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_tf.graph
and trainers are in edgeml_tf.trainer. Usage directions and example notebook for
these algorithms are provided in the examples/tf directory.
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.
Use pip to install the requirements before installing the edgeml_tf library.
Details for cpu based installation and gpu based installation provided below.
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.