batchi

batchi is a lightweight dynamic batching server designed to optimize deep learning model inference. It provides a user-friendly interface to configure batching parameters, manage models, and monitor real-time analytics. After launching, batchi will accumulate incoming requests and batch them according to the configured batch size and timeout settings, delivering improved throughput with minimal latency impact. The system is designed for users who want to streamline model serving without writing custom batching logic.

To use batchi, start the MCP server and access the built-in configuration wizard or command-line tools to load your model, set batching constraints (such as maximum batch size and maximum wait time), and allocate system resources. The real-time analytics dashboard will display throughput, latency distributions, and resource utilization, helping you tune performance. If you need to extend functionality, batchi supports Python-based customization so you can implement custom pre/post-processing steps or integrate with existing inference pipelines.

Prerequisites:

• Python 3.6 or newer
• Access to install Python packages (pip)
• Optional: virtual environment tools (venv, virtualenv)

Install steps:

1. Ensure Python is installed and accessible from your system path.
2. Optional: Create and activate a virtual environment:
   • Python 3.x: python -m venv env
   • Windows: .\env\Scripts\activate
   • macOS/Linux: source env/bin/activate
3. Install batchi. If published on PyPI, use:
   • pip install batchi If you’re using a GitHub-hosted version, clone the repository and install locally:
   • git clone https://github.com/kinpatchii/batchi.git
   • cd batchi
   • pip install -e .
4. Start the server:
   • python -m batchi
5. Verify the server is running by visiting the dashboard URL or by checking logs for startup messages.

Note: If you plan to customize, ensure you have the repository checked out and install any required development dependencies as listed in the repository’s requirements.txt or pyproject.toml.

Tips and caveats:

• Environment: Ensure sufficient RAM (batching trades memory usage). Start with conservative batch sizes and widen as you observe latency.
• Logging: Set BATCHI_LOG_LEVEL to DEBUG temporarily if you need deeper troubleshooting.
• Configuration: Use the integrated wizard to set model paths, batching parameters, and resource allocations. Documented defaults may be conservative; adjust based on workload.
• Troubleshooting: If the server fails to start, verify Python version compatibility and that dependencies are installed. Check for port conflicts if the dashboard is not reachable.
• Security: If exposing the dashboard externally, implement authentication and restrict access to trusted networks.
• Compatibility: batchi is designed to work with common inference frameworks; verify your specific model runtime is compatible and that any required pre/post-processing steps are implemented in your pipeline.