deepspeed

Reduce GPU memory footprint for large models using ZeRO stages 1-3 memory optimization. This technique partitions optimizer states, gradients, and parameters across multi-GPU and multi-node environments to enable training models with over 100 billion parameters.

Set up multi-GPU and multi-node training by initializing your model with a distributed launcher and configuring parallelism strategies. This leverages pipeline and tensor parallelism to efficiently utilize multi-GPU environments for large-scale model training.

Accelerate inference and use mixed precision with PyTorch and torchvision models through optimized inference engines with kernel injection. This provides robust support for mixed precision to speed up large model inference.

Parallelism strategies available for training extremely large deep learning models include pipeline, tensor, and sequence parallelism. These techniques efficiently leverage multi-GPU and multi-node environments to scale models with over 100 billion parameters.

You need torch and ninja installed to optimize distributed memory management. These dependencies are required alongside mmpi and torchvision to execute custom kernels and support advanced parallelism techniques for large-scale training.