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PyTorch Lightning template for rapid, scalable and reproducible ML experimentation with best practices.

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Lightning-Template

A clean and scalable template 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

Arxiv Conference

Description

What it does

How to run

First, install dependencies

# clone project
git clone https://github.com/YourGithubName/your-repository-name
cd your-repository-name

# [SUGGESTED] use conda environment
conda env create -f environment.yaml
conda activate lit-template

# [ALTERNATIVE] install requirements directly
pip install -r requirements.txt

Next, to obtain the main results of the paper:

# commands to get the main results

You can also run experiments with the run script.

# fit with the demo config
./run fit --config configs/demo.yaml
# or specific command line arguments
./run fit --model MNISTModel --data MNISTDataModule --data.batch_size 32 --trainer.gpus 0

# evaluate with the checkpoint
./run test --config configs/demo.yaml --ckpt_path ckpt_path

# get the script help
./run --help
./run fit --help

Citation

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

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PyTorch Lightning template for rapid, scalable and reproducible ML experimentation with best practices.

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