DeepSpeed vs MegEngine
DeepSpeed
deepspeed.aiMegEngine
megengine.org.cnSide by side
| Figure | DeepSpeed | MegEngine |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.0 (#12) | 7.9 (#15) |
| Integrations | The site lists integrations with Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML. | MegFile provides Python file interfaces for S3, HTTP and local files. |
| Intended users | The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference. | The official site presents tutorials for beginners and advanced developers and describes the framework as supporting model development through deployment. |
| Purpose | DeepSpeed is a deep learning optimization library for distributed model training and inference. | MegEngine is a fast, scalable deep learning framework with automatic differentiation. |
| Support | The GitHub repository says DeepSpeed holds public office hours on the last Tuesday of each month. | The project lists GitHub issues, a forum, QQ group and [email protected] for contact. |
Plans
In their own words
MegEngine
MegEngine is a free, open-source deep learning framework for developing and deploying models, with automatic differentiation. It uses one model for training and inference, including quantization and dynamic shapes. The project says enabling DTR can reduce GPU memory use to one-third of the original. Inference is supported across x86, Arm, CUDA, and ROCm; GPU use requires compatible device drivers. Python packages are listed for 64-bit Linux and Windows, macOS 10.14 or later, and Android 7 or later, though macOS and Android installations are CPU-only. The installation guide specifies Python 3.6–3.9. MegEngine Lite supplies C/C++, Rust, and Python runtimes for deployment. MgeConvert handles conversion between MegEngine and third-party model formats, MegFile provides Python file interfaces for S3, HTTP, and local files, and MegFlow is a streaming computation framework for AI applications. Tutorials address beginners and advanced developers. Support options include GitHub issues, a forum, a QQ group, and email.