During large-scale training your GPU memory runs out How can you optimize model parallelism

Question

Can i know During large-scale training, your GPU memory runs out. How can you optimize model parallelism?

Answer 1

Optimize model parallelism by using pipeline parallelism, tensor parallelism, gradient checkpointing, and mixed precision training to reduce GPU memory usage.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Pipeline Parallelism:
  • Splits the model across multiple GPUs to distribute memory usage efficiently.
• Tensor Parallelism:
  • Partitions individual tensors across GPUs to reduce per-GPU memory requirements.
• Gradient Checkpointing:
  • Saves intermediate activations instead of recomputing them, reducing memory load.
• Mixed Precision Training (FP16):
  • Uses half-precision floating points, significantly lowering GPU memory usage.
• ZeRO Optimization (DeepSpeed):
  • Reduces redundant memory copies for gradients and optimizer states.

Hence, by leveraging pipeline parallelism, tensor parallelism, gradient checkpointing, and mixed precision training, large-scale models can be trained efficiently without running out of GPU memory.