All benchmarks were computed on macOS using Python 3.12.0 (for non-uv tools), and come with a few important caveats:
- Benchmark performance may vary dramatically across different operating systems and filesystems. In particular, uv uses different installation strategies based on the underlying filesystem's capabilities. (For example, uv uses reflinking on macOS, and hardlinking on Linux.)
- Benchmark performance may vary dramatically depending on the set of packages being installed. For example, a resolution that requires building a single intensive source distribution may appear very similar across tools, since the bottleneck is tool-agnostic.
- Unlike Poetry, both uv and pip-tools do not generate multi-platform lockfiles. As such, Poetry is (by design) doing significantly more work than other tools in the resolution benchmarks. Poetry is included for completeness, as many projects may not need a multi-platform lockfile. However, it's critical to understand that benchmarking uv's resolution time against Poetry is an unfair comparison. (Benchmarking installation, however, is a fair comparison.)
This document benchmarks against Trio's docs-requirements.in
, as a representative example of a
real-world project.
In each case, a smaller bar (i.e., lower) is better.
Benchmarking package installation (e.g., uv pip sync
) with a warm cache. This is equivalent
to removing and recreating a virtual environment, and then populating it with dependencies that
you've installed previously on the same machine.
Benchmarking package installation (e.g., uv pip sync
) with a cold cache. This is equivalent
to running uv pip sync
on a new machine or in CI (assuming that the package manager cache is
not shared across runs).
Benchmarking dependency resolution (e.g., uv pip compile
) with a warm cache, but no existing
lockfile. This is equivalent to blowing away an existing requirements.txt
file to regenerate it
from a requirements.in
file.
Benchmarking dependency resolution (e.g., uv pip compile
) with a cold cache. This is
equivalent to running uv pip compile
on a new machine or in CI (assuming that the package
manager cache is not shared across runs).
All benchmarks were generated using the scripts/bench/__main__.py
script, which wraps
hyperfine
to facilitate benchmarking uv
against a variety of other tools.
The benchmark script itself has a several requirements:
- A local uv release build (
cargo build --release
). - A virtual environment with the script's own dependencies installed (
uv venv && uv pip sync scripts/bench/requirements.txt
). - The
hyperfine
command-line tool installed on your system.
To benchmark resolution against pip-compile, Poetry, and PDM:
python -m scripts.bench \
--uv \
--poetry \
--pdm \
--pip-compile \
--benchmark resolve-warm --benchmark resolve-cold \
scripts/requirements/trio.in \
--json
To benchmark installation against pip-sync, Poetry, and PDM:
python -m scripts.bench \
--uv \
--poetry \
--pdm \
--pip-sync \
--benchmark install-warm --benchmark install-cold \
--json
After running the benchmark script, you can generate the corresponding graph via:
cargo run -p uv-dev render-benchmarks resolve-warm.json --title "Warm Resolution"
cargo run -p uv-dev render-benchmarks resolve-cold.json --title "Cold Resolution"
cargo run -p uv-dev render-benchmarks install-warm.json --title "Warm Installation"
cargo run -p uv-dev render-benchmarks install-cold.json --title "Cold Installation"
You need to install the Roboto Font if the labels are missing in the generated graph.
The inclusion of this BENCHMARKS.md
file was inspired by the excellent benchmarking documentation
in Orogene.
If you're seeing high variance when running the cold benchmarks, then it's likely that you're running into throttling or DDoS prevention from your ISP. In that case, ISPs forcefully terminate TCP connections with a TCP reset. We believe this is due to the benchmarks making the exact same requests in a very short time (especially true for uv
). A possible workaround is to connect to VPN to bypass your ISPs filtering mechanism.