DeepSpeed(deepspeedai/deepspeed) is a distributed training library that splits large-scale model training across multiple devices. The repository's latest release is v0.19.8, written in Python and distributed under the Apache-2.0 license. What changed in this v0.19.8 is not stated in the given material.
The README describes DeepSpeed as a system optimization project for large-scale deep learning training. ZeRO is a method that shards the state needed for training across multiple devices to reduce GPU memory pressure, and ZeRO-Infinity widens the offloading scope to include CPU and NVMe SSD. 3D parallelism is introduced alongside Ulysses Sequence Parallelism and DeepSpeed-MoE, with DeepSpeed-MoE covering a Mixture of Experts architecture that merges the results split among several specialist models. The README states that these technologies have been used in training MT-530B and BLOOM. DeepSpeed is closer to training infrastructure that directly provides memory management, distributed execution, and communication optimization than to a model or fine-tuning interface.
What can be verified is limited. The material offers no metrics for gauging performance, and among the evaluation scores only Momentum is low, at 2.9 out of 10. The repository, created on January 23, 2020, has gathered 43,252 stars, but star count does not tell you how complete the release is. It is worth recording as a training infrastructure candidate, while the actual adoption decision should be deferred until release notes and performance data are published.