Release v0.3.0

[0.3.0] - 2026-01-30

Added

Hardware-Specific Backends 🚀

• T4 Optimizer (kitsune/backends/t4_optimizer.py): Optimized for Tesla T4 GPUs

  • INT8 quantization support (61 TOPS)
  • FP16 mixed precision (65 TFLOPS)
  • JIT trace → freeze → optimize_for_inference pipeline
  • Achieved 4.06x speedup on Google Colab T4

• Apple Silicon Optimizer (kitsune/backends/apple_optimizer.py): Native M1/M2/M3 support

  • MPS backend with channels-last memory format
  • Chip detection (M1/M2/M3/M4)
  • CoreML integration for Neural Engine
  • Achieved 45x speedup on M1 Pro

• RTX Optimizer (kitsune/backends/rtx_optimizer.py): For RTX 30xx/40xx GPUs

  • TF32 tensor core acceleration
  • FP8 support for RTX 40 series
  • Sparsity optimizations
  • CUDA graphs for repeated patterns

• Backend Selector (kitsune/backends/backend_selector.py): Auto hardware detection

  • detect_platform(): Identifies T4, RTX, Apple Silicon
  • get_optimal_backend(): Returns best optimizer
  • auto_optimize(): One-line optimization API

Platform Test Suite

• benchmarks/platform_tests/test_t4.py: Comprehensive T4 benchmark (Colab-ready)
• benchmarks/platform_tests/test_apple.py: Apple Silicon benchmark
• benchmarks/platform_tests/test_rtx.py: RTX GPU benchmark

Performance Results

Platform	Model	Speedup
T4 (Colab)	ResNet-50	4.06x
Apple M1 Pro	MobileNetV3	45.7x
Apple M1 Pro	ResNet-50	34.7x
Apple M1 Pro	ResNet-18	21.9x

Fixed

• JIT operation order: trace → freeze → optimize_for_inference (was causing errors)
• Deprecated torch.cuda.amp.autocast → torch.amp.autocast('cuda', ...)

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