Torch Cuda Amp Import
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Torch Cuda Amp Import has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
5 facts·4 predicates·2 sources·1 in dispute
Mostly:imported class(2), rdfs:label(1), rdf:type(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedImported Classin disputeimported-class
- Autocast[1]all time · A38a0bc2 6ed2 4089 B908 741e1595c678
- Grad Scaler[1]all time · A38a0bc2 6ed2 4089 B908 741e1595c678
Rdfs:labelrdfs:label
- from torch.cuda.amp import GradScaler, autocast[1]sourceall time · A38a0bc2 6ed2 4089 B908 741e1595c678
Rdf:typerdf:type
- Import Statement[1]all time · A38a0bc2 6ed2 4089 B908 741e1595c678
Providesprovides
- Mixed Precision Functionality[2]sourceall time · 51a366c4 36ad 4c73 A8a6 A8071a33c62a
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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imported-classbeam/a38a0bc2-6ed2-4089-b908-741e1595c678
ex:autocast
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imported-classbeam/a38a0bc2-6ed2-4089-b908-741e1595c678
ex:GradScaler
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providesbeam/51a366c4-36ad-4c73-a8a6-a8071a33c62a
ex:mixed-precision-functionality
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labelbeam/a38a0bc2-6ed2-4089-b908-741e1595c678
from torch.cuda.amp import GradScaler, autocast
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typebeam/a38a0bc2-6ed2-4089-b908-741e1595c678
ex:Import-Statement
References (2)
2 references
- custom
ctx:claims/beam/a38a0bc2-6ed2-4089-b908-741e1595c678- full textbeam-chunktext/plain1 KB
doc:beam/a38a0bc2-6ed2-4089-b908-741e1595c678Show excerpt
### 6. Use `torch.cuda.empty_cache()` Periodically calling `torch.cuda.empty_cache()` can help free up unused memory on the GPU. ### 7. Use `torch.autograd.profiler` Profiling your code can help identify bottlenecks and areas where memory …
- custom
ctx:claims/beam/51a366c4-36ad-4c73-a8a6-a8071a33c62a- full textbeam-chunktext/plain1 KB
doc:beam/51a366c4-36ad-4c73-a8a6-a8071a33c62aShow excerpt
scaler.update() optimizer.zero_grad() # Example usage: train_model_with_amp(model, optimizer, dataloader, device, gradient_accumulation_steps=4) ``` 4. **Data Loading Efficiency:** - Use effici…
See also
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