lightning/pl_examples/basic_examples/README.md

Basic Examples

Use these examples to test how lightning works.

MNIST

Trains MNIST where the model is defined inside the LightningModule.

# cpu
python mnist.py

# gpus (any number)
python mnist.py

# dataparallel
python mnist.py --gpus 2 --distributed_backend 'dp'

---

Image classifier

Generic image classifier with an arbitrary backbone (ie: a simple system)

# cpu
python image_classifier.py

# gpus (any number)
python image_classifier.py --gpus 2

# dataparallel
python image_classifier.py --gpus 2 --distributed_backend 'dp'

---

Autoencoder

Showing the power of a system... arbitrarily complex training loops

# cpu
python autoencoder.py

# gpus (any number)
python autoencoder.py --gpus 2

# dataparallel
python autoencoder.py --gpus 2 --distributed_backend 'dp'

---

Multi-node example

This demo launches a job using 2 GPUs on 2 different nodes (4 GPUs total). To run this demo do the following:

1. Log into the jumphost node of your SLURM-managed cluster.
2. Create a conda environment with Lightning and a GPU PyTorch version.
3. Choose a script to submit

DDP

Submit this job to run with DistributedDataParallel (2 nodes, 2 gpus each)

sbatch submit_ddp_job.sh YourEnv

DDP2

Submit this job to run with a different implementation of DistributedDataParallel. In this version, each node acts like DataParallel but syncs across nodes like DDP.

sbatch submit_ddp2_job.sh YourEnv