1. Performance Optimization: DeepSpeed focuses on optimizing the performance of deep learning models by providing capabilities such as model parallelism, gradient compression, and fused kernels, whereas Deepo mainly focuses on providing pre-built Docker containers for various deep learning frameworks and tools.

2. Model Parallelism Support: DeepSpeed supports model parallelism by allowing the efficient parallel processing of large models across multiple GPUs, while Deepo does not provide specific support for model parallelism but rather offers containers for easy deployment of deep learning frameworks.

3. Training Speed Improvement: DeepSpeed includes optimizations like zero Redundancy optimizer, memory-efficient execution, and dynamic loss scaling to improve the speed of training large models, which may not be directly provided by Deepo's containers.

4. Research-Oriented tool: DeepSpeed is more research-oriented and includes features like ZeRO (Zero Redundancy Optimizer) and 1-bit LAMB, which are not part of the core offerings of Deepo that mainly focuses on providing production-ready environments.

5. Community Support and Documentation: DeepSpeed has a relatively larger community and extensive documentation to support users in optimizing their deep learning models for performance, compared to Deepo, which may have fewer resources available for troubleshooting and optimization.

In Summary, DeepSpeed excels in performance optimization with a focus on model parallelism support and training speed improvement, while Deepo provides pre-built Docker containers with less emphasis on performance optimization strategies.