mirror of
https://github.com/astral-sh/uv.git
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2a50bc94ca
## Summary I noticed the command to install multiple Python versions was wrong as it was failing cause `toolchain` is not a known command. I looked in the `ci.yml` workflow to see which command is used there and updated the instructions accordingly. ## Test Plan I just ran the command locally. :)
133 lines
4.9 KiB
Markdown
133 lines
4.9 KiB
Markdown
# Contributing
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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.
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## Setup
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[Rust](https://rustup.rs/), a C compiler, and CMake are required to build uv.
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### Linux
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On Ubuntu and other Debian-based distributions, you can install the C compiler and CMake with:
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```shell
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sudo apt install build-essential cmake
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```
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### macOS
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You can install CMake with Homebrew:
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```shell
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brew install cmake
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```
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See the [Python](#python) section for instructions on installing the Python versions.
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### Windows
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You can install CMake from the [installers](https://cmake.org/download/) or with `pipx install cmake`.
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## Testing
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For running tests, we recommend [nextest](https://nexte.st/).
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If tests fail due to a mismatch in the JSON Schema, run: `cargo dev generate-json-schema`.
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### Python
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Testing uv requires multiple specific Python versions; they can be installed with:
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```shell
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cargo run python install
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```
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The storage directory can be configured with `UV_PYTHON_INSTALL_DIR`.
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### Local testing
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You can invoke your development version of uv with `cargo run -- <args>`. For example:
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```shell
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cargo run -- venv
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cargo run -- pip install requests
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```
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### Testing on Windows
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When testing debug builds on Windows, the stack can overflow resulting in a `STATUS_STACK_OVERFLOW` error code.
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This is due to a small stack size limit on Windows that we encounter when running unoptimized builds — the release
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builds do not have this problem. We [added a `UV_STACK_SIZE` variable](https://github.com/astral-sh/uv/pull/941) to
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bypass this problem during testing. We recommend bumping the stack size from the default of 1MB to 2MB, for example:
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```powershell
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$Env:UV_STACK_SIZE = '2000000'
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```
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## Running inside a Docker container
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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:
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```bash
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docker buildx build -t uv-builder -f builder.dockerfile --load .
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# Build for musl to avoid glibc errors, might not be required with your OS version
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cargo build --target x86_64-unknown-linux-musl --profile profiling
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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
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```
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We recommend using this container if you don't trust the dependency tree of the package(s) you are trying to resolve or install.
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## Profiling and Benchmarking
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Please refer to Ruff's [Profiling Guide](https://github.com/astral-sh/ruff/blob/main/CONTRIBUTING.md#profiling-projects), it applies to uv, too.
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We provide diverse sets of requirements for testing and benchmarking the resolver in `scripts/requirements` and for the installer in `scripts/requirements/compiled`.
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You can use `scripts/bench` to benchmark predefined workloads between uv versions and with other tools, e.g.
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```
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python -m scripts.bench \
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--uv-path ./target/release/before \
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--uv-path ./target/release/after \
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./scripts/requirements/jupyter.in --benchmark resolve-cold --min-runs 20
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```
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### Analyzing concurrency
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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:
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```shell
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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
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```
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```shell
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RUST_LOG=uv=info TRACING_DURATIONS_FILE=target/traces/jupyter.ndjson cargo run --features tracing-durations-export --bin uv-dev --profile profiling -- resolve jupyter
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```
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### Trace-level logging
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You can enable `trace` level logging using the `RUST_LOG` environment variable, i.e.
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```shell
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RUST_LOG=trace uv
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```
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## Releases
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Releases can only be performed by Astral team members.
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Changelog entries and version bumps are automated. First, run:
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```
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./scripts/release.sh
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```
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Then, editorialize the `CHANGELOG.md` file to ensure entries are consistently styled.
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Then, open a pull request e.g. `Bump version to ...`.
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Binary builds will automatically be tested for the release.
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After merging the pull request, run the [release workflow](https://github.com/astral-sh/uv/actions/workflows/release.yml)
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with the version tag. **Do not include a leading `v`**.
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The release will automatically be created on GitHub after everything else publishes.
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