Deep learning project seed

Use this seed to start new deep learning / ML projects.

• Built in setup.py
• Built in requirements
• Examples with MNIST
• Badges
• Bibtex

Goals

The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.

DELETE EVERYTHING ABOVE FOR YOUR PROJECT

---

Your Project Name

Description

What it does

How to run

Requires Python 3.8+. First, install dependencies

# Create environment
cd ~
conda create -n lava python=3.8
conda activate lava
pip install -U pip

# Install Lava
git clone git@github.com:lava-nc/lava.git
cd lava
pip install -r build-requirements.txt
pip install -r requirements.txt
export PYTHONPATH=$(pwd)/src
ulimit -n 2048 # required or else the pyb command will be stuck
pyb -E unit

# Install lava-dl
cd ~
git clone git@github.com:lava-nc/lava-dl.git
cd lava-dl
pip install -r build-requirements.txt
pip install -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$(pwd)/src
pyb -E unit

git clone https://github.com/Barchid/lava-dl-lightning
cd lava-dl-lightning

# install pytorch-lightning & other useful libs
pip install pytorch-lightning torchmetrics

Next, navigate to any file and run it.

# module folder
cd project

# run module (example: mnist as your main contribution)   
python lit_classifier_main.py    

Imports

This project is setup as a package which means you can now easily import any file into any other file like so:

from project.datasets.mnist import mnist
from project.lit_classifier_main import LitClassifier
from pytorch_lightning import Trainer

# model
model = LitClassifier()

# data
train, val, test = mnist()

# train
trainer = Trainer()
trainer.fit(model, train, val)

# test using the best model!
trainer.test(test_dataloaders=test)

Citation

@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}

Plan

• Prints SDN to know about the input/output tensors
• Make nmnist work + prints to know about
  • Print after Spike classifiers #!important
• DVSGesture Datamodule
  • Transforms
  • Tonic
• Model Slayer
• Lightning Module
• Make it run
• Make Notebook

Barlow Twins

• DataModule + transforms (simple)
• Model
• How to get good representation ?
  • Rate of spikes ?
  • SpikeMax ?
  • Latency ?
  • Mean over output spike trains ?
  • ANN projector ?
• Launch