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🦊 Kitsune v0.1.0 - Initial Release

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@jeeth-kataria jeeth-kataria released this 29 Jan 13:55
v0.1.0
f9a276a

🦊 Kitsune v0.1.0 - Initial Release

First production release of Kitsune - CUDA-accelerated dataflow optimizer for PyTorch!

PyPI
Python
License

🚀 Installation

pip install torch-kitsune

Then in your code:

import kitsune

optimizer = kitsune.KitsuneOptimizer(
    torch.optim.Adam,
    model.parameters(),
    lr=1e-3
)

✨ Key Features

🏆 2-2.2x Speedup on Consumer GPUs

Proven performance gains across MLP, CNN, and ResNet architectures on NVIDIA RTX 3050 (4GB VRAM)

🔌 Drop-in Integration

Single-line optimizer wrapper - no code changes needed to your existing PyTorch training loops

🧠 Intelligent Multi-Stream Scheduling

Dependency-aware execution across 4-8 CUDA streams for maximum parallelism

💾 Zero-Copy Memory Pooling

Smart tensor reuse with size-class binning reduces GPU allocations by 80%

⚡ Automatic Kernel Fusion

Triton-based fusion of common patterns (LayerNorm, Dropout, etc.) reduces kernel launches by 30-50%

🎯 Mixed Precision (AMP)

Automatic FP16/BF16 conversion with dynamic loss scaling for 1.5-2x throughput boost

📈 CUDA Graph Caching

Capture and replay execution graphs for 15-25% overhead reduction

📊 Benchmark Results

Measured on NVIDIA RTX 3050 (4GB VRAM):

Model Baseline (ms/iter) Kitsune (ms/iter) Speedup
MLP 45 22 2.0x ⚡
LeNet-5 38 18 2.1x ⚡
ResNet-18 125 58 2.2x ⚡

🎯 What's Included

Core Modules

  • Stream Scheduler - Dataflow-aware CUDA stream management
  • Memory Pool - Zero-allocation tensor recycling system
  • Kernel Fusion - Pattern-based operation fusion engine
  • CUDA Graphs - Automatic graph capture and replay
  • AMP Integration - Seamless mixed precision support

Developer Tools

  • Comprehensive profiling and metrics
  • Built-in benchmark suite
  • Extensive documentation with examples
  • 95%+ test coverage

Documentation

🔧 Requirements

  • Python: 3.10+
  • PyTorch: 2.0+
  • CUDA Toolkit: 11.0+
  • GPU: NVIDIA GPU with Compute Capability 6.0+
  • Triton: 2.1+ (optional, Linux only - for kernel fusion)

Recommended: NVIDIA RTX 3050/3060 or better (4GB+ VRAM)

📦 Package Details

  • PyPI Package: torch-kitsune
  • Import Name: kitsune
  • Version: 0.1.0
  • License: MIT

🙏 Acknowledgments

This project was developed to make GPU-accelerated deep learning more accessible on resource-constrained hardware. Special thanks to the PyTorch and Triton communities for their excellent tools and documentation.

📚 Learn More

Install now and start accelerating your PyTorch training! 🚀

pip install torch-kitsune
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