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Edge Machine Learning: Tensorflow Library

This directory includes, Tensorflow implementations of various techniques and algorithms developed as part of EdgeML. Currently, the following algorithms are available in Tensorflow:

  1. Bonsai
  2. EMI-RNN
  3. FastRNN & FastGRNN
  4. ProtoNN

The TensorFlow compute graphs for these algoriths are packaged as edgeml.graph. Trainers for these algorithms are in edgeml.trainer. Usage directions and examples for these algorithms are provided in examples directory. To get started with any of the provided algorithms, please follow the notebooks in the the examples directory.


Use pip and the provided requirements file to first install required dependencies before installing the edgeml library. Details for cpu based installation and gpu based installation provided below.

It is highly recommended that EdgeML be installed in a virtual environment. Please create a new virtual environment using your environment manager (virtualenv or Anaconda). Make sure the new environment is active before running the below mentioned commands.


pip install -r requirements-cpu.txt
pip install -e .

Tested on Python3.5 and python 2.7 with >= Tensorflow 1.6.0.


Install appropriate CUDA and cuDNN [Tested with >= CUDA 8.1 and cuDNN >= 6.1]

pip install -r requirements-gpu.txt
pip install -e .

Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT license.

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