gpu-quality-priority

GPU-first quality-preserving processing for immunofluorescence images uses CUDA-backed operations on multi-GPU NVIDIA systems. This Skill enforces GPU-only execution through CuPy, applying production-default parameters across cycle processing, illumination correction, stitching, deconvolution, and segmentation without CPU fallback or fast-mode shortcuts.

Yes. This Skill enables high-throughput batch processing on multi-GPU NVIDIA systems by enforcing explicit device_id selection, ensuring consistent GPU-backed operation. It removes CPU options entirely and prevents quality degradation through validated parameter defaults across all processing stages.

The Skill fails fast with a clear error rather than falling back to CPU execution. GPU availability is a hard requirement; no fallback modes or CPU alternatives are provided, ensuring GPU-first workflows are never compromised.

This Skill prioritizes quality over speed by enforcing GPU-only execution, removing all CPU options, and locking production-default parameters. Unlike hybrid pipelines that offer CPU fallbacks or speed-quality trade-offs, it guarantees consistent quality through GPU acceleration without compromise.

The Skill covers cycle processing, illumination correction, stitching, deconvolution, and segmentation for multiplex immunofluorescence analysis. All stages run on GPU with CuPy, maintaining validated quality defaults throughout the pipeline.

Yes. You must ensure GPU is enabled, a GPU device is available, and you provide explicit device_id selection. The Skill removes fast-mode and CPU options, so existing pipelines using those features require reconfiguration to enforce GPU-only operation.