Optimized implementation of numpy, leveraging Intel® Math Kernel Library to achieve highly efficient multi-threading, vectorization, and memory management.
It provides:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
- and much more
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
All NumPy wheels distributed on PyPI are BSD licensed.
Install instruction
Intel optimized NumPy Pypi packages are now distributed via Anaconda Cloud.
To install Intel optimized NumPy Pypi package please use following command:
python -m pip install -i https://pypi.anaconda.org/intel/simple numpy
2 maintainers
Release files for intel-numpy 1.21.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| intel_numpy-1.21.5-cp39-cp39-manylinux2014_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.17+ x86-64 | Details |
Release files / intel_numpy-1.21.5-cp39-cp39-manylinux2014_x86_64.whl
| Download URL | intel_numpy-1.21.5-cp39-cp39-manylinux2014_x86_64.whl |
|---|---|
| Size | 6.6 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
3b1c594055c0a2586c26833a839150e07bc1c949d362fde9c0f4a24437f89a89
|
|
BLAKE2b-256 checksum How to use checksums |
34be49d08086b9a1131c92552d0c4d283819818da7f9991bfeeca76963419a72
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.12
|
