Dontopedia

Profiling Block

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Profiling Block has 8 facts recorded in Dontopedia across 1 reference, with 1 live disagreement.

8 facts·7 predicates·1 sources·1 in dispute

Mostly:has parameter(2), rdf:type(1), has parameter value(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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.

containedInContained in(1)

containsContains(1)

precededByPreceded by(1)

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.

8 facts
PredicateValueRef
Has Parameterrecord_shapes[1]
Has Parameteruse_cuda[1]
Rdf:typeContext Manager[1]
Has Parameter Valuetrue[1]
Assigned toProf[1]
Contained inModel Training Code[1]
InvokesProfile[1]
ContainsRecord Function Block[1]

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.

typebeam/80e4b051-0931-49af-8359-38149d7a6361
ex:ContextManager
hasParameterbeam/80e4b051-0931-49af-8359-38149d7a6361
record_shapes
hasParameterValuebeam/80e4b051-0931-49af-8359-38149d7a6361
true
hasParameterbeam/80e4b051-0931-49af-8359-38149d7a6361
use_cuda
assignedTobeam/80e4b051-0931-49af-8359-38149d7a6361
ex:prof
containedInbeam/80e4b051-0931-49af-8359-38149d7a6361
ex:model-training-code
invokesbeam/80e4b051-0931-49af-8359-38149d7a6361
ex:profile
containsbeam/80e4b051-0931-49af-8359-38149d7a6361
ex:record-function-block

References (1)

1 references
  1. ctx:claims/beam/80e4b051-0931-49af-8359-38149d7a6361
    • full textbeam-chunk
      text/plain1 KBdoc:beam/80e4b051-0931-49af-8359-38149d7a6361
      Show excerpt
      with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us

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