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Development

Installation

Clone the repository and submodules with

git clone --recurse-submodules URL

For CUDA development, it is recommend to install with BUILD_NO_CUDA=1, which will disable compiling during pip install, and instead use JIT compiling on your first run. The benefit of JIT compiling is that it does incremental compiling as you modify your cuda code so it is much faster than re-compile through pip. Note the JIT compiled library can be found under ~/.cache/torch_extensions/py*-cu*/.

BUILD_NO_CUDA=1 pip install -e .[dev]

If you won't touch the underlying CUDA code, you can just install with compiling:

pip install -e .[dev]

Protect Main Branch over Pull Request

It is recommended to commit the code into the main branch as a PR over a hard push, as the PR would protect the main branch if the code break tests but a hard push won't. Also squash the commits before merging the PR so it won't span the git history.

The current tests that will be triggered by PR:

  • .github/workflows/core_tests.yml: Black formating. Pytests.
  • .github/workflows/doc.yml: Doc build.

Because we check for black formatting, it is recommend to run black before commit in the code:

black . gsplat/ tests/ examples/ profiling/

Since there is no GPU supported on github workflow container, we don't test against those cuda unit tests under tests/ in PR. So it is recommended to check test pass locally before committing:

pytest tests/  # check for all tests
pytest tests/test_basic.py  # check for a single test file.

Note that pytest recognizes and runs all functions named as test_*, so you should name the test functions in this pattern. See test_basic.py as an example.

Build the Doc Locally

If you want to contribute to the doc, here is the way to build it locally. The source code of the doc can be found in docs/source and the built doc will be in _build/. If you are interested in contributing with the doc, here are some examples on documentation: viser, nerfstudio, nerfacc.

pip install -e .[dev]
pip install -r docs/requirements.txt
sphinx-build docs/source _build

Clangd setup (for Neovim)

clangd is a nice tool for providing completions, type checking, and other helpful features in C++. It requires some extra effort to get set up for CUDA development, but there are fortunately only three steps here.

First, we should install a clangd extension for our IDE/editor.

For Neovim+lspconfig users, this is very easy, we can simply install clangd via Mason and add a few setup lines in Lua:

require("lspconfig").clangd.setup{
    capabilities = capabilities
}

Second, we need to generate a .clangd configuration file with the current CUDA path argument.

Make sure you're in the right environment (with CUDA installed), and then from the root of the repository, you can run:

echo "# Autogenerated, see .clangd_template\!" > .clangd && sed -e "/^#/d" -e "s|YOUR_CUDA_PATH|$(dirname $(dirname $(which nvcc)))|" .clangd_template >> .clangd

Third, we'll need a compile_comands.json file.

If we're working on PyTorch bindings, one option is to generate this using bear:

sudo apt update
sudo apt install bear

# From the repository root, 3dgs-exercise/.
#
# This will save a file at 3dgs-exercise/compile_commands.json, which clangd
# should be able to detect.
bear -- pip install -e gsplat/

# Make sure the file is not empty!
cat compile_commands.json

Alternatively: if we're working directly in C (and don't need any PyTorch binding stuff), we can generate via CMake:

# From 3dgs-exercise/csrc/build.
#
# This will save a file at 3dgs-exercise/csrc/build/compile_commands.json, which
# clangd should be able to detect.
cmake .. -DCMAKE_EXPORT_COMPILE_COMMANDS=on

# Make sure the file is not empty!
cat compile_commands.json