Is DeepSpeed compatible with frameworks other than PyTorch?
DeepSpeed is primarily designed to work with PyTorch and offers deep integration with its APIs. While it may be possible to adapt parts of DeepSpeed for other frameworks, official support and optimizations are focused on PyTorch.
What hardware is required to use DeepSpeed effectively?
DeepSpeed is optimized for GPU-based distributed training, particularly NVIDIA GPUs with CUDA support. For best performance, multiple GPUs across one or more nodes are recommended, but it can also run on a single GPU for smaller models.
How does ZeRO optimization improve training?
ZeRO (Zero Redundancy Optimizer) partitions model states such as optimizer states, gradients, and parameters across GPUs to reduce memory duplication. This allows training much larger models than would otherwise fit in GPU memory.
Is DeepSpeed free to use?
Yes, DeepSpeed is an open-source project released under the MIT license, making it free to use for research and commercial purposes.