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a78f1bd8b0
Complete MLSYSIM v0.1.0 implementation with: - Documentation website (Quarto): landing page with animated hero and capability carousel, 4 tutorials (hello world, LLM serving, distributed training, sustainability), hardware/model/fleet/infra catalogs, solver guide, whitepaper, math foundations, glossary, and full quartodoc API reference - Typed registry system: Hardware (18 devices across 5 tiers), Models (15 workloads), Systems (fleets, clusters, fabrics), Infrastructure (grid profiles, rack configs, datacenters) - Core types: Pint-backed Quantity, Metadata provenance tracking, custom exception hierarchy (OOMError, SLAViolation) - SimulationConfig with YAML/JSON loading and pre-validation - Scenario system tying workloads to systems with SLA constraints - Multi-level evaluation scorecard (feasibility, performance, macro) - Examples, tests, and Jetson Orin NX spec fix (100 → 25 TFLOP/s) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
"""
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Tutorial: The Manual Sweep Pattern
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==================================
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This tutorial teaches students how to "think like a systems engineer"
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by manually sweeping a parameter (Batch Size) to find the "Cliff."
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"""
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import mlsysim
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import pandas as pd # Optional, but common for students
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def main():
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print("Scenario: Autonomous Vehicle Perception on Jetson Orin NX")
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scenario = mlsysim.Applications.AutoDrive
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results = []
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# 1. Manually sweep batch sizes from 1 to 128
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batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128]
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print(f"{'Batch':<10} | {'Status':<10} | {'Latency':<15} | {'Bottleneck':<15}")
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print("-" * 60)
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for b in batch_sizes:
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# Evaluate each point
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evaluation = scenario.evaluate(batch_size=b)
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# Flatten results for our table
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row = evaluation.to_dict()
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# Print a quick summary row
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status = row['p_status'] if row['f_status'] == "PASS" else "OOM"
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latency = f"{row['p_latency']:.2f}" if row['f_status'] == "PASS" else "---"
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bottleneck = row.get('p_bottleneck', "---")
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print(f"{b:<10} | {status:<10} | {latency:<15} | {bottleneck:<15}")
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# Collect for later deep analysis
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results.append(row)
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# 2. Convert the list of dicts to a DataFrame for analysis
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df = pd.DataFrame(results)
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print("\nDataFrame Summary (First 5 rows):")
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print(df[['scenario', 'f_status', 'p_latency']].head())
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if __name__ == "__main__":
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main()
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