PyTorch Tutorial

This repository contains material to get started with PyTorch v1.7. It was the base for this [pytorch tutorial] from PyData Berlin 2018.

Table of Contents

PART 0 - Foreword

• Foreword - Why PyTorch and why not? Why this talk?

PART 1 - Basics

• PyTorch basics - tensors, GPU, autograd - open in colab
• Debugging - open in colab
• Example: linear regression - open in colab
• Storing and loading models - open in colab
• Working with data - Dataset, DataLoader, Sampler, transforms - open in colab

PART 2 - Computer Vision

• Transfer Learning - open in colab

PART 3 - Misc, Cool Applications, Tips, Advanced

• Torch JIT - open in colab
• Hooks - register functions to be called during the forward and backward pass - open in colab
• Machine Learning 101 with numpy and PyTorch - open in colab
• PyTorch + GPU in Google's Colab
• Teacher Forcing
• RNNs from Scratch - open in colab
• Mean Shift Clustering - open in colab

PART -2 - WIP and TODO

• TODO nn and nn.Module
• TODO Deployment
• TODO Deployment with TF Serving
• TODO nn.init
• TODO PyTorch C++ frontend

PART -1 - The End

• The End

Setup

Requirements

• Python 3.8

Install Dependencies

python3.8 -m venv .venv
source .venv/bin/activate.fish
pip install -r requirements.txt

Optional

Run the following to enable the jupyter table of contents plugin:

jupyter labextension install @jupyterlab/toc

jupyter nbextension enable --py widgetsnbextension

Download data and models

Download data and models for the tutorial:

python download_data.py

Then you should be ready to go. Start jupyter lab:

jupyter lab

Prior Versions

• Version of this tutorial for the PyData 2018 conference: [material] [video]