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🌐 dev.toSource
TL;DR After del tensor; torch.cuda.empty_cache(), PyTorch's caching allocator still...
🌐 dev.toSource
From Dev.to - machinelearning: MCP Shows What the Agent Did. eBPF Shows Why the GPU Stalled.
🌐 dev.toSource
From Dev Community: MCP Tools Are New API Surfaces. eBPF Sees What They Actually Touch.
🌐 dev.toSource
From Dev RSS Feed: A Cluster Stall Looks Healthy on Every Host. The Cause Is in the Pattern Across Hosts.
🌐 dev.toSource
From Dev.to - pytorch: CUDA Out of Memory at 60% Utilization: Tracing PyTorch GPU Memory Fragmentation
🌐 dev.toSource
TL;DR: A .cpu().numpy() call buried inside a forward pass was forcing a full CPU-GPU synchronization on every batch, every loop iteration. The GPU would finish its work in milliseconds, then sit idle for ~2 seconds waiting for Python and NumPy to catch up. Replacing the NumPy log
🌐 dev.toSource
TL;DR: PyTorch's DataLoader can be 50-124x slower than direct tensor indexing for in-memory GPU workloads. We reproduced a real PyTorch issue on an RTX 4090 and traced every CUDA API call and Linux kernel event to find the root cause. The GPU wasn't slow - it was starving. DataLo