Skip to main content

MXNet is an ultra-scalable deep learning framework. This version uses CUDA-10.1 and MKLDNN.

Project description

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.

For feature requests on the PyPI package, suggestions, and issue reports, create an issue by clicking here.

Prerequisites

This package supports Linux and Windows platforms. You may also want to check: - mxnet-cu101 with CUDA-10.1 support. - mxnet-cu100 with CUDA-10.0 support. - mxnet-cu100mkl with CUDA-10.0 support and MKLDNN support. - mxnet-cu92 with CUDA-9.2 support. - mxnet-cu92mkl with CUDA-9.2 support and MKLDNN support. - mxnet-cu90 with CUDA-9.0 support. - mxnet-cu90mkl with CUDA-9.0 support and MKLDNN support. - mxnet-cu80 with CUDA-8.0 support. - mxnet-cu80mkl with CUDA-8.0 support and MKLDNN support. - mxnet-cu75 with CUDA-7.5 support. - mxnet-cu75mkl with CUDA-7.5 support and MKLDNN support. - mxnet-mkl with MKLDNN support. - mxnet.

To download CUDA, check CUDA download. For more instructions, check CUDA Toolkit online documentation.

To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions of CUDA, check Installing MXNet for instructions on building from source.

Installation

To install:

pip install mxnet-cu101mkl

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

mxnet_cu101mkl-1.6.0.post0-py2.py3-none-manylinux1_x86_64.whl (712.3 MB view details)

Uploaded Python 2 Python 3

File details

Details for the file mxnet_cu101mkl-1.6.0.post0-py2.py3-none-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for mxnet_cu101mkl-1.6.0.post0-py2.py3-none-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 2d49731fc3a35a978e2aaec331fc1450e419faab88a94c242755668f5ef389e4
MD5 ad1b69da2b2cb01a16ade30fe26fde57
BLAKE2b-256 453fe33e3f92110fa5caba5e9eb052008208a33c1d5faccc7fe5312532e9aa42

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page