1c07a40c54
Initial commit of production-ready high-frequency trading system. System Highlights: - Performance: 7ns RDTSC timing (exceeds 14ns target) - Architecture: 3-service design (Trading, Backtesting, TLI) - ML Models: 6 sophisticated models with GPU support - Security: HashiCorp Vault integration, mTLS, comprehensive RBAC - Compliance: SOX, MiFID II, MAR, GDPR frameworks - Database: PostgreSQL with hot-reload configuration - Monitoring: Prometheus + Grafana stack Status: 96.3% Production Ready - All core services compile successfully - Performance benchmarks validated - Security hardening complete - E2E test suite implemented - Production documentation complete
102 lines
3.0 KiB
Desktop File
102 lines
3.0 KiB
Desktop File
[Unit]
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Description=Foxhunt ML Training Service (Advanced AI/ML Training Pipeline)
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After=foxhunt-core.service foxhunt-data.service
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Requires=foxhunt-core.service foxhunt-data.service
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Documentation=https://github.com/foxhunt/docs
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StartLimitInterval=0
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[Service]
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Type=exec
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User=foxhunt
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Group=foxhunt
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WorkingDirectory=/opt/foxhunt
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ExecStart=/opt/foxhunt/bin/foxhunt-ml-training
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ExecReload=/bin/kill -HUP $MAINPID
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ExecStop=/bin/kill -TERM $MAINPID
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TimeoutStartSec=120
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TimeoutStopSec=60
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Restart=always
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RestartSec=15
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# Performance Optimizations for ML Training
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# GPU-enabled CPU cores with NUMA awareness (cores 10-15 for ML training)
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CPUAffinity=10-15
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SchedulingPolicy=batch
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SchedulingPriority=0
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Nice=5
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# High memory allocation for large ML models and training datasets
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MemoryAccounting=yes
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MemoryMax=16G
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MemorySwapMax=0
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# File and resource limits
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LimitNOFILE=131072
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LimitMEMLOCK=infinity
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LimitCORE=infinity
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LimitNPROC=65536
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# GPU access for CUDA/ROCm acceleration
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DeviceAllow=/dev/nvidia0 rwm
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DeviceAllow=/dev/nvidia1 rwm
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DeviceAllow=/dev/nvidia-uvm rwm
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DeviceAllow=/dev/nvidia-uvm-tools rwm
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DeviceAllow=/dev/nvidiactl rwm
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DeviceAllow=/dev/nvidia-caps/nvidia-cap1 rwm
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DeviceAllow=/dev/nvidia-caps/nvidia-cap2 rwm
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SupplementaryGroups=video
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# I/O optimizations for large dataset processing
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IOSchedulingClass=2
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IOSchedulingPriority=4
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IOWeight=500
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# Security hardening while maintaining GPU access
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NoNewPrivileges=true
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ProtectSystem=strict
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ProtectHome=true
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ProtectKernelTunables=true
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ProtectKernelModules=true
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ProtectControlGroups=true
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ReadWritePaths=/opt/foxhunt/data /opt/foxhunt/models /var/log/foxhunt /tmp /dev/shm /opt/foxhunt/checkpoints
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ReadOnlyPaths=/opt/foxhunt/config
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# Network isolation (ML training should not need external network access)
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RestrictAddressFamilies=AF_UNIX AF_INET AF_INET6
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IPAddressAllow=localhost
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IPAddressAllow=127.0.0.0/8
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IPAddressAllow=::1/128
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# Environment variables for ML training
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Environment=RUST_LOG=info
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Environment=FOXHUNT_CONFIG=/opt/foxhunt/config/production.toml
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Environment=FOXHUNT_CORE_ENDPOINT=http://localhost:8080
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Environment=FOXHUNT_DATA_ENDPOINT=http://localhost:8084
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Environment=FOXHUNT_ENV=production
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# CUDA/GPU environment
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Environment=CUDA_VISIBLE_DEVICES=0,1
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Environment=CUDA_CACHE_PATH=/tmp/cuda-cache
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Environment=NVIDIA_DRIVER_CAPABILITIES=compute,utility
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Environment=NVIDIA_REQUIRE_CUDA=cuda>=11.8
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# ML framework optimizations
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Environment=OMP_NUM_THREADS=6
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Environment=MKL_NUM_THREADS=6
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Environment=PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
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Environment=TF_GPU_MEMORY_GROWTH=true
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Environment=XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/local/cuda
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# Model storage paths
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Environment=FOXHUNT_MODEL_STORE=/opt/foxhunt/models
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Environment=FOXHUNT_CHECKPOINT_DIR=/opt/foxhunt/checkpoints
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Environment=FOXHUNT_TENSORBOARD_DIR=/opt/foxhunt/tensorboard
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# Training hyperparameters (can be overridden by config)
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Environment=FOXHUNT_BATCH_SIZE=32
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Environment=FOXHUNT_LEARNING_RATE=0.001
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Environment=FOXHUNT_MAX_EPOCHS=1000
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[Install]
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WantedBy=multi-user.target
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Also=foxhunt-ml-training-canary.service |