History

Jirka Borovec 22a7264e9a improve partial Codecov (#1172 ) * ignore in setup * show report * abs imports * abstract pass * cover loggers * doctest trains * locals * pass * revert tensorboard * use tensorboardX * revert tensorboardX * fix trains * Add TrainsLogger.set_credentials (#1179) * Add TrainsLogger.set_credentials to control trains server configuration and authentication from code. Sync trains package version. Fix CI Trains tests * Add global TrainsLogger set_bypass_mode (#1187) * Add global TrainsLogger set_bypass_mode skips all external communication Co-authored-by: bmartinn <> * rm some no-cov Co-authored-by: Martin.B <51887611+bmartinn@users.noreply.github.com>		2020-03-19 09:14:29 -04:00
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README.md	changes examples to pl_examples for name connflict	2019-10-19 00:41:17 +02:00
__init__.py	changes examples to pl_examples for name connflict	2019-10-19 00:41:17 +02:00
cpu_template.py	fix max_epochs setup in basic example (#1105 )	2020-03-12 12:42:52 -04:00
gpu_template.py	fix max_epochs setup in basic example (#1105 )	2020-03-12 12:42:52 -04:00
lightning_module_template.py	improve partial Codecov (#1172 )	2020-03-19 09:14:29 -04:00

README.md

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