Please see Developer Environment Setup

For fb employees, please read through this as well.

Run through the PyTorch Basics tutorial

If you're not familiar with machine learning, here is a nice lecture series.

• Watch a high-level overview of PyTorch by Joe Spisak
• Take a look at PyTorch tutorials
• Take a look at PyTorch examples

Explore popular open-source models and frameworks using PyTorch

• Detectron and Mask R-CNN, Computer Vision
• PyText, NLP
• Horizon, Applied Reinforcement Learning

If you don't need CUDA, build using USE_CUDA=0: the build is significantly faster. There are also a lot of other build flags that help get rid of components that you might not work on. Below is an opinionated build command that gets rid of a lot of different options that don't get used very often.

USE_KINETO=0 BUILD_CAFFE2=0 USE_DISTRIBUTED=0 USE_NCCL=0 BUILD_TEST=0 USE_XNNPACK=0 USE_FBGEMM=0 USE_QNNPACK=0 USE_MKLDNN=0 USE_MIOPEN=0 USE_NNPACK=0 BUILD_CAFFE2_OPS=0 USE_TENSORPIPE=0 python setup.py develop

(See this for more details)

The head of the pytorch/pytorch master branch may have test failures (see here for the current state). When developing PyTorch, instead of branching off of master, you can branch off of viable/strict. viable/strict is a branch that lags behind master and guarantees that all PyTorch tests are passing on the branch. Basing your work off of viable/strict gives you confidence that any test failures are actually your code's fault.

Some quick git tips on maintaining feature branches:

# These tips assume upstream points to pytorch/pytorch. 
# (Some people develop with origin pointing to pytorch/pytorch.)

# Create a new branch from upstream/viable/strict
git fetch upstream viable/strict
git checkout -b my_new_feature upstream/viable/strict
git push -u origin my_new_feature

# Rebase work on a branch to appear on top of viable/strict
git pull --rebase upstream viable/strict

# Delete a branch
git checkout main
git branch -D my_new_feature
git push --delete origin my_new_feature

See this detailed section in our CONTRIBUTING.MD

Please see https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md and the GitHub First user guide.

For fb employees, feel free to check out instructions for our old workflow of landing internally.

Unit 2: Tensors, Operators, and Testing - Tensor and Operator Basics