lightning/pl_examples
Chapman Siu 4bf1c3c0cb
Fix typo of handling (#2625)
2020-07-17 08:25:14 +02:00
..
basic_examples fix gpu example (#2466) 2020-07-03 00:17:18 -04:00
domain_templates Fix typo of handling (#2625) 2020-07-17 08:25:14 +02:00
models Amp2 (#2505) 2020-07-04 22:52:49 -04:00
README.md simplify examples structure (#1247) 2020-04-03 17:57:34 -04:00
__init__.py simplify examples structure (#1247) 2020-04-03 17:57:34 -04:00
test_examples.py fix gpu example (#2466) 2020-07-03 00:17:18 -04:00

README.md

Examples

This folder has 3 sections:

Basic Examples

Use these examples to test how lightning works.

Test on CPU

python cpu_template.py

Train on a single GPU

python gpu_template.py --gpus 1

DataParallel (dp)

Train on multiple GPUs using DataParallel.

python gpu_template.py --gpus 2 --distributed_backend dp

DistributedDataParallel (ddp)

Train on multiple GPUs using DistributedDataParallel

python gpu_template.py --gpus 2 --distributed_backend ddp

DistributedDataParallel+DP (ddp2)

Train on multiple GPUs using DistributedDataParallel + dataparallel. On a single node, uses all GPUs for 1 model. Then shares gradient information across nodes.

python gpu_template.py --gpus 2 --distributed_backend ddp2

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 ddp_job_submit.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 ddp2_job_submit.sh YourEnv

Domain templates

These are templates to show common approaches such as GANs and RL.