CuPy: NumPy-like API accelerated with CUDA
Project description
CuPy : NumPy-like API accelerated with CUDA
CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA.
This is a CuPy wheel (precompiled binary) package for CUDA 8.0. You need to install CUDA Toolkit 8.0 to use these packages.
If you have another version of CUDA, please see Installation Guide for instructions. If you want to build CuPy from source distribution, use pip install cupy instead.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Filename, size | File type | Python version | Upload date | Hashes |
---|---|---|---|---|
Filename, size cupy_cuda80-7.8.0-cp35-cp35m-manylinux1_x86_64.whl (210.0 MB) | File type Wheel | Python version cp35 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp36-cp36m-manylinux1_x86_64.whl (210.3 MB) | File type Wheel | Python version cp36 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp36-cp36m-win_amd64.whl (151.7 MB) | File type Wheel | Python version cp36 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp37-cp37m-manylinux1_x86_64.whl (210.3 MB) | File type Wheel | Python version cp37 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp37-cp37m-win_amd64.whl (151.7 MB) | File type Wheel | Python version cp37 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp38-cp38-manylinux1_x86_64.whl (210.4 MB) | File type Wheel | Python version cp38 | Upload date | Hashes View |
Filename, size cupy_cuda80-7.8.0-cp38-cp38-win_amd64.whl (151.7 MB) | File type Wheel | Python version cp38 | Upload date | Hashes View |
Close
Hashes for cupy_cuda80-7.8.0-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4efdbd04db2339126ac305cae9756f91444c047a6cf5d5ccd265bd49bf5922ba |
|
MD5 | 0438d55c66ba5811c83b8e9c698eacbc |
|
BLAKE2-256 | 78e4f85744905efe2b3b64c16570a39db0b2b5b1ae11edc440b05eb5f287237c |
Close
Hashes for cupy_cuda80-7.8.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 71536358e8218481110db47f375d87adc1636754fb12af879282d09473e76186 |
|
MD5 | c1a6fd088e1d737f97b64382e6937f86 |
|
BLAKE2-256 | b0b39af951ded2fee72b96ccfe323c592bb009e4dc08b468ad7bdcc373c58365 |
Close
Hashes for cupy_cuda80-7.8.0-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 41fa1c27bf700f2aa3d8387716a03ce1ebbf889e3984080211c671a3acfd8e1e |
|
MD5 | 76df1465ec3cf58fc4a00b955e417c85 |
|
BLAKE2-256 | 3c56a23fa75d6f14711aa83cf06a8d56a5692c0e649393284f4e64c2bce71dfe |
Close
Hashes for cupy_cuda80-7.8.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d2602d687a9e4ad32efe0f797efd544a8a57a7f68d5330a0f0bc6130b662adea |
|
MD5 | 2957840e9418ce85168c9366e0fa03f7 |
|
BLAKE2-256 | 04c4285da8dbd46ac368f09bc42cf2b936dbcd38b97ae4fdc410eb1ea77d06ba |
Close
Hashes for cupy_cuda80-7.8.0-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 248d7bd759e6b1daa0948ca0a615a5347f6763cfc7f5e6f353b99214fd451610 |
|
MD5 | 1fb475b324d49764f3bbfef73ad584b4 |
|
BLAKE2-256 | 21322c6b198b61522f584553e4009381f7fc0b17d5d6f063b0f39913f5acaec8 |
Close
Hashes for cupy_cuda80-7.8.0-cp38-cp38-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 86f36907f259c082b4b230ab6328b9a1b2e9dd0662b957f7090f5d3e308b7617 |
|
MD5 | 605ca4d5b215b2ef12ccd94a452d866d |
|
BLAKE2-256 | cd7e494255b3c05d52155ab866bae579dee1c9acd28bb55e1071c2ae5ead7db9 |
Close
Hashes for cupy_cuda80-7.8.0-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | dad9c4ea275727df60af3f269326a8d495476b3251472379961a512155266596 |
|
MD5 | 42471f148082a2e0bc54532e716ec385 |
|
BLAKE2-256 | 9ac10fb17199b461d36d4903caee0b59e928c12c6146ebedbe9981326bdda004 |