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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>
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
"""
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Hello World: Ten Minutes to mlsysim
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===================================
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This tutorial demonstrates the end-to-end workflow of mlsysim:
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1. Load a Model and Hardware.
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2. Solve single-node performance.
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3. Scale to a fleet.
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4. Calculate Sustainability and Economics.
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"""
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import mlsysim
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from mlsysim import load_config
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def main():
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print("--- 1. Define Your Simulation ---")
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user_choice = {
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"model": "ResNet50",
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"hardware": "A100",
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"batch_size": 32,
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"fleet_size": 128,
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"region": "Quebec"
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}
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# load_config automatically validates physical feasibility!
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config = load_config(user_choice)
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print("Config Validated: " + config.model + " on " + config.hardware + " in " + config.region + "\n")
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print("--- 2. Single-Node Performance (The Iron Law) ---")
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model = getattr(mlsysim.Models, config.model)
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hardware = getattr(mlsysim.Hardware, config.hardware)
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perf = mlsysim.Engine.solve(model, hardware, batch_size=config.batch_size)
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print("Latency: " + str(perf.latency))
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print("Throughput: " + str(perf.throughput))
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print("Bottleneck: " + perf.bottleneck + "\n")
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print("--- 3. Scenario Evaluation & Visualization ---")
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# Using a vetted lighthouse scenario
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scenario = mlsysim.Applications.AutoDrive
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evaluation = scenario.evaluate()
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print(evaluation.scorecard())
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# Visual Scorecard
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fig, ax = mlsysim.plot_evaluation_scorecard(evaluation)
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print("\nVisual Scorecard generated.")
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print("\nSimulation Complete. Check mlsysbook.ai for advanced labs!")
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if __name__ == "__main__":
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main()
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