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src
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1.train-single-node.ipynb
1.train-single-node.ipynb
 
 
2.log-with-tensorboard.ipynb
2.log-with-tensorboard.ipynb
 
 
3.log-with-mlflow.ipynb
3.log-with-mlflow.ipynb
 
 
4.train-multi-node-ddp.ipynb
4.train-multi-node-ddp.ipynb
 
 
README.md
README.md
 
 
environment.yml
environment.yml
 
 

README.md

page_type languages products description experimental
sample
python
azurecli
azure-machine-learning
Learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and Azure ML.
issues with multinode pytorch lightning

Train with PyTorch Lightning

PyTorch Lightning is a lightweight open-source library that provides a high-level interface for PyTorch.

The model training code for this tutorial can be found in src. This tutorial goes over the steps to run PyTorch Lightning on Azure ML, and it includes the following parts:

  1. train-single-node: Train single-node and single-node, multi-GPU PyTorch Lightning on Azure ML.
  2. log-with-tensorboard: Use Lightning's built-in TensorBoardLogger to log metrics and leverage Azure ML's TensorBoard integration.
  3. log-with-mlflow: Use Lightning's MLFlowLogger to log metrics and leverage Azure ML's MLflow integration.
  4. train-multi-node-ddp: Train multi-node, multi-GPU PyTorch Lightning with DistributedDataParallel (DDP) on Azure ML.