nn-stat 1.1.0

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 NN GPT projects.

Create and Activate a Virtual Environment (recommended):

python -m venv .venv
source .venv/bin/activate   # Linux/Mac
.venv\Scripts\activate      # Windows

All subsequent commands are provided for Linux/Mac OS. For Windows, please replace source .venv/bin/activate with .venv\Scripts\activate.

Environment for NN Stat Contributors

Run the following command to install all the project dependencies:

source .venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu124

Installation with the LEMUR Dataset

source .venv/bin/activate
pip install nn-stat[dataset]

Usage

source .venv/bin/activate
python -m ab.stat.export

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

Update of NN Dataset

Remove old version of the LEMUR Dataset and its database:

source .venv/bin/activate
pip uninstall nn-dataset -y
rm -rf db

Installing the stable version:

source .venv/bin/activate
pip install nn-dataset --upgrade --extra-index-url https://download.pytorch.org/whl/cu124

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

source .venv/bin/activate
pip install git+https://github.com/ABrain-One/nn-dataset --upgrade --force --extra-index-url https://download.pytorch.org/whl/cu124

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 of Dr. Dmitry Ignatov