Loader
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Loader has 8 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:needs prefetching(2), rdf:type(2), needs multithreading(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
hasParameterHas Parameter(2)
- Train
ex:train - Train Model
ex:train_model
argumentArgument(1)
- Train Call
ex:train-call
enumeratesEnumerates(1)
- Train Model
ex:train_model
iteratesOverIterates Over(1)
- Batch Loop
ex:batchLoop
statesRequirementForStates Requirement for(1)
- Log Entry 2026 05 01 19 32
ex:log-entry-2026-05-01-19-32
usesDataLoaderUses Data Loader(1)
- Train Model
ex:train_model
Other facts (8)
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 |
|---|---|---|
| Needs Prefetching | true | [1] |
| Needs Prefetching | true | [2] |
| Rdf:type | Py Torch Data Loader | [3] |
| Rdf:type | Data Loader | [5] |
| Needs Multithreading | true | [1] |
| Needs Multithreaded Operation | true | [2] |
| Yields | (data, target) tuples | [4] |
| Iterated Over | train | [4] |
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 (5)
ctx:discord/blah/watt-activation/part-705ctx:discord/blah/watt-activation/702- full textwatt-activation-702text/plain3 KB
doc:agent/watt-activation-702/8ca0f2a3-b72b-46da-95b4-f4cb77d7241fShow excerpt
[2026-05-01 19:32] xenonfun: **TLDR: need multithreaded and prefetching in the loader** At step 110: still stable, BPB noisy but centered roughly mid-1s so far. Token rate has crept to ~4.9K tok/s after startup. It will checkpoint at step 2…
ctx:claims/beam/bd88fada-39be-4f23-92a8-bcf3186013bd- full textbeam-chunktext/plain1 KB
doc:beam/bd88fada-39be-4f23-92a8-bcf3186013bdShow excerpt
[Turn 8818] User: I'm trying to optimize the memory usage for my reranking model, and I've capped it at 1.9GB to reduce spikes by 20% for 11,000 queries. However, I'm not sure if this is the best approach. Can you review my code and suggest…
ctx:claims/beam/2323ffff-3db7-4aa4-aa6c-d68d1e67f614- full textbeam-chunktext/plain1 KB
doc:beam/2323ffff-3db7-4aa4-aa6c-d68d1e67f614Show excerpt
return len(self.data) def __getitem__(self, idx): data = self.data[idx] label = self.labels[idx] return data, label def train(model, device, loader, optimizer, epoch, scaler=None): model.train() …
ctx:claims/beam/71827c26-67ff-489a-bbff-8162b1676ef7
See also
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