[This document is a copy of the original. The latest version is available on Tijmen Tieleman’s homepage.]
Gnumpy is free software, but if you use it in scientific work that gets published, you should cite this tech report in your publication.
Documentation: here.
Do you want to have both the compute power of GPU’s and the programming convenience of Python numpy? Gnumpy + Cudamat will bring you that.
Gnumpy is a simple Python module that interfaces in a way almost identical to numpy, but does its computations on your computer’s GPU. See this example, training an RBM using Gnumpy.
Gnumpy runs on top of, and therefore requires, the excellent cudamat library, written by Vlad Mnih.
Gnumpy can run in simulation mode: everything happens on the CPU, but the interface is the same. This can be helpful if you like to write your programs on your GPU-less laptop before running them on a GPU-equipped machine. It also allows you to easily test what performance gain you get from using a GPU. The simulation mode requires npmat, written by Ilya Sutskever. [npmat is included in this distribution.]
Gnumpy is licensed with a BSD-style license (i.e. it’s completely free to use for everyone, also as a component in commercial software), with one added note: if you use it for scientific work that gets published, you must include reference to the Gnumpy tech report in your publication. For details of the license, see the top of gnumpy.py.
Recent changes:
2012-07-25: Bugfix. gnumpy.dot(x, x), when x is a 1-dimensional array, didn’t work but now works.
2011-06-06: gnumpy.dot() now takes arrays of ndim>2.
2011-04-19: Bugfix: several bugs involving zero size arrays were fixed.
2011-04-15: Bugfix. “x=gnumpy.zeros(10); x[array([])] = garray([])” didn’t work as it should. Now it does.
2011-03-24: Added gnumpy.outer().
2011-03-15: The ability to check for infs and nans automatically has been added to Gnumpy.
2010-07-19: Cudamat now enables fast indexing with arrays of indices. Download the newest Cudamat to have fast indexing with arrays in Gnumpy.
2010-07-08: Renamed the project to Gnumpy. It used to be called Gpunnumpy.
1 maintainer
Metadata
1 maintainer
Release files for gnumpy 0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| gnumpy-0.2.tar.gz | 29.0 kB | Without provenance | Details for gnumpy-0.2.tar.gz |
Release files / gnumpy-0.2.tar.gz
| Download URL | gnumpy-0.2.tar.gz |
|---|---|
| Size | 29.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
070b78ee648c42e3dcd2f56a4308cc26bcaa1de30a4f7c078eb5828050a4ec4f
|
|
BLAKE2b-256 checksum How to use checksums |
46632c7f2fd6763130700dff21aada32b2d6cf3373a625af71cc74f7090818f4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for gnumpy 0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
