RuntimeError: No CUDA GPUs are available and ValueError A user named Stormpie reported on a forum that their pottery dating machine 'terraID' fails with 'RuntimeError: No CUDA GPUs are available' when clicking the 'Analyze' button, despite the app running on CPU at startup. The application log shows a corrupted classifier file 'museum_classifier_v5.pt' was detected and retrained, achieving 45.7% validation accuracy on 490 memory examples, and a subsequent 'ValueError: Invalid file descriptor: -1' occurred during shutdown. Hello everyone, this is my first post on this forum. I made a pottery dating machine. The space is called ‘terraID’. However, whenever I click the ‘Analyze’ button, I keep getting the error that there are No CUDA GPUs are available. I tried everything yes, import Spaces is the first line of the app.py . Is there anyone who knows how this problem can be solved?There is also a ValueError that I can’t quite figure out. My username is ‘Stormpie’. Thanks in advance for the help ===== Application Startup at 2026-09-07 08:25:26 ===== notice To update, run: pip install --upgrade pip Device at startup: CPU CLIP: lazy-loaded on ZeroGPU request Imported 490 memory examples from museum memory.json FAISS memory rebuilt: 490 vectors Could not load classifier: PytorchStreamReader failed reading zip archive: unsupported multidisk archive. This is an internal miniz error. If you are seeing this error, there is a high likelihood that your checkpoint file is corrupted. This can happen if the checkpoint was not saved properly, was transferred incorrectly, or the file was modified after saving. Corrupt classifier moved to: museum classifier v5.pt.corrupt Training classifier from existing memory… Epoch 001/60 val=11.7% Epoch 010/60 val=21.3% Epoch 020/60 val=27.7% Epoch 030/60 val=30.9% Epoch 040/60 val=35.1% Epoch 050/60 val=40.4% Epoch 060/60 val=44.7% Classifier checkpoint saved: museum classifier v5.pt Classifier getraind. Validation accuracy: 45.7%. Training: 396, validation: 94. Totaal: 490 voorbeelden 1. ca. 5300-2000 v.Chr. — 64 2. 2000-0 v.Chr — 73 3. 0-400 n.Chr — 79 4. 400-1200 n.Chr — 73 5. 1200-1500 n.Chr — 35 6. 1500-1700 n.Chr — 73 7. 1700-1899 n.Chr — 52 8. 1900-nu n.Chr — 41 FAISS vectors: 490 Validation accuracy: 45.7% Running on local URL: http://0.0.0.0:7860 http://0.0.0.0:7860 , with SSR Node proxy → Python :7861 Exception ignored in: