NumPy is the fundamental package for array computing with Python.
Project description
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 --extra-index-url https://pypi.org/simple numpy
If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Anaconda Cloud:
python -m pip install -i https://pypi.anaconda.org/intel/simple --extra-index-url https://pypi.org/simple numpy==<numpy_version>
Where <numpy_version>
should be the latest version from https://anaconda.org/intel/numpy
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for intel_numpy-1.21.4-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a658594515a50eaa342a821adebae30392996d62b3814f9e977b547d4c951ed7 |
|
MD5 | 1e2b6ad2cc227f3589727105dd66a653 |
|
BLAKE2b-256 | 49950ef74166acd55e30fb1d152bdd8034a2cf749398ae358fb790e9f7548d78 |
Hashes for intel_numpy-1.21.4-cp39-cp39-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 30457fd17574eec1e0c5fb9fe3eb9d340a2025727b1c0117d88e22b0e4b1d3a8 |
|
MD5 | 0a1bcebe8d3a995b70f3942aa51d0dee |
|
BLAKE2b-256 | 4dd5b1aa347a6b943713b0faa66e78b7fbb245dc5bbe9eefb70a90bd6319666e |
Hashes for intel_numpy-1.21.4-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1b7160c2fae8460bbfc21d2776a7f3b0cf7c346af95a6fb51b4db5ee74bc2362 |
|
MD5 | aa40f00ee19aa6dbaadd70049f63800d |
|
BLAKE2b-256 | 6ada00adefc753c293287479af265b3f2cb6f4929b8bc0546c25be82e0fe6d4f |
Hashes for intel_numpy-1.21.4-cp38-cp38-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f636bd396bc51673958052684bc1e6591439bd87f26e1eb6c74b10d71fb04440 |
|
MD5 | c27070658ba76207436ec26f610de85f |
|
BLAKE2b-256 | ab2b1862c59d265fea89ec777db320fafbcc69c453f9116a5883d6e1320f6bb6 |
Hashes for intel_numpy-1.21.4-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5ba87fb7eee5414de5ab8d5b58591bd14c9f037b1ba3eb7eee3a03a9593e29c9 |
|
MD5 | caa07808f19a0de4cf303e0c7b7ac488 |
|
BLAKE2b-256 | 9f1b3a973b71cd2888b3054fa2bd88d398cc21760295cc0430657057eeb4b842 |
Hashes for intel_numpy-1.21.4-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3ebbde35c16a6cdb7112264fb43fbcff9825c607120e5a6b53118b4aa523e6b2 |
|
MD5 | 5fb6281fc34358808dc48bf058dd04f3 |
|
BLAKE2b-256 | 668655b23cb6721da85a9394000c7f078d38990ba5d706c3f8ac3d2be07ca017 |