The PyTorch Foundation is excited to welcome DeepSpeed, a deep learning optimization library, as a PyTorch Foundation-hosted project. Contributed by Microsoft, DeepSpeed empowers developers to streamline distributed training and inference, making it easier to scale AI models efficiently while minimizing costs and operational complexity. Since inception, DeepSpeed has leveraged core PyTorch functionalities as the foundation for building deep learning features and optimizations.
The PyTorch Foundation recently announced its expansion to an umbrella foundation to accelerate AI innovation and is pleased to welcome DeepSpeed as one of the first new projects. Foundation-Hosted Projects are projects that fall under the umbrella, they are officially governed and administered under the PyTorch Foundation’s neutral and transparent governance model.Â
What is DeepSpeed?
DeepSpeed is designed to optimize deep learning workflows, providing a robust set of features that enhance the performance, scalability, and cost-efficiency of AI model training and deployment. It enables seamless scaling across thousands of GPUs while also optimizing resource utilization for constrained systems, addressing key technical challenges in AI development.
Key features of DeepSpeed include:
- Scalable Model Training: Supports dense and sparse Mixture-of-Experts (MoE) models with billions or trillions of parameters, scaling seamlessly across thousands of GPUs.
- Heterogeneous Hardware Support: Offers compatibility with diverse hardware platforms, including Nvidia, AMD, and Intel GPUs, Huawei Ascend NPU, and Intel Gaudi, ensuring flexibility and adaptability in deployment.
- Optimized Resource Use: Facilitates training and inference on systems with limited GPU capacity, maximizing hardware efficiency and increasing accessibility.
- Low-Latency Inference: Achieves minimal latency and high throughput for real-time model inference.
- Compression Capabilities: Reduces model size and inference latency, lowering costs for large-scale deployments without sacrificing performance.
Accelerating Open Source AI Together
DeepSpeed’s addition as a PyTorch Foundation strengthens the foundation’s mission to accelerate open source AI. By joining PyTorch Foundation, DeepSpeed gains access to a thriving ecosystem of open source projects, a global network of contributors, and robust technical and operational resources. This collaboration enables the DeepSpeed community to scale its efforts, enhance interoperability with other projects, and drive broader adoption of its optimization library. Additionally, PyTorch Foundation’s focus on open governance and community-driven development ensures that DeepSpeed’s growth aligns with the shared goals of transparency, inclusivity, and innovation in the AI space.
Learn more about DeepSpeed and how to get involved by visiting the DeepSpeed website.