NErlNet

Nerlnet is an open-source framework designed for researching and deploying distributed machine learning algorithms on IoT devices. It offers comprehensive insights into both edge devices running neural network models and the performance and statistics of network operations. With the ability to simulate distributed ML clusters on a single machine or across multiple machines, Nerlnet enables seamless deployment of these clusters—with minimal modifications—onto various types of IoT devices.

By streamlining the setup of distributed clusters composed of multiple edge models, Nerlnet provides full control and monitoring over communication flows. Its Python API empowers users to manage experiments efficiently, collect data, and analyze performance metrics throughout the research process.

Nerlnet library combines the following languages to achieve a stable and efficient distributed ML system framework:
• The communication layer of Nerlnet is based on an Cowboy - an HTTP web server open-source library.
• ML on the edge of the distributed cluster is based on OpenNN library, an open-source project of Cpp Neural Network library.
• Management of Nerlnet cluster - An HTTP server of Flask communicates with Nerlnet's main server to control the cluster's entities.

Nerlnet cluster is defined by three configuration files (Json files):

• Distributed Configuration that defines entities of Nerlnet: Source, Router, Client.
  • A client is a host of workers. A worker is a NN model that can move between phases of train and predict.
  • Source generates data streams that are sent to workers.
  • Router controls the data flow through Nerlnet cluster.

References and libraries:

• OpenNN, an open-source neural networks library for machine learning.
  
• Cowboy an HTTP server for Erlang/OTP.
  
• NIFPP C++11 Wrapper for Erlang NIF API.
  
• Rebar3, an Erlang tool that makes it easy to create, develop, and release Erlang libraries, applications, and systems in a repeatable manner.
• Simple Cpp Logger, simple cpp logger headers-only implementation.

Nerlnet is developed by David Leon, Dr. Yehuda Ben-Shimol, and the community of Nerlnet open-source contributors.
Academic researchers can use Nerlnet for free, provided they cite this repository.

Nerlnet Architecture Example:

Build and Run Nerlnet:

Recommended cmake version 3.26
Minimum erlang version otp 25 (Tested 24,25,26,28)
Minimum gcc/g++ version 10.3.0

On every device that hosts Nerlnet cluster entities, do the following steps:

1. Clone this repository with its subomdules git clone --recurse-submodules <link to this repo> NErlNet
   
2. Run sudo ./NerlnetInstall.sh
   2.1 With argument -i script builds and installs Erlang (OTP 28), and CMake from source. (validate that erlang is not installed before executing installation from source)
   2.2 On successful installation, NErlNet directory is accessible
       via the following path: /usr/local/lib/nerlnet-lib
3. Run ./NerlnetBuild.sh
4. Test Nerlnet by running: ./tests/NerlnetFullFlowTest.sh
5. Nerlplanner is a Nerlnet tool to generate required jsons files to setup a distributed system of Nerlnet.
   To use NerlPlanner execute ./NerlPlanner.sh.
   Create json files of distributed configurations, connection map and experiment flow as follows:
   
6. dc_<any name>.json
   
7. conn_<any name>.json
   
8. exp_<any name>.json
   
9. Run ./NerlnetRun.sh.
10. On API-Server device, Start Jupyter NB with ./NerlnetJupyterLaunch.sh and follow ApiServerInstance.help() and examples.

Python API and Jupyter-lab (For Api-Server):

Minimum Python version: 3.8

Communication with Nerlnet is done through a simple python API that can be easily used through Jupyter notebook.
The API allows the user to collect statistics insights of a distributed machine learning network:
Number of messages, throughput, loss, predictions, models performance, etc.

Instructions

1. Open a jupyter lab environment using ./NerlnetJupyterLaunch.sh -d <experiment_direcotry>
   1.1 Use -h to see the help menu of NerlnetJupyterLaunch.sh script.
   1.2 If --no-venv option is selected then required modules can be read from src_py/requirements.txt.
   
2. Read the instructions of importing Api-Server within the generated readme.md file inside folder.
3. Follow the Example Notebook

Distributed ML on The Edge

Distributed ML on the edge - A new evolution step of AI.

https://github.com/leondavi/NErlNet/assets/18975070/15a3957a-3fd6-4fb2-a365-7e1578468298

Gratitudes

Amazon AWS

A grant of AWS credits as part of AWSOpen program for open source projects (2025-2027).

Microsoft Azure

A grant of Azure credits as part of Microsoft’s Azure credits for open source projects program (2024-2025).