1) Data Parallelism and Model Parallelism:

•   Data Parallelism: Split the training data across multiple GPUs/TPUs, where each device processes a different batch of data simultaneously. Gradients are then averaged and synchronized across all devices.
• Model Parallelism: Split the model itself across multiple devices, with each handling different layers or sections of the model. This approach is essential for very large models that can’t fit into a single device’s memory.

2) Efficient Communication and Mixed Precision Training:

• Efficient Communication: Utilize high-bandwidth interconnects (e.g., NVLink, InfiniBand) to reduce communication overhead. Optimize gradient synchronization using algorithms like AllReduce, and overlap communication with computation to minimize delays.
• Mixed Precision Training: Implement half-precision floating-point numbers to reduce memory usage and increase computational speed. Use loss scaling techniques to maintain numerical stability during training.

3) Gradient Accumulation and Checkpointing:

• Gradient Accumulation: Accumulate gradients over multiple mini-batches to effectively simulate larger batch sizes, which is particularly useful when memory resources are limited.
• Checkpointing and Fault Tolerance: Regularly save model checkpoints to ensure progress isn’t lost in case of failures. Implement robust recovery mechanisms to continue training seamlessly after interruptions.