Pyscheduler

A C++ library that embeds a single Python interpreter, providing thread‑pooled, asynchronous, and synchronous invocation of Python functions from C++ with optimized GIL usage.

Optimized as an execution engine for Machine Learning pipelines.

Table of Contents

• Features
• Requirements
• Installation

Features

• Thread Safe Implementation Ensures only one Python interpreter is ever initialized per process for pre python3.13.
  • Pre Python 3.13
  • Python 3.13+ no-gil sub interpreter
• Thread‑Pooled Execution
  Uses a high‑performance round robin queue to minimize latency during high throughput workloads
• Optimized GIL Management
  Acquires/releases the Global Interpreter Lock only around actual Python execution.
• Synchronous & Asynchronous APIs
  Easily call Python functions synchronously or schedule them with callbacks returning std::future.
• Opportunistic Batching Batches similar workloads together to minimize up-call latencies into Python.

Requirements

System Dependencies

• CMake ≥ 3.14
• C++17 (or later)
• Python ≥ 3.x development headers
• CUDAToolkit

Package Dependencies

• pybind11
• moodycamel::BlockingConcurrentQueue
• dmlc::dlpack

Can be installed ob Ubuntu using sudo apt install pybind11-dev libconcurrentqueue-dev libdlpack-dev

Installation

1. Initialize submodules git submodule update --init --recursive
2. We provide a simple configure.sh script to invoke CMake configuration. Use the -h | --help flag to see the possible options.

# Configure a Release build, including examples
./configure.sh -t Release -e

3. Install sudo cmake --install build
4. Uninstall sudo xargs rm < build/install_manifest.txt

The configure script also supports custom install prefix. You can either use the -p | --prefix flag, or specify the PYTHON_UDL_INTERFACE_PREFIX environment variable.