https://github.com/xinshuoweng/cnnramwatcher
Memory Monitor and Net Visualizer for Deep Neural Network
https://github.com/xinshuoweng/cnnramwatcher
caffe memory monitor tensorflow torch visualizer
Last synced: 10 months ago
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Memory Monitor and Net Visualizer for Deep Neural Network
- Host: GitHub
- URL: https://github.com/xinshuoweng/cnnramwatcher
- Owner: xinshuoweng
- License: mit
- Created: 2017-04-02T18:27:17.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-04-06T17:44:42.000Z (about 9 years ago)
- Last Synced: 2025-08-16T17:44:20.310Z (11 months ago)
- Topics: caffe, memory, monitor, tensorflow, torch, visualizer
- Language: Python
- Homepage:
- Size: 1.59 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Ramwatcher
A very lightweight deep learning tool for monitoring **data flow**, **parameter size** and their corresponding **memory usage** throughout deep neural network. This tool **doesn't** need any powerful computational resource (eg. GPU). And it's very easy to use since it follows many similar rules in popular deep learning frameworks (**Caffe**, **Tensorflow**, **Torch**)
#### Usage:
*1. clone the github repository recursively including xinshuo_toolbox.*
~~~shell
git clone --recursive https://github.com/xinshuoweng/ramwatcher
~~~
*2. install dependency for the toolbox.*
~~~shell
cd ramwatcher/xinshuo_toolbox
pip install -r requirements.txt
~~~
*3. define the network and print the memory info (one might want to look at example.py first for a quick and simple instruction).*
~~~shell
cd ..
python example.py
~~~
#### Example Result:
*Network Info Table:*
*Memory Usage Pie Chart:*
*Model Graph:*
#### Limitation:
*1. Only basic layers (Convolution, Pooling, Activation, Dense, Concat) are supported right now. More layers will be added in the future.*
*2. Add prototxt parse function in the future.*