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https://github.com/MLSysBook/TinyTorch.git
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🎯 NORTH STAR VISION DOCUMENTED: 'Don't Just Import It, Build It' - Training AI Engineers, not just ML users AI Engineering emerges as a foundational discipline like Computer Engineering, bridging algorithms and systems to build the AI infrastructure of the future. 🧪 ROBUST TESTING FRAMEWORK ESTABLISHED: - Created tests/regression/ for sandbox integrity tests - Implemented test-driven bug prevention workflow - Clear separation: student tests (pedagogical) vs system tests (robustness) - Every bug becomes a test to prevent recurrence ✅ KEY IMPLEMENTATIONS: - NORTH_STAR.md: Vision for AI Engineering discipline - Testing best practices: Focus on robust student sandbox - Git workflow standards: Professional development practices - Regression test suite: Prevent infrastructure issues - Conv->Linear dimension tests (found CNN bug) - Transformer reshaping tests (found GPT bug) 🏗️ SANDBOX INTEGRITY: Students need a solid, predictable environment where they focus on ML concepts, not debugging framework issues. The framework must be invisible. 📚 EDUCATIONAL PHILOSOPHY: TinyTorch isn't just teaching a framework - it's founding the AI Engineering discipline by training engineers who understand how to BUILD ML systems. This establishes the foundation for training the first generation of true AI Engineers who will define this emerging discipline.
85 lines
2.6 KiB
Python
Executable File
85 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python
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"""
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TinyTorch Sandbox Integrity Tests
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==================================
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Run this to ensure the student learning sandbox is robust.
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All core infrastructure must work perfectly so students can
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focus on learning ML systems, not debugging framework issues.
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"""
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import sys
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import os
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import importlib
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# Test modules to run
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TEST_MODULES = [
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'test_conv_linear_dimensions',
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'test_transformer_reshaping',
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]
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def run_sandbox_tests():
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"""Run all sandbox integrity tests."""
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print("="*60)
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print("🧪 TINYTORCH SANDBOX INTEGRITY CHECK")
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print("="*60)
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print("\nEnsuring the learning environment is robust...\n")
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all_passed = True
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results = []
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for test_module in TEST_MODULES:
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try:
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# Import and run the test module
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print(f"Running {test_module}...")
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module = importlib.import_module(test_module)
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# Look for a main function or run tests directly
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if hasattr(module, 'main'):
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result = module.main()
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elif '__main__' in dir(module):
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# Module runs tests when imported
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result = True
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else:
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# Try to run all test functions
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test_funcs = [f for f in dir(module) if f.startswith('test_')]
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for func_name in test_funcs:
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func = getattr(module, func_name)
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func()
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result = True
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results.append((test_module, True, "PASSED"))
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print(f" ✅ {test_module}: PASSED\n")
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except Exception as e:
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results.append((test_module, False, str(e)))
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print(f" ❌ {test_module}: FAILED")
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print(f" Error: {e}\n")
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all_passed = False
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# Summary
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print("="*60)
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print("📊 SANDBOX TEST SUMMARY")
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print("="*60)
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for module, passed, status in results:
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icon = "✅" if passed else "❌"
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print(f"{icon} {module}: {status}")
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if all_passed:
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print("\n🎉 SANDBOX IS ROBUST!")
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print("Students can focus on learning ML systems.")
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return 0
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else:
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print("\n⚠️ SANDBOX NEEDS ATTENTION")
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print("Some infrastructure tests failed.")
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print("Students might encounter framework issues.")
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return 1
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if __name__ == "__main__":
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# Add the test directory to path
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test_dir = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, test_dir)
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# Run tests
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exit_code = run_sandbox_tests()
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sys.exit(exit_code) |