Clara Training Framework

Clara provides a training framework to help accelerate deep learning training and inference for medical imaging use cases. It allows medical imaging researchers and developers to quickly implement new models using a high-level intuitive API. This guide describes installation, essential concepts, and tutorials to help you get started with Clara.

User Guide

• Overview
• Converting from Clara 3.1 to Clara 4.0
  
  • MMAR Conversion Tool
  • Changes from Clara v3.X MMARs
• Essential concepts
  
  • Components
  • Data pipelines
  • Transforms
  • Handlers
  • Metrics
• Installation
  
  • System requirements
  • Download the docker container
  • Running the container
• Getting started with Clara
  
  • Supervised training
  • Using the data converter
  • Working with classification models
  • Working with segmentation models
• Medical Model Archive (MMAR)
  
  • Model training and validation configurations
  • MMAR Commands
  • Configuration
  • Using model.pt or final_model.pt
  • Cloning MMAR
• Bring your own components (BYOC)
  
  • Examples of BYOC
• AutoML
  
  • Clara AutoML high-level design
  • AutoML user guide
  • AutoML programming guide
  • AutoML search space definition for Clara Train
• API reference
  
  • API reference: MONAI Applications
  • API reference: MONAI Transforms
  • API reference: MONAI Loss functions
  • API reference: MONAI Network architectures
  • API reference: MONAI Metrics
  • API reference: MONAI Optimizers
  • API reference: MONAI Data
  • API reference: MONAI Engines
  • API reference: MONAI Inference methods
  • API reference: MONAI Event handlers
  • API reference: MONAI Visualizations
  • API reference: MONAI Utilities
• Clara Train FAQ
  
  • 1. Why should I use Clara Train?
  • 2. Does the order of handlers matter?
  • 3. Is determinism supported?
  • 4. How can I run the MMAR on a very small part of my dataset to quickly verify it?
  • 5. How to enable sampling for classification task to balance the dataset?
  • 6. How can I adjust the LR with the “ReduceLROnPlateau” scheduler based on validation metrics?
  • 7. How can I set different LR for different network layers?
  • 8. How can I enable TF32 for Ampere GPUs?
  • 9. How can I enable auto mixed precision (AMP)?
  • 10. How does AMP affect performance?
  • 11. What can I do if AMP doesn’t show me any difference in the model memory footprint?
  • 12. How can I save and load checkpoints of optimizer and lr_scheduler?
  • 13. Can I use the LoadImageD transform to load DICOM image or serials?
  • 14. How can I invert all the spatial transforms on a model prediction and compute metrics or save to NIFTI or PNG files?
  • 15. How can I set “torch.backends.cudnn.benckmark=True” to accelerate training?
  • 16. How can I set “find_unused_parameters=True” for distributed training if the network has some return values not included in loss computation?
  • 17. How can I enable SyncBN for multi-gpu training?
  • 18. Can I set several models, several losses, and several optimizers in a single MMAR?
  • 19. Where does CheckpointSaver go for saving a final model?
  • 20. How can I save “config_train.json” into the checkpoint during training and load the model config from checkpoint during validation?
  • 21. How can I apply “EarlyStop” logic based on the loss value or validation metrics during model training?
  • 22. How can I compute metrics on every class of the model output?
  • 23. How can handlers be configured to execute only on one rank in distributed data parallel?
  • 24. How can I register new events and trigger them in my own custom components?
  • 25. In order to do transfer learning on a dataset with different class numbers, how can I load the model and finetune?
  • 26. How can I save validation metrics details into reports?
• Appendix
  
  • Model training and validation configurations
  • Converting from Clara 3.1 to Clara 4.0
  • Clara Train - Getting started with a Cloud Service Provider