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https://github.com/harvard-edge/cs249r_book.git
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a9878ad6bd
Snapshot of the standalone /Users/VJ/GitHub/mlperf-edu/ repo as of 2026-04-16, brought into MLSysBook as a parked feature branch for backup and iteration. Not for merge to dev. Contents (88 files, ~2.3 MB): - 16 reference workloads (cloud / edge / tiny / agent divisions) - LoadGen proxy harness + SUT plugin protocol - Compliance checker, autograder, hardware fingerprint - Paper draft (paper.tex) with TikZ/SVG figure sources - Three lab examples + practitioner workflow configs - Workload + dataset YAML registries (single source of truth) Excluded (per mlperf-edu/.gitignore + size constraints): - Datasets (6.6 GB), checkpoints (260 MB), gpt2 weights (523 MB) - Generated PDFs, .venv, build artifacts
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import time
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import pytest
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from mlperf_edu.power import PowerMeter, measure_power
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def test_power_meter_basic():
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# Use a high TDP to make sure we see some energy used even in short time
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meter = PowerMeter(tdp_watts=200.0, interval=0.01)
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meter.start()
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time.sleep(0.1)
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joules = meter.stop()
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# Energy should be greater than 0
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# Expected: 200W * 0.1s = 20J (approximately)
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assert joules > 0
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assert joules < 50 # Safety margin
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def test_measure_power_context_manager():
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with measure_power(tdp_watts=100.0) as meter:
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time.sleep(0.2)
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current_joules = meter.energy_joules
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assert current_joules > 0
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final_joules = meter.energy_joules
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assert final_joules >= current_joules
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# Expected: P_idle * TDP * time = 0.15 * 100 * 0.2 = 3.0J
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# With some slack:
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assert 2.0 < final_joules < 10.0
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def test_power_meter_integration():
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# Simulate some work
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with measure_power(tdp_watts=100.0) as meter:
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# Busy wait to increase CPU util
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start = time.time()
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while time.time() - start < 0.2:
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_ = [i**2 for i in range(1000)]
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joules = meter.energy_joules
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assert joules > 0
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print(f"Measured {joules} Joules for 0.2s of work at 100W TDP")
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