Lava-DL, but with PyTorch-Lightning flavour

Last update: Oct 31, 2022

Related tags

Deep Learning lava-dl-lightning

Overview

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

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

# install lava
wget https://github.com/lava-nc/lava/releases/download/v0.2.0/lava-nc-0.2.0.tar.gz
pip install -U pip
pip install lava-nc-0.2.0.tar.gz

# install lava-dl
wget https://github.com/lava-nc/lava-dl/releases/download/v0.1.1/lava-dl-0.1.1.tar.gz
pip install lava-dl-0.1.1.tar.gz

# install pytorch-lightning
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}
}

Lava-DL, but with PyTorch-Lightning flavour

Related tags

Overview

Deep learning project seed

Goals

DELETE EVERYTHING ABOVE FOR YOUR PROJECT

Your Project Name

Description

How to run

Imports

Citation

Owner

Sami BARCHID

JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces

Numerical-computing-is-fun - Learning numerical computing with notebooks for all ages.

Simple SN-GAN to generate CryptoPunks

Mapping Conditional Distributions for Domain Adaptation Under Generalized Target Shift

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

Code accompanying paper: Meta-Learning to Improve Pre-Training

PyTorch implementation of our ICCV 2019 paper: Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

A Tensorflow implementation of BicycleGAN.

The story of Chicken for Club Bing

MarcoPolo is a clustering-free approach to the exploration of bimodally expressed genes along with group information in single-cell RNA-seq data

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

In this project, we'll be making our own screen recorder in Python using some libraries.

Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

Pose estimation for iOS and android using TensorFlow 2.0

A self-supervised 3D representation learning framework named viewpoint bottleneck.

Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)