GPU availability check
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
GPU availability check has 6 facts recorded in Dontopedia across 2 references.
Mostly:purpose(1), function(1), rdf:type(1)
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| Predicate | Value | Ref |
|---|---|---|
| Purpose | check GPU availability | [1] |
| Function | torch.cuda.is_available | [1] |
| Rdf:type | Conditional Check | [1] |
| Precedes | Logging Config | [1] |
| Enables | Cuda Device Selection | [2] |
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References (2)
ctx:claims/beam/16ad261b-9fcf-4975-8708-5450c6d4ee02- full textbeam-chunktext/plain1 KB
doc:beam/16ad261b-9fcf-4975-8708-5450c6d4ee02Show excerpt
import json # Check if a GPU is available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(…
ctx:claims/beam/85ae2d49-1794-4084-81ec-929c41dddb99- full textbeam-chunktext/plain1 KB
doc:beam/85ae2d49-1794-4084-81ec-929c41dddb99Show excerpt
- If the loss oscillates or diverges, you might need to decrease the learning rate (e.g., \(0.0005\) or \(0.0001\)). 3. **Use Learning Rate Schedules**: - Implement learning rate schedules such as step decay, exponential decay, or co…
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