Torch.save
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Torch.save has 10 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(3), parameter(2), serializes to(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Function[3]sourceall time · C1be541d D993 4ec7 8f83 600f374f3493
- Py Torch Function[2]all time · 343d7abc 9aa0 4e2b 8884 910c760bfe88
- Serialization Function[1]all time · Ce394f12 8ac0 426e A183 A35c685c72ce
Parameterin disputeparameter
- Filename Argument[2]sourceall time · 343d7abc 9aa0 4e2b 8884 910c760bfe88
- State Dict Argument[2]sourceall time · 343d7abc 9aa0 4e2b 8884 910c760bfe88
Serializes toserializesTo
Savessaves
- Dictionary[3]sourceall time · C1be541d D993 4ec7 8f83 600f374f3493
File OutputfileOutput
- Model V{version}.pth[1]sourceall time · Ce394f12 8ac0 426e A183 A35c685c72ce
Serializes DictserializesDict
- Version Model Optimizer Dict[1]all time · Ce394f12 8ac0 426e A183 A35c685c72ce
Requires Three ArgumentsrequiresThreeArguments
- Version Model Optimizer[1]all time · Ce394f12 8ac0 426e A183 A35c685c72ce
Inbound mentions (2)
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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.
References (3)
- custom
ctx:claims/beam/ce394f12-8ac0-426e-a183-a35c685c72ce- full textbeam-chunktext/plain1 KB
doc:beam/ce394f12-8ac0-426e-a183-a35c685c72ceShow excerpt
This approach ensures that your versioning and rollback strategies work correctly, providing a reliable mechanism to handle model updates and potential errors. [Turn 9100] User: I'm trying to implement the versioning logic for my 90,000 mo…
- custom
ctx:claims/beam/343d7abc-9aa0-4e2b-8884-910c760bfe88- full textbeam-chunktext/plain1 KB
doc:beam/343d7abc-9aa0-4e2b-8884-910c760bfe88Show excerpt
self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # Initialize the model and optimizer model = MyModel() opt…
- custom
ctx:claims/beam/c1be541d-d993-4ec7-8f83-600f374f3493- full textbeam-chunktext/plain1 KB
doc:beam/c1be541d-d993-4ec7-8f83-600f374f3493Show excerpt
- Use `nvidia-smi` to monitor GPU usage and ensure that the GPU is being utilized effectively. - Example command: `nvidia-smi --loop-ms=1000 --format=csv,noheader,nounits --query-gpu=index,name,utilization.gpu,memory.total,memory.used,m…
See also
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