CUDA Out of Memory

• A CUDA Out of Memory Error in PyTorch occurs when your GPU doesn't have enough memory to allocate for the tensors, models, or data being processed.
• This typically happens when working with large models or batch sizes that exceed the GPU's memory capacity.

Strategies

1. Batch Size Adjustment

   • Reduce the batch size to fit the available GPU memory.
   • Smaller batches use less memory, allowing the model to process without exceeding the GPU's capacity.
   • Trade-off: Smaller batch sizes may lead to noisier gradients and slower convergence.

2. Gradient Accumulation

   • Split a large batch into smaller "micro-batches" that fit into memory.
   • Perform forward and backward passes for each micro-batch, accumulating gradients.
   • Update the model's weights after accumulating gradients for the desired batch size.
   • This allows training with effectively larger batch sizes without exceeding memory limits.

3. Model Simplification

   • Reduce the size of the model by using fewer layers or smaller layer dimensions.
   • Use model pruning or quantization to make the model lighter.
   • Consider swapping to more memory-efficient architectures or using pre-trained models with fewer parameters.

4. Data Preprocessing

   • Resize or crop input images to smaller dimensions to reduce memory usage.
   • Normalize and optimize data loading pipelines to minimize GPU workload.
   • Use mixed precision training (with torch.cuda.amp) to reduce the memory footprint of computations.

5. Memory Cleanup

   • Explicitly free unused GPU memory using torch.cuda.empty_cache().
   • Ensure no unnecessary tensors are being stored in memory by removing references.
   • Use checkpointing to save intermediate activations and recompute them during the backward pass, saving memory at the cost of additional computation.