PyTorch Lightning
PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. With Neptune integration you can:
monitor model training live,
log training, validation, and testing metrics, and visualize them in the Neptune UI,
log hyperparameters,
monitor hardware usage,
log any additional metrics,
log performance charts and images,
save model checkpoints.
To install Neptune + PyTorch-Lightning integration go to your console and run:
pip install 'neptune-client[pytorch-lightning]'
from neptune.new.integrations.pytorch_lightning import NeptuneLogger​neptune_logger = NeptuneLogger(api_token='<YOUR_API_TOKEN>')project='<YOUR_WORKSPACE/YOUR_PROJECT>',name='lightning-run', # Optional)
Pass your Neptune Project name and API token to NeptuneLogger.
See how to:
​Your full project name​
Pass NeptuneLogger instance to lightning Trainer to log model training metadata to Neptune:
from pytorch_lightning import Trainer​trainer = Trainer(max_epochs=10, logger=neptune_logger)
Pass your lightning Module and training Loader to Trainer and run .fit():
trainer.fit(model, train_loader)
You just learned how to start logging PyTorch Lightning model training runs to Neptune, by using Neptune logger.
You can use log Images, model checkpoints, and other ML metadata from inside your training and evaluation steps.
To do that you need to:
access the Run object at
self.logger.experimentuse one of the Neptune logging methods
from neptune.new.types import File​class LitModel(LightningModule):def training_step(self, batch, batch_idx):# log metricsacc = ...self.logger.experiment['train/acc'].log(acc)# log imagesimg = ...self.logger.experiment['train/misclassified_images'].log(File.as_image(img))​def any_lightning_module_function_or_hook(self):# log model checkpoint...self.logger.experiment['checkpoints/epoch37'].upload('epoch=37.ckpt')# generic recipemetadata = ...self.logger.experiment['your/metadata/structure'].log(metadata)
You can log other model-building metadata like metrics, images, video, audio, interactive visualizations, and more. See What can you log and display?​
If you want to log objects after the training is finished use close_after_fit=False:
neptune_logger = NeptuneLogger(...,close_after_fit=False)trainer = Trainer(logger=neptune_logger)trainer.fit(model)​# Log confusion matrix after trainingfrom neptune.new.types import Filefrom scikitplot.metrics import plot_confusion_matriximport matplotlib.pyplot as plt​fig, ax = plt.subplots(figsize=(16, 12))plot_confusion_matrix(y_true, y_pred, ax=ax)neptune_logger.experiment['test/confusion_matrix'].upload(File.as_image(fig))​# Stop loggingneptune_logger.experiment.stop()
You can also pass kwargs to specify the Run in greater detail, like tags and description:
neptune_logger = NeptuneLogger(project='common/new-pytorch-lightning-integration',name='lightning-run',description='mlp quick run with pytorch-lightning',tags=['mlp', 'quick-run'],)trainer = Trainer(max_epochs=3, logger=neptune_logger)
For more information about the Neptune Run, see Core Concepts.
