ZeRO-Infinity is a cutting-edge technology designed to help data scientists tackle larger and more complex machine learning projects. It is an extension of ZeRO, a sharded data parallel system that allows for parallel training of large models across multiple GPUs. However, what sets ZeRO-Infinity apart is its innovation in heterogeneous memory access, which includes the infinity offload engine and memory-centric tiling.
Infinity Offload Engine
One of the biggest challenges of training large models is the limited amount of memory that can be accessed by GPUs. ZeRO-Infinity addresses this problem by allowing the CPU and NVMe memory to be used in tandem with the GPU. The infinity offload engine makes it possible to offload memory from the GPU to the CPU or NVMe when needed, which means that larger models can be trained efficiently on resources that would previously have been limiting.
Memory-Centric Tiling
Another challenge associated with large models is the necessity for extremely large individual layers that may not fit within the GPU memory all at once. Memory-centric tiling is a technique that addresses this issue by dividing large layers into smaller tiles. Each tile is then processed in turn, with the results being sent back to the CPU for further computation. This technique allows for the processing of extremely large models that may have been impossible with other systems.
Benefits of ZeRO-Infinity
The benefits of ZeRO-Infinity are numerous for data scientists working on large machine learning models. These include:
- The ability to train larger models with limited GPU resources.
- Improved efficiency in processing, thanks to the offloading of memory to the CPU and NVMe.
- The ability to process extremely large individual layers with memory-centric tiling.
- Faster computation times, leading to more rapid conclusions and insights.
Overall, ZeRO-Infinity represents a significant leap forward in the field of machine learning, enabling data scientists to take on larger and more complex projects than ever before. It is a promising technology that is sure to have a significant impact on the world of data science in the years to come.
