EdgeML

История

Harsha Vardhan Simhadri eb1b95a4be added setup file		2019-08-22 01:48:06 +05:30
..
edgeml_tf	moved TF and PyTorch packages to another folder	2019-08-21 23:20:51 +05:30
README.md	more fixes	2019-08-22 00:50:56 +05:30
requirements-cpu.txt	more fixes	2019-08-22 00:50:56 +05:30
requirements-gpu.txt	more fixes	2019-08-22 00:50:56 +05:30
setup.py	added setup file	2019-08-22 01:48:06 +05:30

README.md

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_tf.graph. Trainers for these algorithms are in edgeml_tf.trainer. Usage directions and examples for these algorithms are provided in $EDGEML_ROOT/examples/tf directory. To get started with any of the provided algorithms, please follow the notebooks in the examples/tf directory.

Installation

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.

CPU

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

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

GPU

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

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