TrainingRunScenario
Training scenarios · Implementation: mlsysim/engine/scenario_definitions/training.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
Training workload on fleet: peak time to delivered wall-clock time.
Notes
Equation: Compose mlsysim.solvers.TrainingRunModel.
Assumptions: The stated fields are inputs to mlsysim.solvers.TrainingRunModel; its documented validity regime, units, and modeling choices apply unchanged.
Excludes: This scenario adds no measurement, empirical calibration, or mechanisms beyond mlsysim.solvers.TrainingRunModel.
References
Derivation: this scenario binds its input fields to the referenced implementation contracts: mlsysim.solvers.TrainingRunModel. The methods below identify any additional arithmetic.
Examples
>>> from mlsysim import Scenarios
>>> case = Scenarios.Gpt3PretrainingFrontier8K
>>> type(case.result).__name__
'TrainingRunResult'Attributes
| Name | Description |
|---|---|
| fleet | |
| local_efficiency | |
| overheads | |
| precision | |
| result | Evaluate this scenario through mlsysim.solvers.TrainingRunModel.solve. Equation: |
| scaling_efficiency | |
| workload |
workload
Type: Workload.
Declared default: required.
fleet
Type: Fleet.
Declared default: required.
precision
Type: str.
Declared default: 'fp8'.
local_efficiency
Type: float.
Declared default: required.
scaling_efficiency
Type: float.
Declared default: required.
overheads
Type: Dict[str, float].
Declared default: required.
result
engine.scenario_types.TrainingRunScenario.result
Evaluate this scenario through mlsysim.solvers.TrainingRunModel.solve. Equation: pass the stated fields to that implementation; its contract defines the calculation and feasibility tests. Units: each returned field follows the referenced result schema; no independent equation is evaluated by this wrapper.