mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-03 07:52:11 -05:00
66a17a8a40
✅ **Full Module Implementation:** - module.yaml: Proper metadata and dependencies - README.md: Comprehensive documentation with learning objectives - benchmarking_dev.py: Complete implementation with educational pattern ✅ **MLPerf-Inspired Architecture:** - BenchmarkScenarios: Single-stream, server, and offline scenarios - StatisticalValidator: Proper statistical validation and significance testing - TinyTorchPerf: Complete framework integrating all components - PerformanceReporter: Professional report generation for capstone projects ✅ **Educational Excellence:** - Same structure as layers_dev.py with Build → Use → Analyze framework - Comprehensive TODO guidance with step-by-step implementation - Unit tests for each component with immediate feedback - Integration testing with realistic TinyTorch models - Professional module summary with career connections ✅ **Test Results:** - All 5 test functions passing (100% success rate) - Complete benchmarking workflow validated - Statistical validation working correctly - Professional reporting generating capstone-ready outputs - Framework ready for student use ✅ **Capstone Preparation:** - Students can now systematically evaluate their final projects - Professional reporting suitable for academic presentations - Statistical validation ensures meaningful results - Industry-standard methodology following MLPerf patterns 🎓 **Perfect Bridge to Module 13 (MLOps):** - Benchmarking establishes performance baselines - MLOps will monitor production systems against these baselines - Statistical validation transfers to production monitoring - Professional reporting becomes production dashboards
62 lines
1.9 KiB
YAML
62 lines
1.9 KiB
YAML
# TinyTorch Module Metadata
|
|
# Essential system information for CLI tools and build systems
|
|
|
|
name: "12_benchmarking"
|
|
title: "Benchmarking - Systematic ML Performance Evaluation"
|
|
description: "Industry-standard benchmarking methodology for ML systems, inspired by MLPerf patterns"
|
|
version: "1.0.0"
|
|
author: "TinyTorch Team"
|
|
|
|
# Dependencies - Used by CLI for module ordering and prerequisites
|
|
dependencies:
|
|
prerequisites: [
|
|
"00_setup", "01_tensor", "02_activations", "03_layers",
|
|
"04_networks", "05_cnn", "06_dataloader", "07_autograd",
|
|
"08_optimizers", "09_training", "10_compression", "11_kernels"
|
|
]
|
|
enables: ["13_mlops"]
|
|
|
|
# Package Export - What gets built into tinytorch package
|
|
exports_to: "tinytorch.core.benchmarking"
|
|
|
|
# File Structure - What files exist in this module
|
|
files:
|
|
dev_file: "benchmarking_dev.py"
|
|
test_file: "tests/test_benchmarking.py"
|
|
readme: "README.md"
|
|
benchmark_dir: "benchmarks/"
|
|
|
|
# Components - What's implemented in this module
|
|
components:
|
|
- "TinyTorchPerf"
|
|
- "BenchmarkScenarios"
|
|
- "StatisticalValidator"
|
|
- "ResultsAnalyzer"
|
|
- "PerformanceReporter"
|
|
|
|
# Learning Objectives - What students will achieve
|
|
learning_objectives:
|
|
- "Design systematic benchmarking experiments for ML systems"
|
|
- "Apply MLPerf-inspired patterns to evaluate model performance"
|
|
- "Implement statistical validation for benchmark results"
|
|
- "Create professional performance reports and comparisons"
|
|
- "Prepare for capstone project benchmarking requirements"
|
|
|
|
# Pedagogical Framework
|
|
pedagogical_framework: "Build → Use → Analyze"
|
|
|
|
# Assessment - How learning is verified
|
|
assessment:
|
|
total_points: 100
|
|
breakdown:
|
|
framework_usage: 30
|
|
scenario_implementation: 25
|
|
statistical_analysis: 25
|
|
capstone_preparation: 20
|
|
|
|
# Estimated Time
|
|
time_estimate: "6-8 hours"
|
|
difficulty: "⭐⭐⭐⭐ Advanced"
|
|
|
|
# Next Steps
|
|
next_modules: ["13_mlops"] |