DeepSpeed Examples is a collection of reference implementations demonstrating how to use DeepSpeed for training, inference, compression, and benchmarking of machine learning models.
The repository addresses the challenge of understanding how to apply DeepSpeed's optimization library to real-world machine learning workflows. It provides end-to-end examples across multiple use cases: applications that combine training and inference of cutting-edge models, training and finetuning examples with individual instructions for each, inference examples using both DeepSpeed-MII with DeepSpeed-FastGen and Huggingface integrations, model compression techniques, and performance benchmarks. By organizing examples by task type, the repository helps developers see concrete implementations rather than abstract API documentation.
Developers should use this repository when learning DeepSpeed or when building systems that require distributed training, optimized inference, or model compression. It suits projects ranging from research prototypes to production applications that need to scale large models efficiently. The repository is most valuable for those already committed to using DeepSpeed and seeking practical guidance on implementation patterns.
The project maintains active integration testing through automated build pipelines. The repository welcomes external contributions under a standard contributor license agreement and has adopted an open source code of conduct, indicating structured governance for community participation.
