Automatic Backend Selection

[jeremymanning]

Parent Epic: #155

Task: Automatic Backend Selection

Description

Implement intelligent backend selection when backend=None, detecting available resources (GPU, Metal, CUDA) and libraries.

Acceptance Criteria

• Detects GPU/Metal/CUDA availability
• Checks for JAX/PyTorch installation
• Selects optimal backend automatically
• Falls back to NumPy gracefully
• Clear logging of backend selection

Technical Details

• Create backend selection logic in htfa/backends/init.py
• Check for GPU: nvidia-smi, Metal API, ROCm
• Try importing jax/torch, check versions
• Implement priority: JAX+GPU > PyTorch+GPU > NumPy
• Cache detection results for performance

Dependencies

• Task 159 must be complete (backend interface exists)

Effort Estimate

• Size: S
• Hours: 4-6
• Parallel: false

Definition of Done

• Detection logic implemented
• Automatic selection works
• Fallback mechanisms tested
• Performance profiling done
• Documentation updated

[jeremymanning]

🔄 Progress Update - 2025-09-11

✅ Completed Work

• Hardware Detection Module - Fully implemented with GPU detection (CUDA/Metal/ROCm), system resource queries, and caching
• Library Detection Module - Complete with JAX/PyTorch/NumPy detection, version compatibility checks, and optimal backend selection logic
• Testing & Documentation - Comprehensive unit/integration tests created, documentation updated with examples

🔄 In Progress

• Backend Selection Logic Integration - Ready to start now that detection modules are complete
• Needs integration into htfa/backends/__init__.py and creation of htfa/backends/selector.py

📝 Technical Notes

• Hardware detection implemented with subprocess calls and environment checks
• Library detection uses safe import testing with exception handling
• All detection functions cached with @lru_cache for performance
• Standardized dictionary returns across all detection functions
• Priority system established: JAX+GPU > PyTorch+GPU > JAX+CPU > PyTorch+CPU > NumPy

📊 Acceptance Criteria Status

• ✅ Detects GPU/Metal/CUDA availability
• ✅ Checks for JAX/PyTorch installation
• 🔄 Selects optimal backend automatically (integration pending)
• 🔄 Falls back to NumPy gracefully (logic ready, integration pending)
• ✅ Clear logging of backend selection

🚀 Next Steps

1. Launch Stream 3 agent for backend selection integration
2. Update htfa/backends/__init__.py with auto-selection logic
3. Create htfa/backends/selector.py for centralized selection
4. Integrate with HTFA model initialization
5. Execute full test suite with actual implementations

⚠️ Blockers

None - All dependencies (Stream 1 & 2) are complete

💻 Recent Implementation

• Created htfa/utils/hardware_detection.py (~400 lines)
• Created htfa/utils/library_detection.py (~350 lines)
• Created comprehensive test files and documentation
• Updated htfa/utils/__init__.py with exports

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Progress: 75% | Synced from local updates at 2025-09-11T02:29:29Z

[jeremymanning]

✅ Task Completed - 2025-09-11

🎯 All Acceptance Criteria Met

• ✅ Detects GPU/Metal/CUDA availability
• ✅ Checks for JAX/PyTorch installation
• ✅ Selects optimal backend automatically
• ✅ Falls back to NumPy gracefully
• ✅ Clear logging of backend selection

📦 Deliverables

• htfa/utils/hardware_detection.py - Comprehensive hardware detection (CUDA, Metal, ROCm, CPU/memory)
• htfa/utils/library_detection.py - Library availability checking (JAX, PyTorch, NumPy)
• htfa/backends/selector.py - BackendSelector class with priority-based selection algorithm
• Updated htfa/core/htfa.py and htfa/core/tfa.py to auto-select when backend=None
• Test script verifying all functionality

🧪 Testing

• Manual testing: ✅ Complete - All detection modules tested
• Integration testing: ✅ Complete - HTFA/TFA classes work with auto-selection
• Test output shows PyTorch+CUDA correctly selected on system with GPU

📚 Implementation Details

• Priority algorithm: JAX+TPU > JAX+GPU > PyTorch+CUDA > PyTorch+MPS > JAX+CPU > PyTorch+CPU > NumPy
• All detection functions cached with @lru_cache for performance
• Graceful fallback to NumPy when other backends unavailable
• Clear logging of selection rationale

💻 Final Commit

Issue #160: Implement automatic backend selection
- Created hardware detection module with GPU/CPU detection
- Created library detection module for JAX/PyTorch availability  
- Implemented BackendSelector with priority-based selection
- Updated HTFA/TFA classes to auto-select when backend=None
- Added comprehensive test script verifying all functionality

This task is ready for review and can be closed.

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Task completed: 100% | Synced at 2025-09-11T13:41:35Z

[jeremymanning]

Completed and merged to main