Bump actions/checkout from 4.3.1 to 6.0.1 - #5
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Bumps [actions/checkout](https://github.com/actions/checkout) from 4.3.1 to 6.0.1. - [Release notes](https://github.com/actions/checkout/releases) - [Commits](actions/checkout@v4.3.1...v6.0.1) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: 6.0.1 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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Aug 14, 2026
## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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Aug 14, 2026
## Summary This changes the default for `prune-cache` from `true` to `false`, motivated by [#745](astral-sh/setup-uv#745). Users that want the existing behavior can continue to set `prune-cache: true` explicitly. Some history: I originally added [`uv cache prune --ci`](astral-sh/uv#5391) after looking at a workload where the uv cache was ~2.2 GB, almost entirely due to the enormous pre-built `torch` and `nvidia_cudnn_cu12` wheels ([original analysis](actions/setup-python#822 (comment))). Persisting and restoring thousands of extracted files through the GitHub Actions cache could be slower than downloading the wheels again. In contrast, wheels built from source can be very expensive to recreate. The intent was to remove pre-built wheels while retaining locally-built wheels. `setup-uv` subsequently made pruning configurable, but defaulted `prune-cache` to `true`; it also later enabled caching by default on GitHub-hosted runners. As a result, the default configuration repeatedly downloads pre-built wheels from PyPI even on a cache hit. That tradeoff has become more important as uv adoption has grown: [the PyPI analysis in #745](astral-sh/setup-uv#745 (comment)) estimates that uv accounts for roughly half of reported CI downloads from PyPI, and roughly 65-75% for `boto3`. I ran the comparison across a few different workloads: | Workload | PR | Packages | Cache: keep / prune / prune-ci | Warm restore+sync: keep / prune / prune-ci | Downloads: prune / prune-ci | |---|---:|---:|---:|---:|---:| | Tiny | [#1](https://github.com/astral-sh/setup-uv-benchmarks/pull/1) | 19 | 6 / 6 / 2 MB | 0.3-0.4 / 0.3 / 0.4-0.5 s | 0 / 2 | | Web | [#2](https://github.com/astral-sh/setup-uv-benchmarks/pull/2) | 65 | 43 / 43 / 7 MB | 0.6-1.0 / 0.5-0.6 / 1.5-1.7 s | 0 / 6 | | Scientific | [#3](https://github.com/astral-sh/setup-uv-benchmarks/pull/3) | 118 | 586 / 586 / 8 MB | 8.2-16.0 / 7.0-8.2 / 8.9-12.1 s | 0 / 19 | | PySpark | [#4](https://github.com/astral-sh/setup-uv-benchmarks/pull/4) | 19 | 1820 / 1820 / 436 MB | 9.9-21.1 / 10.5-11.0 / 5.0-7.0 s | 0 / 4 | | CPU PyTorch | [#5](https://github.com/astral-sh/setup-uv-benchmarks/pull/5) | 14 | 182 / 182 / 1 MB | 3.0-6.0 / 3.6-4.0 / 5.7-6.4 s | 0 / 6 | | CPU-PyTorch ML | [#6](https://github.com/astral-sh/setup-uv-benchmarks/pull/6) | 137 | 346 / 346 / 10 MB | 7.4-18.0 / 8.8-8.9 / 9.7-11.9 s | 0 / 20 | | CUDA PyTorch | [#7](https://github.com/astral-sh/setup-uv-benchmarks/pull/7) | 201 | 2316 / 2315 / 16 MB | 30.2-67.9 / 31.0-63.6 / 33.3-36.7 s | 0 / 40 | The CUDA workload intentionally reproduces the original `torch==2.1.1` example. Keeping wheels again produces a ~2.3 GB Actions cache. Across nine warm runs, restoring that cache ranged from slightly faster than re-downloading to roughly twice as slow; pruning consistently re-downloaded 40 distributions in ~33-37 seconds ([original runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29750292738), [additional runs](https://github.com/astral-sh/setup-uv-benchmarks/actions/runs/29761705492)). I also tried running `uv cache prune --force` without `--ci` across every workload, to see if it provided a useful middle ground. It did not meaningfully reduce any of the caches: plain prune took 11-21 ms and left the extracted cache and file count unchanged, including PySpark. On these fresh caches, there are no dangling entries to remove; without `--ci`, the pre-built wheels and unpacked source/build artifacts are retained. The per-workload runs are linked in the table above. So the original motivation still holds for very large CUDA or source-heavy workloads, but it is not representative of the common case. For smaller workloads, keeping pre-built wheels is generally faster and avoids repeated PyPI traffic. This changes the default accordingly, while retaining `prune-cache: true` as an opt-in for workloads where the smaller cache is worthwhile. Closes astral-sh/setup-uv#745.
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Bumps actions/checkout from 4.3.1 to 6.0.1.
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8e8c483Clarify v6 README (#2328)033fa0dAdd worktree support for persist-credentials includeIf (#2327)c2d88d3Update all references from v5 and v4 to v6 (#2314)1af3b93update readme/changelog for v6 (#2311)71cf226v6-beta (#2298)069c695Persist creds to a separate file (#2286)ff7abcdUpdate README to include Node.js 24 support details and requirements (#2248)08c6903Prepare v5.0.0 release (#2238)9f26565Update actions checkout to use node 24 (#2226)You can trigger a rebase of this PR by commenting
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