mirror of
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
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import subprocess
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import sys
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import os
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def test_cli_help():
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result = subprocess.run(
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[sys.executable, "-m", "mlperf_edu.cli", "--help"],
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capture_output=True,
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text=True,
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env={**os.environ, "PYTHONPATH": "."}
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)
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assert result.returncode == 0
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assert "MLPerf EDU" in result.stdout
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def test_cli_run_cloud_help():
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result = subprocess.run(
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[sys.executable, "-m", "mlperf_edu.cli", "run", "cloud", "--help"],
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capture_output=True,
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text=True,
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env={**os.environ, "PYTHONPATH": "."}
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)
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assert result.returncode == 0
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assert "--task" in result.stdout
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assert "llm-inference" in result.stdout
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def test_cli_run_cloud_inference_offline():
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# Run a very small test to keep it fast
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# We can't easily pass arguments to change total_samples from CLI yet without more changes
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# but we can verify it runs.
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result = subprocess.run(
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[sys.executable, "-m", "mlperf_edu.cli", "run", "cloud", "--task", "llm-inference", "--scenario", "Offline"],
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capture_output=True,
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text=True,
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env={**os.environ, "PYTHONPATH": "."}
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)
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assert result.returncode == 0
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assert "MLPerf EDU Cloud Benchmark Report" in result.stdout
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assert "Scenario: Offline" in result.stdout
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