superfastml

SuperfastML is an open-source machine learning library designed to enable fast training and efficient deployment across CPU, GPU, and accelerator hardware. It provides a Python API with a high-performance C++ core and employs a just-in-time compiler to optimize core tensor operations. The project emphasizes low-latency inference and memory efficiency, aiming to complement existing frameworks rather than replace them in all workflows.

History and governance: SuperfastML was initiated in 2021 by researchers and engineers from multiple institutions. It is maintained as an open-source project on GitHub under the Apache 2.0 license. Development follows a community-driven model with periodic releases, feature proposals, and benchmarks published by the maintainers.

Architecture and features: The library includes a tensor library, a graph optimizer, and a runtime that supports both eager and graph execution. Core features include fused kernels, automatic mixed precision, and distributed data parallel training across multiple GPUs. It provides interoperability adapters to import models from PyTorch and TensorFlow, plus a model zoo and data utilities for preprocessing and loading.

Performance and use: Benchmarks in the official documentation report speedups on matrix multiplications, convolutions, and other compute-heavy operations on modern hardware, with reduced memory footprint in large models. Real-world results depend on model architecture and hardware. The project is used in academic experiments and in enterprise prototypes, with ongoing community contributions.

Licensing and availability: SuperfastML is released under the Apache 2.0 license with source code hosted on GitHub. Documentation and tutorials are provided by the project, along with example notebooks and a contribution guide.