autocast context manager
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
autocast context manager has 8 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:rdf:type(3), enables(2), is used for(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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occursWithinOccurs Within(1)
- Forward Pass
ex:forward-pass
requiresRequires(1)
- Forward Pass
ex:forward-pass
usedInUsed in(1)
- Mixed Precision
ex:mixed-precision
Other facts (7)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Context Manager | [2] |
| Rdf:type | Context Manager | [3] |
| Rdf:type | Mixed Precision Context | [4] |
| Enables | Mixed Precision Training | [2] |
| Enables | Mixed Precision Training | [4] |
| Is Used for | Automatic Mixed Precision Training | [1] |
| Purpose | Mixed Precision | [3] |
Timeline
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References (4)
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…
ctx:claims/beam/d722ad53-d442-458e-b561-cab7e12fcbbf- full textbeam-chunktext/plain1 KB
doc:beam/d722ad53-d442-458e-b561-cab7e12fcbbfShow excerpt
optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…
ctx:claims/beam/43e9fcd8-67ff-4a5a-a1bd-5302a703a02a- full textbeam-chunktext/plain1 KB
doc:beam/43e9fcd8-67ff-4a5a-a1bd-5302a703a02aShow excerpt
To profile your code and identify bottlenecks, you can use `torch.autograd.profiler`. Here's a quick example of how to profile your training loop: ```python from torch.autograd import profiler # Training loop with profiling for epoch in r…
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 …
See also
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