WinCore-v0.7.1

WinCore 0.7.1

WinCore 0.7.1 expands the library with new Windows, AI, PyTorch, memory, and CUDA-related capabilities, along with a number of reliability and build-system fixes.

Added

• OS-level CPU priority and CPU affinity controls.
• FP8 tensor compression and decompression with dynamic per-tensor scaling.
• WinCore.kv.StepCache for generic per-step tensor state, including append, sliding-window, replace, and optional FP8 compression.
• Windows working-set trimming through memory.trim_working_set().
• DataLoader RAM usage estimation through memory.estimate_worker_ram_multiplier().

Improved

• Native CUDA extension build and runtime handling.
• Ninja detection and diagnostics.
• Visual Studio and MSVC auto-detection, including newer and preview installations.
• CPU affinity selection on Intel hybrid CPUs.
• Cross-process DiskCache handling, eviction, LRU tracking, and cache-hit detection.
• Test coverage and fallback-path testing.
• Real Windows, CUDA, and MSVC verification of the compiled fused_bias_gelu kernel.

Fixed

• False Ninja-not-found errors when Ninja was installed but unavailable through the current process PATH.
• Stale CUDA kernel reuse during clean rebuilds on Windows.
• Temporary-file collisions in concurrent atomic_write() calls.
• Incorrect DiskCache byte accounting across multiple worker processes.
• Cache tests affected by changing filesystem usage during execution.
• Test-suite coverage gaps in environments without a real PyTorch installation.
• Documentation that overstated the hardware requirements of FP8 quantization.
• Unnecessary upstream MSVC environment warnings during native CUDA builds.

WinCore 0.7.1 is released for general use. Compatibility may still vary across Windows versions, hardware, drivers, CUDA Toolkit versions, MSVC toolsets, Python versions, and PyTorch builds.

For detailed API documentation, see:
https://github.com/FWKMultiverse/WinCore/blob/main/wincore/API_REFERENCE.md

Report issues:
https://github.com/FWKMultiverse/WinCore/issues