PyTorch Tutorial
This repository contains material to get started with PyTorch v1.0.
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
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
andnn.Module
- TODO Deployment
- TODO Deployment with TF Serving
- TODO
nn.init
- TODO PyTorch C++ frontend
PART -1 - The End
Setup
Requirements
- Python 3.6 or higher
- conda
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]