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jgrusewski 41effb1450 fix: Remove hardcoded CUDA features from Docker builds

- Make candle-core CUDA features optional (not hardcoded) in ml/Cargo.toml
- Add CUDARC_CUDA_VERSION=13000 to skip nvcc detection in Dockerfiles
- Add CUDA_COMPUTE_CAP=86 to skip nvidia-smi GPU detection
- Remove invalid --features cuda from ml_training_service build

FIXES:
- Trading Service: nvidia-smi failed (candle-kernels build)
- Backtesting Service: nvidia-smi failed (candle-kernels build)
- ML Training Service: Wrong feature flag (cuda doesn't exist on service)

IMPACT:
- Services build without CUDA toolchain requirements
- CUDA still available at runtime via nvidia/cuda base images
- GPU auto-detected by candle when running with --gpus all

BUILD RESULTS:
- API Gateway: ✅ 119MB
- Trading Service: ✅ 119MB (3m 36s build)
- Backtesting Service: ✅ 120MB (3m 31s build)
- ML Training Service: 🟡 IN PROGRESS (CUDA base image ~1.6GB)

Wave 121 - Docker CUDA Build Fixes

2025-10-07 20:23:40 +02:00

api_gateway

fix: Add missing workspace members to Dockerfiles (Agent 94)

2025-10-07 18:40:36 +02:00

backtesting_service

fix: Remove hardcoded CUDA features from Docker builds

2025-10-07 20:23:40 +02:00

integration_tests

🚀 Wave 125 Phase 2: Performance 100%, Monitoring 100%, +36 Tests - 99.1% Production Ready

2025-10-07 18:28:28 +02:00

load_tests

🚀 Wave 122 Complete: Deployment Readiness Validated