The plan is to run a number of "peers" on one system, train them individually and share knowledge across the swarm of containers. We explore the performance of such a system with different peer configurations.

We work on top of the p2plib shared by James Bae to create a peer-to-peer aware application (currently closed-source).

• Demo UI code in demo/
• Demo server code in src/server.py
• Application code in src/p2plib/cmd/flat/fl_peer.py (along with changes in flat.go)
• Evaluation docker environment set up using the dockerfile and docker-compose.yaml
• PPT in ./Group11.FinalReport.pptx

First the python environment must be set up. To do this, go to the /pre folder. Run the two shell files:

cd pre
sudo sh install_fl.sh
sudo sh install_go.sh

Repeat the same process in other machines where you'd want to launch peers.

Note down the IP address on your machine using ifconfig. You can then start up a peer by running (and start an interface server):

cd src/p2plib/cmd/flat

# Start server in the background
python3 ../../../server.py &

# Start peer
python3 fl_peer.py <SELF_IP> <PORT>

To run more peers, enter those machines and start. Bootstrap IP and port are processes you have spun up before in other systems:

cd src/p2plib/cmd/flat
python3 fl_peer.py <SELF_IP> <PORT> <BOOTSTRAP_IP:PORT>

You can view the visualization by running the UI locally

cd demo

# Install dependencies
npm i

npm start

# Go to src/app/nodeviz/*.ts, change the IP address there to the machine where the 
# flask server is running. 

# Navigate to localhost:4200 in chrome

Execute ./run.sh. This will:

• Build the image, and setup the python env inside the image.
• Create a docker-internal network for communication across nodes. Also volume mount required dirs on the containers.
• Run the image as 6 different containers (acting as 6 peers) joining the same network incrementally.
• Start the container with fl_peer.py running at startup. Other peers will discover any container that joins in.
• Run the flask server for interaction on one of the nodes.

Once the container is created, follow these steps to access any container in the docker.

• sudo docker container ls - lists the containers with their ids in the docker
• sudo docker exec -it <container_id> bash You will have access to the container now. The source code is present in /home/work.

• For testing scalability, we brought up instances in AWS: https://aws.amazon.com/
• For spinning up the environment for development, we rely on docker: https://www.docker.com/
• All model training and evaluation is done using the tensorflow library (using Keras abstraction): https://www.tensorflow.org/
• Dataset used for evaluation is CIFAR-10: https://www.cs.toronto.edu/~kriz/cifar.html
• For the demo, we wrote the UI in angular (https://angular.io/) and the backend using Flask (https://flask.palletsprojects.com/en/2.0.x/)
• The application layer for the p2p nodes was written in Python (https://www.python.org/)