mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-07-16 23:24:55 -05:00
02c37be63a
Move the simulator out of core/ into a new engine/ package: solver, engine,
evaluation, results, pipeline, dse, walls, resolver_factory, explainers,
config, calibration, and the evaluatable scenarios bundle. core/ now holds
only primitives (units, types, provenance, registry, validation, exceptions).
No core primitive imports an engine module; the dependency runs one way.
Also relocate the appendix_lineage audit gate (a used lineage auditor, NOT
vestigial — kept, not deleted) from core/ to tools/, beside its consumer
audit_provenance.py.
Consumers repointed mlsysim.core.<mod> -> mlsysim.engine.<mod> (and the
from-core-import-calibration form): package internals (cli, sim, solvers,
viz), tests, runnable examples, paper scripts, and 9 book chapters. Pure
reorg, zero functional change.
482 passed; import clean (no cycles); vol1 (engine.calibration) and vol2
(engine.solver) chapters render with 0 {python} leaks, 0 tracebacks.
Deferred to P10: mlsysim/docs prose, generated docs/api/* stubs, tutorial
slides, paper.tex, README — propagate to website/slides/paper.
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
"""
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Example: Custom System Design
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=============================
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This script demonstrates how to build a hypothetical system from scratch
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without using the vetted registries. This is how researchers can use
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mlsysim to model unreleased or generic hardware.
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"""
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import mlsysim
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from mlsysim.hardware.types import HardwareNode, ComputeCore, MemoryHierarchy
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from mlsysim.models.types import CNNWorkload
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from mlsysim.engine.scenarios import Scenario
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def main():
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print("--- Designing a Hypothetical 'Generic Drone' ---")
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# 1. Manually define hardware (Supply)
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drone_chip = HardwareNode(
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name="Hypothetical Drone NPU",
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release_year=2026,
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compute=ComputeCore(peak_flops="10 TFLOPs/s"),
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memory=MemoryHierarchy(capacity="2 GB", bandwidth="50 GB/s"),
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tdp="10 W",
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dispatch_tax="0.5 ms"
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)
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# 2. Manually define workload (Demand)
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my_model = CNNWorkload(
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name="Custom Vision Model",
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architecture="CNN",
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parameters="50 Mparam",
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inference_flops="10 Gflop"
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)
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# 3. Bundle into a Scenario
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my_scenario = Scenario(
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name="Generic Drone Vision",
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description="A custom vision task on unreleased drone hardware.",
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workload=my_model,
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system=drone_chip,
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sla_latency="30 ms"
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)
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# 4. Evaluate the custom design
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print(f"Evaluating {my_scenario.name}...")
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report = my_scenario.evaluate()
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print(report.scorecard())
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if __name__ == "__main__":
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main()
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# Expected output (mlsysim v0.1.1):
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# --- Designing a Hypothetical 'Generic Drone' ---
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# Evaluating Generic Drone Vision...
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# +============================================================+
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# | MLSys-im SYSTEM EVALUATION
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# | Scenario: Generic Drone Vision
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# +============================================================+
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# | Level 1: Feasibility [PASS]
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# | Model fits in memory (100.0 MB / 2000.0 MB)
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# +------------------------------------------------------------+
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# | Level 2: Performance [PASS]
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# | Latency: 2.71 millisecond (Target: 30 ms)
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# +------------------------------------------------------------+
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# | Level 3: Macro/Economics [PASS]
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# | Annual Carbon: 42.1 kg | TCO: $11,512
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# +============================================================+
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