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 platform-independent 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 platform-independent 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/benchmark
package, 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
). - An installation of the production
uv
binary in your path. - The
hyperfine
command-line tool installed on your system.
To benchmark resolution against pip-compile, Poetry, and PDM:
uv run resolver \
--uv-pip \
--poetry \
--pdm \
--pip-compile \
--benchmark resolve-warm --benchmark resolve-cold \
--json \
../requirements/trio.in
To benchmark installation against pip-sync, Poetry, and PDM:
uv run resolver \
--uv-pip \
--poetry \
--pdm \
--pip-sync \
--benchmark install-warm --benchmark install-cold \
--json \
../requirements/compiled/trio.txt
Both commands should be run from the scripts/benchmark
directory.
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.