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https://github.com/MLSysBook/TinyTorch.git
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Features: - 16 checkpoint test suite validating ML systems capabilities - Integration tests covering complete learning progression - Rich CLI progress tracking with visual timelines - Capability-driven assessment from environment to production Checkpoints: - Environment setup through full ML system deployment - Each checkpoint validates integrated functionality - Progressive capability building with clear success criteria - Professional CLI interface with status/timeline/test commands
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
"""
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Checkpoint 0: Environment Setup (After Module 1 - Setup)
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Question: "Can I configure my TinyTorch development environment?"
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"""
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import sys
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import platform
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import pytest
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def test_checkpoint_00_environment():
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"""
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Checkpoint 0: Environment Setup
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Validates that the development environment is properly configured
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and TinyTorch is available for use.
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"""
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print("\n🔧 Checkpoint 0: Environment Setup")
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print("=" * 50)
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# Test 1: Python environment
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python_version = f"{sys.version_info.major}.{sys.version_info.minor}"
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print(f"✅ Python {python_version}")
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assert sys.version_info.major >= 3, "Python 3+ required"
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assert sys.version_info.minor >= 8, "Python 3.8+ recommended"
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# Test 2: Platform information
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system = platform.system()
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print(f"✅ Platform: {system}")
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# Test 3: TinyTorch availability
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try:
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import tinytorch
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version = getattr(tinytorch, '__version__', 'unknown')
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print(f"✅ TinyTorch {version} ready")
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except ImportError:
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pytest.fail("❌ TinyTorch not available - run installation first")
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# Test 4: Core dependencies
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try:
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import numpy as np
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print(f"✅ NumPy {np.__version__}")
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except ImportError:
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pytest.fail("❌ NumPy not available")
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print("\n🎉 Environment Setup Complete!")
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print("📝 You can now configure TinyTorch development environments")
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print("🎯 Next: Build tensor foundations")
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if __name__ == "__main__":
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test_checkpoint_00_environment() |