PyTorch Tutorial

This repository contains material to get started with PyTorch v1.3. It was the base for this [pytorch tutorial] from PyData Berlin 2018.

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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

• Training Libraries and Visualization
• 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

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Setup

Requirements

• conda
• Python 3.7 or higher

Install Dependencies

# If you have a GPU and CUDA 10
conda env create -f environment_gpu.yml
# If you don't have a GPU
conda env create -f environment_cpu.yml

# activate the conda environment
source activate pytorch_tutorial_123

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

Misc

To get the jupyter lab table of contents extensions do the following:

jupyter labextension install @jupyterlab/toc

Prior Versions

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