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Table Of Contents

Install

Platform: Local Cloud

Provider: Alibaba AWS Google Cloud Microsoft Azure Oracle Cloud

Installation Guides:

OS: Linux macOS Windows

Package: Pip Docker

Backend: Native CUDA MKL-DNN CUDA + MKL-DNN

Build-in backend for CPU.
Required to run on Nvidia GPUs.
Accelerate Intel CPU performance.
Enable both Nvidia CPUs and Inter CPU acceleration.

Prerequisites:

  • Requires docker and Docker can be used by a non-root user.

  • Requires pip >= 9.. Both Python 2 and Python 3 are supported.

  • Hint: append the flag --pre at the end of the command will install the nightly build.

  • Requires CUDA. Supported versions include 8.0, 9.0, and 9.2.

  • Hint: cuDNN is already included in the MXNet binary, so you don’t need to install it.

  • Hint: MKL-DNN is already included in the MXNet binary, so you don’t need to install it.

Command:

pip install mxnet
# Here we assume CUDA 9.2 is installed. You can change the number
# according to your own CUDA version.
pip install mxnet-cu92
pip install mxnet-mkl
# Here we assume CUDA 9.2 is installed. You can change the number
# according to your own CUDA version.
pip install mxnet-cu92mkl
docker pull mxnet/python
docker pull mxnet/python:gpu
docker pull mxnet/python:1.3.0_cpu_mkl
docker pull mxnet/python:1.3.0_gpu_cu90_mkl_py3

Next steps

  • For new users: Crash Course. It contains a 60-minutes crash course using Gluon that teaches you how to train a handwritten digits classifier.

  • For experienced users: Guide. Check out the variety of MXNet guides.

  • For advanced users: API. Browse the MXNet classes and methods.

Table Of Contents