s5cmd 0.3.3

This project provides the infrastructure to build s5cmd Python wheels.

s5cmd Python Distributions

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. 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.

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