https://github.com/imagingdatacommons/s5cmd-python-distributions
This project provides the infrastructure to build s5cmd Python wheels.
https://github.com/imagingdatacommons/s5cmd-python-distributions
Last synced: 9 months ago
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This project provides the infrastructure to build s5cmd Python wheels.
- Host: GitHub
- URL: https://github.com/imagingdatacommons/s5cmd-python-distributions
- Owner: ImagingDataCommons
- License: mit
- Created: 2024-02-14T23:40:40.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-02T13:26:24.000Z (9 months ago)
- Last Synced: 2025-05-02T14:44:21.694Z (9 months ago)
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# s5cmd Python Distributions
[![Actions Status][actions-badge]][actions-link]
[![PyPI version][pypi-version]][pypi-link]
[![PyPI platforms][pypi-platforms]][pypi-link]
## Overview
`s5cmd` is a very fast S3 and local filesystem execution tool. It comes with
support for a multitude of operations including tab completion and wildcard
support for files, which can be very handy for your object storage workflow
while working with large number of files.
This project provides the infrastructure for building the `s5cmd` Python wheels.
For more information about `s5cmd`, please refer to
https://github.com/peak/s5cmd.
The Python wheels provided here contain the official `s5cmd` executable, which
is sourced from the [GitHub releases](https://github.com/peak/s5cmd/releases).
Once the wheel is installed, a convenient launcher executable is automatically
placed in the PATH. This launcher is created during installation by pip,
leveraging the `[project.scripts]` configuration defined in the `pyproject.toml`
file.
## Platforms
The following platforms are supported by the binary wheels:
| OS | Arch |
| ------------- | ---------------------------------------------------------------------------- |
| Windows | 64-bit
32-bit
ARM64 |
| Linux Intel | manylinux 64-bit
musllinux 64-bit
manylinux 32-bit
musllinux 32-bit |
| Linux ARM | manylinux AArch64
musllinux AArch64 |
| Linux PowerPC | manylinux ppc64le
musllinux ppc64le |
| macOS 10.10+ | Intel |
| macOS 11+ | Apple Silicon |
## License
This project is maintained by Jean-Christophe Fillion-Robin from Kitware Inc. It
is covered by the OSI-approved MIT License.
`s5cmd` is distributed under the OSI-approved MIT License. For further details
regarding `s5cmd`, please visit https://github.com/peak/s5cmd.
[actions-badge]: https://github.com/jcfr/s5cmd-python-distributions/workflows/CI/badge.svg
[actions-link]: https://github.com/jcfr/s5cmd-python-distributions/actions
[pypi-link]: https://pypi.org/project/s5cmd/
[pypi-platforms]: https://img.shields.io/pypi/pyversions/s5cmd
[pypi-version]: https://img.shields.io/pypi/v/s5cmd
## Acknowledgments
This software is supported by the Imaging Data Commons (IDC) project, which has
been funded in whole or in part with Federal funds from the NCI, NIH, under task
order no. HHSN26110071 under contract no. HHSN261201500003l.
NCI Imaging Data Commons (IDC) (https://imaging.datacommons.cancer.gov/) is a
cloud-based environment containing publicly available cancer imaging data
co-located with analysis and exploration tools and resources. IDC is a node
within the broader NCI Cancer Research Data Commons (CRDC) infrastructure that
provides secure access to a large, comprehensive, and expanding collection of
cancer research data.
Learn more about IDC from this publication:
> Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D.,
> Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H.
> J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P.,
> Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R.
> _National Cancer Institute Imaging Data Commons: Toward Transparency,
> Reproducibility, and Scalability in Imaging Artificial Intelligence_.
> RadioGraphics (2023). https://doi.org/10.1148/rg.230180