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https://github.com/npatta01/pytorch-serving-workshop

Slides and notebook for the workshop on serving bert models in production
https://github.com/npatta01/pytorch-serving-workshop

deep-learning mlops pytorch torchserve

Last synced: about 2 months ago
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Slides and notebook for the workshop on serving bert models in production

Host: GitHub
URL: https://github.com/npatta01/pytorch-serving-workshop
Owner: npatta01
Created: 2021-09-20T13:12:25.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-11-12T10:32:13.000Z (about 2 years ago)
Last Synced: 2024-11-02T09:51:36.882Z (about 2 months ago)
Topics: deep-learning, mlops, pytorch, torchserve
Language: Jupyter Notebook
Homepage:
Size: 3.28 MB
Stars: 24
Watchers: 6
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # Readme

## Overview

This repo contains notebooks for Pytorch Serving Workshop.

Note: We **do not** need a GPU runtime

## Setup 

If you came to this repo, during a workshop visit this custom [jupyter hub](http://hub2.np.training) with all the dependencies already set up.

Otherwise, consider using [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/npatta01/pytorch-serving-workshop/main)

## Contents

There are five notebooks.

a. `00_prepare_dataset.ipynb`

Notebook that prepares the e-comeerce dataset and saves it.

b. `01_train.ipynb`

Trains a DistilBert model

c. `02_inference_review.ipynb`

Notebook that shows how to use the HuggingFace ecosystem. Also shows how to use the trained model from previous notebook.

d. `03_optimizing_model.ipynb`

Notebook that shows impact of Quantization and TorschScript

e. `04_packaging.ipynb`

Notebook that shows how to use TorchServe to serve models

## Slides

[![Watch the video](assets/slides_cover.png)](https://www.slideshare.net/nidhinpattaniyil/serving-bert-models-in-production-with-torchserve)

## Video

[![PyData Video](https://img.youtube.com/vi/sDGxzkOvxqY/0.jpg)](https://www.youtube.com/watch?v=sDGxzkOvxqY&ab_channel=PyData)

## References

[Pydata 2021 Slides](https://www.slideshare.net/nidhinpattaniyil/serving-bert-models-in-production-with-torchserve)

[Pydata 2021 Conference Page](https://pydata.org/global2021/schedule/presentation/136/serving-pytorch-models-in-production/)

## Libraries

This repro uses HuggingFace transformers and dataset pacakge. 

The dataset used is [Amazon Berkeley Objects (ABO) Dataset](https://amazon-berkeley-objects.s3.amazonaws.com/index.html) created by Amazon and UC Berkeley.

For more reference, refer to this [paper](https://arxiv.org/abs/2110.06199)

## Contact

For help or feedback, please reach out to :

- [Nidhin Pattaniyil](https://www.linkedin.com/in/nidhinpattaniyil/)   

- [Adway Dhillon](https://www.linkedin.com/in/adwaydhillon/)    

- [Vishal Rathi](https://www.linkedin.com/in/vishalkumarrathi/)