pip install mxnet-cu80==1.6.0b20190918

MXNet is an ultra-scalable deep learning framework. This version uses CUDA-8.0.

Source
Among top 5% packages on PyPI.
Over 20.2K downloads in the last 90 days.

Commonly used with mxnet-cu80

Based on how often these packages appear together in public requirements.txt files on GitHub.

Keras

Deep Learning for humans

tensorflow-gpu

TensorFlow is an open source machine learning framework for everyone.

skdata

Data Sets for Machine Learning in Python

Theano

Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

hyperas

Simple wrapper for hyperopt to do convenient hyperparameter optimization for Keras models

inspect-it

Reads your code and returns a JSON description you can use to generate documentation. Like Sphinx AutoDoc but without Sphinx.

display

simple printer of nested list

backports.weakref

Backport of new features in Python's weakref module

climate

Command line arguments parsing

nlpre

Natural Language Preprocessing (NLPre) utilities.

hdf5storage

Utilities to read/write Python types to/from HDF5 files, including MATLAB v7.3 MAT files.

fluteline

Easy thread based pipelines

watson-streaming

Speech to text transcription in real-time using IBM Watson

menpo

A Python toolkit for handling annotated data

menpo3d

Menpo library providing tools for 3D Computer Vision research

phe

Partially Homomorphic Encryption library for Python

downhill

Stochastic optimization routines for Theano

pyRserve

A Python client to remotely access the R statistic package via network

resampy

Efficient signal resampling

Version usage of mxnet-cu80

Proportion of downloaded versions in the last 3 months (only versions over 1%).

1.5.0

13.33%

1.2.0

6.32%

0.9.5

6.20%

1.1.0

5.88%

1.4.0

5.69%

1.4.1

5.63%

1.3.0

5.53%

1.1.0.post0

4.15%

1.4.0.post0

3.62%

1.0.0.post4

3.46%

1.3.1

3.22%

1.0.0.post2

3.08%

0.11.0

3.07%

1.2.1

3.06%

1.0.0.post1

2.74%

1.0.0.post0

2.45%

1.0.0

2.04%

0.9.3a3

1.84%

1.3.0.post0

1.74%

0.10.0

1.49%

0.9.3a1

1.48%

0.9.3a0

1.47%

1.2.1.post1

1.34%

0.12.0

1.32%

0.9.5.post1

1.31%

0.10.0.post2

1.24%