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CONTRIBUTING.md

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Contributing

Setup

Rust, a C compiler, and CMake are required to build uv.

Linux

On Ubuntu and other Debian-based distributions, you can install the C compiler and CMake with

sudo apt install build-essential cmake

macOS

CMake may be installed with Homebrew:

brew install cmake

The Python bootstrapping script requires coreutils and zstd; we recommend installing them with Homebrew:

brew install coreutils zstd

See the Python section for instructions on installing the Python versions.

Windows

You can install CMake from the installers or with pipx install cmake (make sure that the pipx install path is in PATH, pipx complains if it isn't).

Testing

For running tests, we recommend nextest.

Python

Testing uv requires multiple specific Python versions. You can install them into <project root>/bin via our bootstrapping script:

pipx run scripts/bootstrap/install.py

Alternatively, you can install zstandard from PyPI, then run:

python3.12 scripts/bootstrap/install.py

Local testing

You can invoke your development version of uv with cargo run -- <args>. For example:

cargo run -- venv
cargo run -- pip install requests

Running inside a docker container

Source distributions can run arbitrary code on build and can make unwanted modifications to your system ("Someone's Been Messing With My Subnormals!" on Blogspot, "nvidia-pyindex" on PyPI), which can even occur when just resolving requirements. To prevent this, there's a Docker container you can run commands in:

docker buildx build -t uv-builder -f builder.dockerfile --load .
# Build for musl to avoid glibc errors, might not be required with your OS version
cargo build --target x86_64-unknown-linux-musl --profile profiling --features vendored-openssl
docker run --rm -it -v $(pwd):/app uv-builder /app/target/x86_64-unknown-linux-musl/profiling/uv-dev resolve-many --cache-dir /app/cache-docker /app/scripts/popular_packages/pypi_10k_most_dependents.txt

We recommend using this container if you don't trust the dependency tree of the package(s) you are trying to resolve or install.

Profiling

Please refer to Ruff's Profiling Guide, it applies to uv, too.

Analysing concurrency

You can use tracing-durations-export to visualize parallel requests and find any spots where uv is CPU-bound. Example usage, with uv and uv-dev respectively:

RUST_LOG=uv=info TRACING_DURATIONS_FILE=target/traces/jupyter.ndjson cargo run --features tracing-durations-export --profile profiling -- pip compile scripts/requirements/jupyter.in
RUST_LOG=uv=info TRACING_DURATIONS_FILE=target/traces/jupyter.ndjson cargo run --features tracing-durations-export --bin uv-dev --profile profiling -- resolve jupyter

Trace-level logging

You can enable trace level logging using the RUST_LOG environment variable, i.e.

RUST_LOG=trace uv …