A multi-language machine learning library ecosystem built for performance, extensibility, and educational clarity.
rslearn-lib is a GitHub organization dedicated to building high-performance machine learning libraries across multiple programming languages. It currently focuses on:
- Python (reference implementation)
- C++ (high-performance core implementation)
- Rust (safe and memory-efficient implementation)
- Java (future expansion)
The goal is to provide consistent APIs and core functionality across all languages while leveraging each language’s strengths.
The Python implementation serves as the primary reference and development base. It is designed for:
- Rapid prototyping
- Research and experimentation
- Easy API design validation
- Educational usage
This version defines the core architecture that other language implementations follow.
All implementations in this ecosystem follow these principles:
- Consistent API design across languages
- Modular and extensible structure
- Minimal external dependencies
- Performance-aware implementations for core computations
- Clean separation of core, models, preprocessing, and utilities
Focused on:
- High-performance numerical computation
- Low-level memory control
- Production-grade ML pipelines
Focused on:
- Memory safety without garbage collection
- Concurrency-friendly design
- Reliable and secure ML components
Focused on:
- Cross-platform compatibility
- Enterprise-level ML applications
- JVM ecosystem integration
- Build a unified multi-language ML ecosystem
- Bridge the gap between learning and production ML systems
- Provide reference-grade implementations for educational use
- Explore performance tradeoffs across languages
All repositories under the rslearn-lib organization are licensed under the BSD License.
This allows free use, modification, and distribution with minimal restrictions while maintaining attribution.
This project is actively under development. APIs, structure, and modules may evolve over time as the ecosystem matures.
- ItzRustam
Contributions are welcome once the core architecture stabilizes. Guidelines will be provided in individual repositories.
Maintained by the rslearn-lib organization.