fegger
46657c7ffe
Containerize the application with ROCm GPU support for AMD Radeon R9700: - Add Dockerfile with PyTorch/PyG ROCm 5.6 wheels - Add docker-compose.yml with dashboard, live-trading, and train services - Add .dockerignore and .env.example for configuration Fix benchmark script to use batch_size instead of num_stocks for variable dimensions, and replace fragile partial model surgery with a standalone MLP for memory estimation. Fix data pipeline to skip dates with no next trading date instead of fabricating zero returns. Add slippage to paper broker, optional mark-to-market prices to broker interface, and health check endpoint for container orchestration.
184 lines
4.5 KiB
YAML
184 lines
4.5 KiB
YAML
# =============================================================================
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# StockGNN R9700 — Docker Compose Stack
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# =============================================================================
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# Services:
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# - dashboard : FastAPI web frontend (http://localhost:8000)
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# - live-trading: Continuous paper/live trading engine
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# - train : One-off model training & backtesting
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#
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# Requirements:
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# - Docker 20.10+ with Compose v2
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# - AMD GPU + ROCm 5.6+ drivers on host
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# - docker-compose run --rm train # manual training
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# =============================================================================
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services:
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# -------------------------------------------------------------------------
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# Base image build (shared by all services)
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# -------------------------------------------------------------------------
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dashboard:
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build:
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context: .
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dockerfile: Dockerfile
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image: stockgnn:r9700
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container_name: stockgnn-dashboard
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restart: unless-stopped
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# Override default CMD for the web dashboard
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command: >
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python -m uvicorn src.web.app:app
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--host 0.0.0.0
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--port 8000
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--reload
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ports:
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- "8000:8000"
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volumes:
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# Persist data & model artefacts across restarts
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- ./data:/app/data
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- ./models:/app/models
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- ./logs:/app/logs
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# Optional: mount source for live-reload during development
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# - ./src:/app/src
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env_file:
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- .env
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environment:
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# ROCm / AMD GPU tuning
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HSA_OVERRIDE_GFX_VERSION: "${HSA_OVERRIDE_GFX_VERSION:-10.3.0}"
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PYTORCH_HIP_ALLOC_CONF: "expandable_segments:True"
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# Ensure the app knows it is inside a container
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STOCKGNN_ENV: docker
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# AMD GPU device access
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devices:
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- /dev/kfd
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- /dev/dri
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# Required groups for GPU access inside the container
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group_add:
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- video
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- render
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# Security / capability settings for ROCm
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security_opt:
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- seccomp:unconfined
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# Shared memory size for PyTorch DataLoader multiprocessing
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shm_size: "8gb"
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# Health-check for the web dashboard
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8000/api/dashboard/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 40s
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networks:
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- stockgnn-net
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# -------------------------------------------------------------------------
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# Live Trading Engine (paper or live — controlled via config.py)
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# -------------------------------------------------------------------------
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live-trading:
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build:
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context: .
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dockerfile: Dockerfile
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image: stockgnn:r9700
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container_name: stockgnn-live-trading
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restart: unless-stopped
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command: >
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python live_trading.py
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volumes:
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- ./data:/app/data
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- ./models:/app/models
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- ./logs:/app/logs
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env_file:
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- .env
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environment:
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HSA_OVERRIDE_GFX_VERSION: "${HSA_OVERRIDE_GFX_VERSION:-10.3.0}"
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PYTORCH_HIP_ALLOC_CONF: "expandable_segments:True"
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STOCKGNN_ENV: docker
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devices:
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- /dev/kfd
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- /dev/dri
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group_add:
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- video
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- render
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security_opt:
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- seccomp:unconfined
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shm_size: "8gb"
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networks:
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- stockgnn-net
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# Do not auto-start live trading until the user explicitly brings it up
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profiles:
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- live
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# -------------------------------------------------------------------------
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# Model Training & Backtesting (run manually)
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# -------------------------------------------------------------------------
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# Usage:
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# docker compose run --rm train
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# -------------------------------------------------------------------------
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train:
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build:
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context: .
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dockerfile: Dockerfile
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image: stockgnn:r9700
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container_name: stockgnn-train
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command: >
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python main.py
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volumes:
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- ./data:/app/data
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- ./models:/app/models
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- ./logs:/app/logs
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env_file:
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- .env
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environment:
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HSA_OVERRIDE_GFX_VERSION: "${HSA_OVERRIDE_GFX_VERSION:-10.3.0}"
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PYTORCH_HIP_ALLOC_CONF: "expandable_segments:True"
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STOCKGNN_ENV: docker
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devices:
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- /dev/kfd
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- /dev/dri
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group_add:
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- video
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- render
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security_opt:
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- seccomp:unconfined
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shm_size: "16gb"
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networks:
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- stockgnn-net
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profiles:
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- train
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# -----------------------------------------------------------------------------
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# Shared network
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# -----------------------------------------------------------------------------
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networks:
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stockgnn-net:
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driver: bridge
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