DeepSpeedExamples is a collection of reference implementations and scripts for training, fine-tuning, and executing inference on large-scale AI models using DeepSpeed optimization. It provides a distributed model training guide and practical workflows for adapting large language models through memory-efficient techniques.
The repository includes specialized implementations for pipeline parallelism to handle models exceeding single GPU memory and a suite of examples for ZeRO memory optimization to reduce per-device overhead. It also features standardized test suites for benchmarking the throughput and latency of models running on DeepSpeed inference engines.
The project covers broad capability areas including GPU memory optimization, distributed AI benchmarking, and high-performance model inference. It demonstrates the use of weight compression and distributed optimization to scale neural networks across multiple computing nodes.
