This repository contains notebooks for a PyTorch model-serving workshop.
You do not need a GPU runtime.
During the workshop, use this custom JupyterHub, which has all dependencies preinstalled.
Outside the workshop, use 
There are five notebooks.
a. 00_prepare_dataset.ipynb
Prepares and saves the e-commerce dataset.
b. 01_train.ipynb
Trains a DistilBERT model.
c. 02_inference_review.ipynb
Introduces the Hugging Face ecosystem and shows how to use the trained model from the previous notebook.
d. 03_optimizing_model.ipynb
Demonstrates the impact of quantization and TorchScript.
e. 04_packaging.ipynb
Shows how to package and serve models with TorchServe.
This repository uses the Hugging Face Transformers and Datasets packages.
The dataset used is Amazon Berkeley Objects (ABO) Dataset created by Amazon and UC Berkeley. For more information, see the accompanying paper.
For help or feedback, please reach out to: