rclib 0.0.4

High-performance, scalable Reservoir Computing framework in C++ with Python bindings.

rclib: Reservoir Computing Library

rclib is a high-performance, scalable, and general-purpose reservoir computing framework implemented in C++ with Python bindings. It is designed to handle both small-scale networks and very large-scale (40,000+ neurons) architectures, supporting deep (stacked) and parallel reservoir configurations.

Project Goals

• Performance: Core logic in C++17 using Eigen for linear algebra.
• Scalability: Efficient handling of large sparse reservoirs and complex architectures.
• Flexibility: Modular design separating Reservoirs and Readouts.
• Ease of Use: Pythonic interface via pybind11 and scikit-learn style API.
• Reproducibility: Deterministic results via explicit seeding of random reservoirs.

Getting Started

Prerequisites

• C++ Compiler: GCC, Clang, or MSVC supporting C++17.
• CMake: Version 3.15 or higher.
• Python: Version 3.10 or higher (for Python bindings).
• Build Tool: uv is recommended for managing the Python environment, but standard pip works too.
• OpenMP: Required for parallelization.
  • Ubuntu/Debian: sudo apt install libomp-dev

Building from Source

1. Clone the repository:

   git clone --recursive https://github.com/hrshtst/rclib.git
   cd rclib
   

   Note: The --recursive flag is crucial to fetch dependencies (Eigen, Catch2, pybind11) located in cpp_core/third_party. If you cloned without --recursive, run:

   git submodule update --init --recursive
   

2. Build C++ Core and Examples:

   # Build with examples enabled (defaults: Release type, Export Compile Commands ON)
   cmake -S . -B build -DBUILD_EXAMPLES=ON
   cmake --build build --config Release -j $(nproc)
   

3. Run a C++ Example:

   # Run the Mackey-Glass time series prediction example (if built with -DBUILD_EXAMPLES=ON)
   ./build/examples/cpp/mackey_glass
   

Using the Python Interface

This project provides Python bindings for the core C++ code, leveraging uv, scikit-build-core, and pybind11.

To enable fast incremental builds and automatic rebuilding when C++ source files change (see astral-sh/uv#13998), use the following two-step installation process:

# 1. Install build dependencies without installing the project
uv sync --no-install-project --only-group build

# 2. Install the project and remaining dependencies
uv sync

# Run the quick start example
uv run python examples/python/quick_start.py

With this configuration, any changes to the C++ source code in cpp_core will automatically trigger a rebuild of the Python extension module upon the next import, ensuring your Python environment always uses the latest C++ logic without manual recompilation.

Integrating rclib_core into Your C++ Project (CMake)

If you wish to use rclib_core as a static library in your own C++ project, the recommended approach is to add it as a Git submodule.

1. Add rclib as a Git Submodule: Navigate to your project's root directory and add rclib as a submodule:

   git submodule add https://github.com/hrshtst/rclib.git third_party/rclib
   git submodule update --init --recursive third_party/rclib
   

   (Adjust third_party/rclib to your desired path.)

2. Integrate with Your CMakeLists.txt: In your project's CMakeLists.txt file, add rclib as a subdirectory and link against its rclib_core target. Ensure you propagate relevant build options like OpenMP if needed.

   # Add rclib as a subdirectory
   add_subdirectory(third_party/rclib)
   
   # Example of how to link rclib_core to your target executable or library
   add_executable(my_rc_app main.cpp)
   target_link_libraries(my_rc_app PRIVATE rclib_core)
   
   # Note: rclib_core internally handles its Eigen dependency.
   # If your project directly uses Eigen, ensure it's properly configured in your CMakeLists.txt.
   

3. Configure Parallelization (Optional): If your project also uses OpenMP or needs to control rclib's parallelization, you can set the RCLIB_USE_OPENMP and RCLIB_ENABLE_EIGEN_PARALLELIZATION CMake options before calling add_subdirectory(third_party/rclib).

   set(RCLIB_USE_OPENMP ON CACHE BOOL "Enable OpenMP support in rclib_core")
   set(RCLIB_ENABLE_EIGEN_PARALLELIZATION OFF CACHE BOOL "Enable Eigen's internal parallelization in rclib_core")
   add_subdirectory(third_party/rclib)
   # ... rest of your project's CMakeLists.txt
   

Running Tests

Using Nox (Recommended)

nox automates environment setup and execution for both Python and C++.

# Run all default sessions (Lint, Type Check, Python Tests)
uv run nox

# Run Python tests only
uv run nox -s tests

# Run C++ tests only
uv run nox -s tests_cpp

Manual Execution (Step-by-Step)

If you prefer to run tests manually without nox:

C++ Tests

# 1. Configure and build
cmake -S . -B build -DBUILD_TESTING=ON
cmake --build build --config Release -j $(nproc)

# 2. Run tests
ctest --test-dir build --output-on-failure

Python Tests

# 1. Build and install the C++ extension in the current environment
cmake -S . -B build
cmake --build build --config Release -j $(nproc) --target _rclib

# 2. Run pytest
uv run pytest

Documentation

The project documentation is built using mkdocs and the Material theme. It includes theoretical background, user guides, and API references.

Using Nox (Recommended)

# Build the documentation
uv run nox -s docs

Manual Execution

If you prefer to run mkdocs directly:

# 1. Install documentation dependencies
uv sync --group docs

# 2. Build the documentation
uv run mkdocs build

# 3. Serve the documentation locally with live-reloading
uv run mkdocs serve

The documentation is automatically deployed to https://hrshtst.github.io/rclib/ on every push to the main branch.

Parallelization Configuration

rclib provides flexible options to control parallelization strategies, allowing you to optimize for your specific workload and hardware. This is managed via CMake options.

Options

Option	Default	Description
RCLIB_USE_OPENMP	ON	Enables OpenMP support. Required for any multi-threading.
RCLIB_ENABLE_EIGEN_PARALLELIZATION	OFF	Enables Eigen's internal parallelization (using OpenMP).

Recommended Configurations

1. User-Level Parallelism (Default)

Best for: Training multiple reservoirs, batch processing, or typical workloads.

• Configuration:

  cmake -S . -B build -DRCLIB_USE_OPENMP=ON -DRCLIB_ENABLE_EIGEN_PARALLELIZATION=OFF
  

• How it works: rclib uses OpenMP #pragma omp parallel for loops to parallelize high-level operations (e.g., updating state for multiple reservoirs in a parallel architecture, or processing batches). Eigen is forced to run in single-threaded mode to avoid oversubscription (too many threads competing for resources).

2. Eigen-Level Parallelism

Best for: Very large single networks or dense matrix operations where linear algebra is the bottleneck.

• Configuration:

  cmake -S . -B build -DRCLIB_USE_OPENMP=ON -DRCLIB_ENABLE_EIGEN_PARALLELIZATION=ON
  

• How it works: rclib disables its own OpenMP loops. Instead, it lets Eigen use the OpenMP thread pool to parallelize internal matrix operations (like large dense matrix multiplications). This is useful when the reservoir state size is huge.

3. Serial (Single-Threaded)

Best for: Debugging or systems without OpenMP.

• Configuration:

  cmake -S . -B build -DRCLIB_USE_OPENMP=OFF
  

Performance Benchmarking

The benchmarks/ directory contains scripts to evaluate performance across different thread counts and parallelization modes.

1. Run the Benchmark Suite:

   ./benchmarks/benchmark_parallel_comparison.sh
   

   This script compiles the project in different modes (Serial, User OMP, Eigen OMP) and runs the performance_benchmark executable multiple times.

2. Visualize Results:

   uv run python benchmarks/plot_parallel_comparison.py
   

   This generates plots comparing execution time and MSE for different methods and configurations.

Development

Code Quality Tools

The project uses several tools to ensure code quality, all of which are integrated into pre-commit and nox:

• Ruff: For Python linting and formatting.
• Basedpyright: For static type checking.
• clang-format: For C++ formatting (LLVM style).
• shellcheck: For shell script linting.
• cmake-format / cmake-lint: For CMake formatting and linting.
• pre-commit: To enforce checks before committing.

Automation with Nox

nox is used to automate various development tasks:

• uv run nox -s lint: Run linters.
• uv run nox -s type_check: Run type checkers.
• uv run nox -s tests: Run Python tests.
• uv run nox -s tests_cpp: Run C++ tests.
• uv run nox -s docs: Build documentation.

Setting up pre-commit

To ensure all code follows the project's style and quality standards, it is recommended to set up pre-commit:

uv run pre-commit install

AI Assistance & Development Workflow

This project is developed with the assistance of an AI coding agent using the Gemini CLI tool. The AI is also used to generate commit messages and parts of the documentation, including API and theoretical reference sections.

Workflow:

1. Context & Theory (Human): The maintainer, Hiroshi Atsuta, establishes the project roadmap in GEMINI.md and writes the theoretical background implemented as documentation in docs/theory/.
2. Implementation (AI): The AI assistant uses these documents and the constraints defined in GEMINI.md to implement code scaffolding, core logic, tests, and documentation.
3. Review & Revision (Human): The maintainer reviews, tests, and revises the generated code to ensure quality and correctness. This iterative cycle ensures high standards while leveraging AI efficiency.

Responsibility: All responsibilities for the code hosted in this repository lie with the maintainer. The AI serves strictly as an implementation assistant; final architectural decisions and code quality are human-led.

Feedback: If you identify problems, or find code that appears to be unoriginal or rights-protected, please notify the maintainer immediately by filing an issue.

Contributor Policy: External contributors are welcome to use AI tools for assistance, provided they adhere to the same standard of review and responsibility. If you use AI to generate code for a Pull Request, please disclose it in the PR description and ensure you have thoroughly reviewed and tested the code.

Acknowledgments

   

This project is supported by the MITOU Target Program (Reservoir Computing field) of the Information-technology Promotion Agency, Japan (IPA). Details of the supported project can be found in the official summary (Japanese).

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.