Pytorch vs Pytorch Lightning
PyTorch and PyTorch Lightning are both frameworks for building and training neural network models, but they differ in terms of abstraction, structure, and ease of use. Here are some key differences between PyTorch and PyTorch Lightning:
Features | PyTorch | PyTorch Lightning |
---|---|---|
Training Loop | User-defined, training loop, validation loop, and testing loop explicitly, including handling aspects like moving data to the GPU, computing gradients, and updating model parameters. | Users define hooks and callbacks to customize behavior without directly modifying the training loop. |
Model Setup | User define the model, loss function, optimizer, and other components explicitly. | Standardized with dedicated methods |
Abstraction Level | Lower-level, requires more manual coding | Higher-level, hides boilerplate code |
GPU and Distributed Training | Requires manual efforts for explicitly moving models and data to GPU, manage distributed training, and handle multi-GPU scenarios. | Automatic based on user configuration. Users can specify the number of GPUs. |
Logging and Experiment Tracking | Logging metrics and tracking experiments require manual implementation using tools like TensorBoard or custom loggers. | built-in support for various logging frameworks (TensorBoard, CSV, etc.) and experiment tracking platforms (e.g., WandB, Comet) |
Debugging and Profiling | Manual instrumentation | Provides hooks for common debugging and profiling tools. |
Integration with other Libraries | User-managed integrations | Built-in integrations |
Best Practices | Independent discovery | Benefits from a growing community and ecosystem, allowing users to leverage pre-built components and best practices. |
Standardized Interface | Code structure may vary. | Enforces a consistent structure through LightningModule. |
Module System | Left to developer discretion. | Promotes a modular system with LightningModules. |
Checkpointing | Users must implement checkpointing logic. | Built-in support for simplified model checkpointing. |
PyTorch vs PyTorch Lightning
The PyTorch research team at Facebook AI Research (FAIR) introduced PyTorch Lightning to address these challenges and provide a more organized and standardized approach. In this article, we will see the major differences between PyTorch Lightning and Pytorch.
Table of Content
- Pytorch
- Pytorch Lightning: Advanced Framework of Pytorch
- Pytorch vs Pytorch Lightning
- Implementation: From Pytorch to Pytorch Lightning
- Code Difference between PyTorch and PyTorch Lightning