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