# Contributing

We have issues labeled as [Good First Issue](https://github.com/astral-sh/uv/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) and [Help Wanted](https://github.com/astral-sh/uv/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) which are good opportunities for new contributors.

## Setup

[Rust](https://rustup.rs/), 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:

```shell
sudo apt install build-essential cmake
```

### macOS

You can install CMake with Homebrew:

```shell
brew install cmake
```

See the [Python](#python) section for instructions on installing the Python versions.

### Windows

You can install CMake from the [installers](https://cmake.org/download/) or with `pipx install cmake`.

## Testing

For running tests, we recommend [nextest](https://nexte.st/).

If tests fail due to a mismatch in the JSON Schema, run: `cargo dev generate-json-schema`.

### Python

Testing uv requires multiple specific Python versions; they can be installed with:

```shell
cargo dev fetch-python
```

The storage directory can be configured with `UV_TOOLCHAIN_DIR`.

### Local testing

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

```shell
cargo run -- venv
cargo run -- pip install requests
```

### Testing on Windows

When testing debug builds on Windows, the stack can overflow resulting in a `STATUS_STACK_OVERFLOW` error code.
This is due to a small stack size limit on Windows that we encounter when running unoptimized builds — the release
builds do not have this problem. We [added a `UV_STACK_SIZE` variable](https://github.com/astral-sh/uv/pull/941) to
bypass this problem during testing. We recommend bumping the stack size from the default of 1MB to 2MB, for example:

```powershell
$Env:UV_STACK_SIZE = '2000000'
```

## 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](https://moyix.blogspot.com/2022/09/someones-been-messing-with-my-subnormals.html), ["nvidia-pyindex" on PyPI](https://pypi.org/project/nvidia-pyindex/)), which can even occur when just resolving requirements. To prevent this, there's a Docker container you can run commands in:

```bash
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
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 and Benchmarking

Please refer to Ruff's [Profiling Guide](https://github.com/astral-sh/ruff/blob/main/CONTRIBUTING.md#profiling-projects), it applies to uv, too.

We provide diverse sets of requirements for testing and benchmarking the resolver in `scripts/requirements` and for the installer in `scripts/requirements/compiled`.

You can use `scripts/bench` to benchmark predefined workloads between uv versions and with other tools, e.g.

```
python -m scripts.bench \
    --uv-path ./target/release/before \
    --uv-path ./target/release/after \
    ./scripts/requirements/jupyter.in --benchmark resolve-cold --min-runs 20
```

### Analyzing concurrency

You can use [tracing-durations-export](https://github.com/konstin/tracing-durations-export) to visualize parallel requests and find any spots where uv is CPU-bound. Example usage, with `uv` and `uv-dev` respectively:

```shell
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
```

```shell
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.

```shell
RUST_LOG=trace uv
```

## Releases

Releases can only be performed by Astral team members.

Changelog entries and version bumps are automated. First, run:

```
./scripts/release.sh
```

Then, editorialize the `CHANGELOG.md` file to ensure entries are consistently styled.

Then, open a pull request e.g. `Bump version to ...`.

Binary builds will automatically be tested for the release.

After merging the pull request, run the [release workflow](https://github.com/astral-sh/uv/actions/workflows/release.yml)
with the version tag. **Do not include a leading `v`**.
The release will automatically be created on GitHub after everything else publishes.
