Advanced PyTorch Lightning: Scaling and Optimizing Deep Learning Workflows

Course Goal: To empower learners with advanced techniques for building, scaling, and optimizing deep learning models using PyTorch Lightning, incorporating the latest features and best practices, including DeepSpeed and Fully Sharded Data Parallelism (FSDP).

Prerequisites:

• Successful completion of "Modern AI Development: From Transformers to Generative Models" or equivalent experience. This implies a strong understanding of:
  • Python programming
  • Object-Oriented Programming (OOP) principles
  • Calculus and Linear Algebra fundamentals
  • Core Machine Learning concepts
  • PyTorch basics (Tensors, Autograd, nn.Module, Dataset, DataLoader)
  • Transformer architectures and their applications
  • Generative models (Diffusion, Flow Matching - at a conceptual level)
  • Basic usage of the Hugging Face ecosystem (Transformers, Datasets, Diffusers)
• Familiarity with basic PyTorch Lightning concepts (e.g., LightningModule, Trainer).

Course Duration: Approximately 8-10 weeks, with each module taking roughly 1 week.

Tools:

• Python (>= 3.8)
• PyTorch (latest stable version)
• PyTorch Lightning (latest stable version)
• DeepSpeed (latest stable version)
• Hugging Face Transformers library
• Hugging Face Datasets library
• Hugging Face Accelerate library (for comparison and specific use-cases)
• Weights & Biases (or other experiment tracking tools)
• Jupyter Notebooks/Google Colab
• Standard Python libraries (NumPy, Pandas, Matplotlib, etc.)
• Access to GPU resources (strongly recommended, especially for distributed training modules)

Curriculum Draft:

Module 1: Deep Dive into PyTorch Lightning Core (Week 1)

• Topic 1.1: Revisiting the Lightning Paradigm and Core Components:
  • The philosophy behind PyTorch Lightning: Structure, Reproducibility, and Engineering Best Practices.
  • In-depth exploration of LightningModule: Understanding the lifecycle methods (training_step, validation_step, test_step, configure_optimizers, etc.).
  • Mastering the Trainer: Dissecting key arguments and their implications for training behavior.
  • Understanding the relationship between Lightning and vanilla PyTorch.
• Topic 1.2: Advanced Trainer Configurations and Control:
  • Fine-tuning training loops with callbacks: Implementing custom behaviors, early stopping, learning rate monitoring, and more.
  • Utilizing loggers effectively: Integrating with Weights & Biases, TensorBoard, and other experiment tracking tools.
  • Checkpointing strategies: Implementing advanced checkpointing for fault tolerance and resuming training.
  • Understanding and customizing the default progress bar.
• Topic 1.3: Data Loading and Management in Lightning:
  • Optimizing DataLoader performance: Understanding num_workers, pin_memory, and other parameters.
  • Advanced data augmentation strategies within Lightning.
  • Working with large datasets efficiently: Exploring techniques like iterable datasets.
• Topic 1.4: Debugging and Testing Lightning Applications:
  • Effective debugging strategies for Lightning models.
  • Writing unit and integration tests for LightningModule and callbacks.
  • Using Lightning's built-in testing capabilities.
• Hands-on Exercises: Implementing custom callbacks for various tasks (e.g., cosine annealing learning rate scheduler, custom metric logging), setting up robust checkpointing, optimizing DataLoader performance for a given dataset.

Module 2: Scaling with PyTorch Lightning - Multi-GPU Training (Week 2)

• Topic 2.1: Introduction to Data Parallelism (DP):
  • Understanding the concept and implementation of Data Parallelism.
  • Using Lightning's simple multi-GPU training with gpus argument.
  • Limitations of basic Data Parallelism for large models.
• Topic 2.2: Distributed Data Parallel (DDP) with Lightning:
  • Concepts of distributed training: Processes, ranks, and communication.
  • Implementing DDP using Lightning's strategy="ddp" or strategy="ddp_spawn".
  • Understanding the role of the launcher and environment variables.
  • Ensuring reproducibility in distributed settings.
• Topic 2.3: Optimizing Communication Overhead in DDP:
  • Gradient accumulation: Reducing communication frequency.
  • Understanding and utilizing the sync_batchnorm option.
  • Strategies for efficient all-reduce operations.
• Topic 2.4: Advanced DDP Configurations:
  • Using specific device types (e.g., specifying GPUs).
  • Customizing the DDP communication backend.
  • Troubleshooting common DDP issues.
• Hands-on Exercises: Training a medium-sized Transformer model on multiple GPUs using DDP, experimenting with gradient accumulation and sync_batchnorm, analyzing the impact on training speed and memory usage.

Module 3: Leveraging DeepSpeed with PyTorch Lightning (Week 3 & 4)

• Topic 3.1: Introduction to DeepSpeed:
  • The motivation behind DeepSpeed: Addressing memory limitations and accelerating training.
  • Key features of DeepSpeed: ZeRO optimizers, mixed precision training, gradient accumulation, and more.
  • Understanding the trade-offs and benefits of using DeepSpeed.
• Topic 3.2: Integrating DeepSpeed into PyTorch Lightning:
  • Using the DeepSpeedStrategy in the Trainer.
  • Automatic configuration and the role of the DeepSpeed configuration file.
  • Understanding the relationship between Lightning's Trainer and DeepSpeed's engine.
• Topic 3.3: Exploring DeepSpeed ZeRO Optimizers:
  • ZeRO Stage 1 (Optimizer State Partitioning).
  • ZeRO Stage 2 (Optimizer State + Gradients Partitioning).
  • ZeRO Stage 3 (Optimizer State + Gradients + Parameters Partitioning).
  • Choosing the appropriate ZeRO stage for different model sizes and hardware.
• Topic 3.4: Mixed Precision Training with DeepSpeed:
  • Benefits of mixed precision (FP16, BF16) for speed and memory.
  • Automatic mixed precision (AMP) with DeepSpeed.
  • Handling numerical stability issues in mixed precision training.
• Topic 3.5: Advanced DeepSpeed Features:
  • DeepSpeed-Inference for optimized inference.
  • Utilizing DeepSpeed's checkpoint saving and loading mechanisms.
  • Profiling and debugging DeepSpeed training runs.
• Hands-on Exercises: Training a large Transformer model (e.g., a smaller version of GPT) using different DeepSpeed ZeRO stages, experimenting with mixed precision training, comparing training performance with and without DeepSpeed, analyzing memory footprint.

Module 4: Fully Sharded Data Parallelism (FSDP) with PyTorch Lightning (Week 5 & 6)

• Topic 4.1: Understanding Fully Sharded Data Parallelism (FSDP):
  • The limitations of DDP for extremely large models.
  • Key concepts of FSDP: Parameter Sharding, Gradient Communication, and Activation Checkpointing.
  • Benefits of FSDP compared to DDP and DeepSpeed ZeRO.
• Topic 4.2: Implementing FSDP in PyTorch Lightning:
  • Using the FSDPStrategy in the Trainer.
  • Understanding FSDP auto-wrapping and manual wrapping strategies.
  • Configuring FSDP parameters: sharding_strategy, mixed_precision, backward_prefetch.
• Topic 4.3: Optimizing FSDP Performance:
  • Activation checkpointing (or gradient checkpointing) to reduce memory usage.
  • Understanding and configuring backward_prefetch.
  • Choosing the appropriate sharding strategy for your model and hardware.
• Topic 4.4: Comparison of FSDP and DeepSpeed:
  • Contrasting the implementation and performance characteristics of FSDP and DeepSpeed.
  • When to choose FSDP over DeepSpeed and vice versa.
  • Exploring hybrid approaches.
• Topic 4.5: Advanced FSDP Configurations and Troubleshooting:
  • Working with nested modules and complex model architectures in FSDP.
  • Debugging common FSDP issues and performance bottlenecks.
  • Integrating FSDP with custom training loops.
• Hands-on Exercises: Training a very large language model (if resources permit, or a suitably sized model to demonstrate the benefits) using FSDP, experimenting with different sharding strategies and activation checkpointing, comparing performance with DeepSpeed (if possible), analyzing memory savings.

Module 5: PyTorch Lightning Ecosystem and Integrations (Week 7)

• Topic 5.1: Lightning Flash: High-Level Tasks and Applications:
  • Introduction to Lightning Flash for simplifying common AI tasks (image classification, text classification, etc.).
  • Leveraging pre-built components and workflows in Flash.
  • Extending and customizing Flash tasks for specific needs.
• Topic 5.2: Lightning Fabric: Fine-Grained Control and Flexibility:
  • Understanding the purpose of Lightning Fabric for users who need more control.
  • Using Fabric to manage distributed training loops and hardware.
  • When to choose Fabric over the full Trainer.
• Topic 5.3: Integrating with Experiment Tracking Tools:
  • In-depth look at using Weights & Biases (or Neptune.ai, etc.) with Lightning.
  • Advanced logging techniques: Custom metrics, visualizations, artifact tracking.
  • Hyperparameter optimization with Weights & Biases Sweeps and Lightning.
• Topic 5.4: Deployment Considerations with PyTorch Lightning:
  • Exporting Lightning models for inference.
  • Brief overview of deployment options (e.g., TorchServe, ONNX).
  • Considerations for deploying large, distributed models.
• Hands-on Exercises: Building a simple application using Lightning Flash, experimenting with Lightning Fabric for a custom training loop, setting up comprehensive experiment tracking with Weights & Biases, exploring model export options.

Module 6: Advanced Topics and Best Practices (Week 8)

• Topic 6.1: Optimizing Training for Speed and Efficiency:
  • Profiling PyTorch Lightning models and identifying bottlenecks.
  • Techniques for optimizing data loading and preprocessing.
  • Exploring different hardware accelerators and their integration with Lightning (e.g., TPUs).
• Topic 6.2: Advanced Learning Rate Scheduling and Optimization Techniques:
  • Implementing sophisticated learning rate schedules (e.g., cyclical learning rates, adaptive learning rates).
  • Exploring different optimizers and their characteristics.
  • Techniques for handling learning rate tuning in distributed training.
• Topic 6.3: Handling Imbalanced Datasets and Robust Training:
  • Strategies for training on imbalanced data: Loss weighting, oversampling, undersampling.
  • Techniques for improving model generalization and robustness.
• Topic 6.4: Advanced Callbacks and Customizations:
  • Building complex, reusable callbacks for specific training scenarios.
  • Extending Lightning's functionality through custom components.
• Topic 6.5: Staying Up-to-Date with PyTorch Lightning:
  • Understanding the release cycle and new features.
  • Best practices for migrating to newer versions of Lightning.
• Hands-on Exercises: Profiling a training run and identifying areas for optimization, implementing a custom learning rate scheduler callback, experimenting with techniques for handling imbalanced data.

Module 7: Project Work and Implementation (Week 9)

• Learners will work on individual or group projects applying the advanced PyTorch Lightning concepts learned in the course.
• Project ideas could include:
  • Scaling the training of a large language model or diffusion model using DeepSpeed or FSDP.
  • Developing a custom Lightning Flash application for a specific domain.
  • Building a complex training pipeline with custom callbacks and loggers.
  • Implementing and evaluating different distributed training strategies for a given task.
  • Optimizing the training performance of an existing PyTorch model using PyTorch Lightning features.
• Guidance and mentorship will be provided by the instructor.

Module 8: Project Presentations and Future Directions (Week 10)

• Topic 8.1: Project Presentations:
  • Learners present their projects, methodologies, and findings.
• Topic 8.2: The Future of Scalable Deep Learning and PyTorch Lightning:
  • Emerging trends in distributed training and hardware.
  • Potential future directions for PyTorch Lightning and its ecosystem.
• Topic 8.3: Resources for Continued Learning:
  • Recommended resources for staying up-to-date with PyTorch Lightning and related technologies.
  • Engaging with the PyTorch Lightning community.

Assessment:

• Hands-on exercises throughout the modules.
• Potentially, short quizzes or coding assignments to assess understanding of key concepts.
• A final project demonstrating the ability to apply advanced PyTorch Lightning techniques.
• Active participation in discussions and project feedback.

Key Pedagogical Considerations:

• Building on Prior Knowledge: The curriculum assumes a strong foundation from the previous course.
• Hands-on and Practical: Emphasis on coding exercises and real-world application.
• Focus on Latest Technologies: Incorporating the most recent features and best practices in PyTorch Lightning, DeepSpeed, and FSDP.
• Problem-Solving and Debugging: Equipping learners with the skills to troubleshoot and optimize complex training setups.
• Community and Collaboration: Encouraging interaction and knowledge sharing among learners.
• Real-World Relevance: Connecting the concepts to practical challenges in scaling and optimizing deep learning models.