MXNet is an ultra-scalable deep learning framework. This version uses openblas.
Project description
MXNet Python Package
Release history Release notifications
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 mxnet-1.5.0b20190712-cp34-cp34m-macosx_10_11_x86_64.whl (14.2 MB) | File type Wheel | Python version cp34 | Upload date | Hashes View hashes |
| Filename, size mxnet-1.5.0b20190712-cp35-cp35m-macosx_10_11_x86_64.whl (14.2 MB) | File type Wheel | Python version cp35 | Upload date | Hashes View hashes |
| Filename, size mxnet-1.5.0b20190712-cp36-cp36m-macosx_10_11_x86_64.whl (14.2 MB) | File type Wheel | Python version cp36 | Upload date | Hashes View hashes |
| Filename, size mxnet-1.5.0b20190712-cp37-cp37m-macosx_10_11_x86_64.whl (14.2 MB) | File type Wheel | Python version cp37 | Upload date | Hashes View hashes |
| Filename, size mxnet-1.5.0b20190712-py2.py3-none-manylinux1_x86_64.whl (25.5 MB) | File type Wheel | Python version py2.py3 | Upload date | Hashes View hashes |
| Filename, size mxnet-1.5.0b20190712-py2.py3-none-win_amd64.whl (23.5 MB) | File type Wheel | Python version py2.py3 | Upload date | Hashes View hashes |
Close
Hashes for mxnet-1.5.0b20190712-cp34-cp34m-macosx_10_11_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 169447eb04b340f0f4529f31a75763905ac181f5bbc3e07a1bcbfb6dd5ca50c6 |
|
| MD5 | 613d4f9ee2ff9dbe309629c6b6806b01 |
|
| BLAKE2-256 | fdab846239101c6cca2f30290c503375df5b676956503df11fe609e1a54907bb |
Close
Hashes for mxnet-1.5.0b20190712-cp35-cp35m-macosx_10_11_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f2c6fe7646feffaa9bc34286e1b9b74548830197561725e460787a142127a2b5 |
|
| MD5 | 1bc5a9ab43a99c8fcd1b1a06d1316507 |
|
| BLAKE2-256 | e17fc57ecba1611b705b3539ff8bc1e567909dfe795aa5e86d763df11d1ed05a |
Close
Hashes for mxnet-1.5.0b20190712-cp36-cp36m-macosx_10_11_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a5974068eaf603a2b97570e1e3587c86d85899ba6130b6cc2a48f789fd27e9e5 |
|
| MD5 | 4fcd65d5a62fde5934c2f01737d6e288 |
|
| BLAKE2-256 | 20dafdbe52fea2b8c085ae65608020a13086cb6efb7a0f160bee0d6d41d5871d |
Close
Hashes for mxnet-1.5.0b20190712-cp37-cp37m-macosx_10_11_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | c97b97c7dc7ee33e6f2505af36cb79794f5aa99a23072d8ddacdbd9667bc05f5 |
|
| MD5 | bdb9e3a9e6c39ba8cf4748c8c8dd20f8 |
|
| BLAKE2-256 | a9ca7820f987ac12beebdae267fc945fcd58ab99ea23ace3636e2b2f8a8157e6 |
Close
Hashes for mxnet-1.5.0b20190712-py2.py3-none-manylinux1_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2690ee8a93f2b5d671a50dde7e88d8390b03b9501b61c12fa7cf8814fc07f259 |
|
| MD5 | 1e15393e0365eef796b9822440d909e2 |
|
| BLAKE2-256 | f47732a2c793222cf01a268dc11fbd19314e1c3ce809c7b082ff094d56fc4831 |
Close
Hashes for mxnet-1.5.0b20190712-py2.py3-none-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 04de07d616758455653c8c7ec92536fbc6c9cc701f93651110eb6e7b5677f084 |
|
| MD5 | ec44407b7267186e3e55f0468fc99c32 |
|
| BLAKE2-256 | 96bf615e9ab17cdc449d359cb41b94d59d4d5e4f07cbac6a66b6f7fea8ca2fa3 |
