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.
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.
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 |
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.
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