nn-stat 1.2.1

Neural Network Performance Analysis

Neural Network Performance Analysis

_{short alias lmurs}

The original version of the NN Stat project was created by Waleed Khalid at the Computer Vision Laboratory, University of Würzburg, Germany.

Overview 📖

Automated conversion of LEMUR data into Excel format with statistical visualizations. It is developed to support the NN Dataset and NNGPT projects.

Create and Activate a Virtual Environment (recommended)

For Linux/Mac:

python3 -m venv .venv
source .venv/bin/activate

For Windows:

python3 -m venv .venv
.venv\Scripts\activate

It is assumed that CUDA 12.6 is installed. If you have a different version, please replace 'cu126' with the appropriate version number.

Environment for NN Stat Contributors

Run the following command to install all the project dependencies:

python -m pip install --upgrade pip
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu126

Installation with the LEMUR Dataset

pip install nn-stat[dataset]

Usage

python -m ab.stat.export

Data and statistics are stored in the stat directory in Excel files and PNG/SVG plots.

To use 'ab/stat/nn_analytics.ipynb' install jupyter:

pip install jupyter

and run jupyter notebook:

jupyter notebook --notebook-dir=.

Update of NN Dataset

Remove old version of the LEMUR Dataset and its database:

pip uninstall nn-dataset -y
rm -rf db

Install from GitHub to get the most recent code and statistics updates:

pip install git+https://github.com/ABrain-One/nn-dataset --upgrade --force --extra-index-url https://download.pytorch.org/whl/cu126

Installing the stable version:

pip install nn-dataset --upgrade --extra-index-url https://download.pytorch.org/whl/cu126

Docker

All versions of this project are compatible with AI Linux and can be run inside a Docker image:

docker run -v /a/mm:. abrainone/ai-linux bash -c "PYTHONPATH=/a/mm python -m ab.stat.export"

Some recently added dependencies might be missing in the AI Linux. In this case, you can create a container from the Docker image abrainone/ai-linux, install the missing packages (preferably using pip install <package name>), and then create a new image from the container using docker commit <container name> <new image name>. You can use this new image locally or push it to the registry for deployment on the computer cluster.

The idea and leadership of Dr. Ignatov