

## Further Reading
* [Quantization — PyTorch 1.11.0 documentation](https://pytorch.org/docs/stable/quantization.html)
* [Practical Quantization in PyTorch](https://pytorch.org/blog/quantization-in-practice/)
* [FX Graph Mode Quantization User Guide](https://pytorch.org/tutorials/prototype/fx_graph_mode_quant_guide.html)
* [PyTorch Forum - Quantization](https://discuss.pytorch.org/c/quantization/17)
* [PyTorch Github Issues](https://github.com/pytorch/pytorch/issues)

## Issues/Requests
If you encounter a bug, please open an issue or a PR. See [CONTRIBUTING.MD](CONTRIBUTING.MD)