Fading Coder

One Final Commit for the Last Sprint

Analyzing PyTorch GPU Memory Usage with Snapshot Tools

GPU memory snapshot tools in PyTorch enable detailed analysis of memory allocation and deallocation events during model execution. These tools help diagnose common issues such as out-of-memory (OOM) errors and provide insights into memory consumption patterns. Core Functions PyTorch provides interna...

Implementing mean Average Precision (mAP) for Object Detection in PyTorch

mAP (mean Average Precision) is a standard metric for evaluating object detectors. It summarizes precision–recall tradeoffs across cateegories, optionally averaged over multiple IoU thresholds. Precision = TP / (TP + FP) Recall = TP / (TP + FN) AP (per class) = area under the precision–recall curve...

Building PyTorch with CUDA Support for Legacy GPUs on Windows

PyTorch binaries after 1.3 dropped support for GPUs with compute capability 3.5 and below, and by 1.7 the prebuilt wheels target compute capability 5.2 or higher. If you have an older GPU (for example, a Kepler device like GT 730M with CC 3.5) and still want GPU acceleration, you can compile PyTorch...