# ML4T 3rd Edition — Docker Compose Configuration # # QUICK START (pre-built images from Docker Hub): # docker compose pull ml4t # Pull pre-built image (~12 GB amd64, ~3 GB arm64) # docker compose up ml4t # Start Jupyter Lab at http://localhost:8888 # # To build locally instead: # docker compose build ml4t # Build from Dockerfile (slow, ~45 min) # # PLATFORM SUPPORT: # - Linux x86_64: All services work, GPU optional # - Windows x86_64: All services via Docker Desktop (WSL2), GPU via WSL2+nvidia-toolkit # - macOS Intel: All services except GPU # - Apple Silicon: ml4t + benchmark (native arm64); ml4t-py312 not available (see below) # # IMAGES ON DOCKER HUB (docker.io/ml4t/): # ml4t:latest — All chapters, Python 3.14, PyTorch CUDA 12.8 (amd64+arm64) # ml4t-py312:latest — Python 3.12: signatory, esig, gensim, tfcausalimpact (amd64 only) # ml4t-benchmark:latest — Storage benchmarks: DuckDB, HDF5, DB clients (amd64+arm64) # # WHICH IMAGE DO I NEED? # Most readers: ml4t only (covers Ch01-Ch27 + case studies) # Ch05 NB01/03/07, Ch09 NB06/12, Ch10 NB01-03, Ch12 NB10, Ch14 NB06, Ch15 NB06, Ch21 deep_hedging: # ml4t-py312 (x86 only, Apple Silicon: see .ipynb outputs) # Ch02 storage benchmarks: benchmark + database services # Ch12 GBM GPU benchmark: rapids (requires NVIDIA GPU) # # See docs/installation.md for detailed setup instructions x-common: &common user: "${UID:-1000}:${GID:-1000}" volumes: - .:/app:rw # Read-write so the in-container download workflow (data/download_all.py) # can populate /data. ML4T_DATA_PATH=/data below points every downloader # here; a read-only mount makes each write fail with "Read-only file system". - ${ML4T_DATA_PATH:-./data}:/data:rw environment: - ML4T_DATA_PATH=/data - ML4T_PATH=/app - TEST=${TEST:-0} - NEO4J_URI=${NEO4J_URI:-bolt://neo4j:7687} - NEO4J_USER=${NEO4J_USER:-neo4j} - NEO4J_PASSWORD=${NEO4J_PASSWORD:-password} - NUMBA_CACHE_DIR=/tmp/numba_cache - MPLCONFIGDIR=/tmp/matplotlib - POLARS_FMT_MAX_ROWS=20 - POLARS_FMT_STR_LEN=50 - VIRTUAL_ENV=/opt/ml4t - UV_PROJECT_ENVIRONMENT=/opt/ml4t - PATH=/opt/ml4t/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games env_file: - path: .env required: false working_dir: /app stdin_open: true tty: true services: # ============================================ # ML4T MAIN (All Chapters) # Works on all platforms including Apple Silicon # ============================================ ml4t: <<: *common image: ml4t/ml4t:latest build: context: . dockerfile: envs/ml4t/Dockerfile container_name: ml4t-review ports: - "8888:8888" command: ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"] # ============================================ # ML4T GPU (NVIDIA only — Linux/Windows x86) # Same as ml4t but with GPU passthrough # ============================================ ml4t-gpu: <<: *common image: ml4t/ml4t:latest build: context: . dockerfile: envs/ml4t/Dockerfile container_name: ml4t-review-gpu ports: - "8889:8888" command: ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"] profiles: ["gpu"] deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: [gpu] # ============================================ # BENCHMARK (ARM64 Compatible) # Storage benchmarks: Parquet, DuckDB, HDF5, database clients # Excludes ArcticDB (no ARM64 wheels) # Works on Apple Silicon # ============================================ benchmark: <<: *common image: ml4t/ml4t-benchmark:latest build: context: . dockerfile: envs/benchmark/Dockerfile container_name: ml4t-benchmark ports: - "8887:8888" profiles: ["benchmark"] environment: - ML4T_DATA_PATH=/data - ML4T_PATH=/app - TEST=${TEST:-0} # Database connections (when running with database services) - CLICKHOUSE_HOST=ml4t-clickhouse - CLICKHOUSE_PORT=8123 - QUESTDB_HOST=ml4t-questdb - QUESTDB_HTTP_PORT=9000 - TIMESCALE_HOST=ml4t-timescaledb - TIMESCALE_PORT=5432 - TIMESCALE_PASSWORD=benchmark - INFLUXDB_HOST=ml4t-influxdb - INFLUXDB_PORT=8086 - INFLUXDB_ORG=ml4t - INFLUXDB_TOKEN=benchmark-token - INFLUXDB_BUCKET=market_data - POSTGRES_HOST=ml4t-postgres - POSTGRES_PORT=5432 - POSTGRES_USER=postgres - POSTGRES_PASSWORD=benchmark - POSTGRES_DB=ml4t # ============================================ # BENCHMARK FULL (x86 Only) # All benchmarks including ArcticDB # WARNING: Does not work on Apple Silicon # ============================================ benchmark-full: <<: *common build: context: . dockerfile: envs/benchmark/Dockerfile.full # Force x86 build (required for ArcticDB) platforms: - linux/amd64 container_name: ml4t-benchmark-full ports: - "8887:8888" profiles: ["benchmark-full"] environment: - ML4T_DATA_PATH=/data - ML4T_PATH=/app - TEST=${TEST:-0} # Database connections - CLICKHOUSE_HOST=ml4t-clickhouse - CLICKHOUSE_PORT=8123 - QUESTDB_HOST=ml4t-questdb - QUESTDB_HTTP_PORT=9000 - TIMESCALE_HOST=ml4t-timescaledb - TIMESCALE_PORT=5432 - TIMESCALE_PASSWORD=benchmark - INFLUXDB_HOST=ml4t-influxdb - INFLUXDB_PORT=8086 - INFLUXDB_ORG=ml4t - INFLUXDB_TOKEN=benchmark-token - INFLUXDB_BUCKET=market_data - POSTGRES_HOST=ml4t-postgres - POSTGRES_PORT=5432 - POSTGRES_USER=postgres - POSTGRES_PASSWORD=benchmark - POSTGRES_DB=ml4t # ============================================ # RAPIDS GBM BENCHMARK (NVIDIA GPU Required) # Ch12 GBM library benchmark: RAPIDS cuML XGBoost, LightGBM CUDA, # CatBoost GPU, plus CPU baselines. Single-purpose image. # Start with: docker compose --profile rapids run --rm rapids python 12_gradient_boosting/02_gbm_comparison.py # ============================================ rapids: <<: *common build: context: . dockerfile: envs/rapids/Dockerfile platforms: - linux/amd64 container_name: ml4t-rapids profiles: ["rapids"] user: root entrypoint: [] deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: [gpu] # ============================================ # PY312 (Python 3.12, x86 Only) # For packages without Python 3.14 support: gensim, signatory, esig, pfhedge, # tfcausalimpact, plus notebooks hitting the Python 3.14 torch CUDA import bug. # Covers: Ch05 NB01/03/07, Ch09 NB06/12, Ch10 NB01-03, Ch12 NB10, Ch14 NB06, # Ch15 NB06, Ch21 deep_hedging. # Start with: docker compose --profile py312 run --rm py312 python ... # Apple Silicon: there is no native arm64 image (signatory/esig/gensim have no # arm64 wheels). Either read the committed .ipynb outputs for the notebooks # above, or run the amd64 image under Rosetta emulation (slow, no GPU): # DOCKER_DEFAULT_PLATFORM=linux/amd64 docker compose --profile py312 run --rm py312 python ... # ============================================ py312: <<: *common image: ml4t/ml4t-py312:latest build: context: . dockerfile: envs/py312/Dockerfile platforms: - linux/amd64 container_name: ml4t-py312 profiles: ["py312"] deploy: resources: reservations: devices: - driver: nvidia count: 1 capabilities: [gpu] # ============================================ # KNOWLEDGE GRAPH (Neo4j Community Edition) # Start with: docker compose --profile kg up -d neo4j # ============================================ neo4j: image: neo4j:2026.02.2 container_name: ml4t-neo4j profiles: ["kg"] ports: - "7474:7474" - "7687:7687" environment: - NEO4J_AUTH=${NEO4J_AUTH:-neo4j/password} volumes: - neo4j_data:/data - neo4j_logs:/logs healthcheck: test: ["CMD-SHELL", "cypher-shell -u neo4j -p password 'RETURN 1;'"] interval: 10s timeout: 10s retries: 10 # ============================================ # Database Services (for benchmarks) # Start with: docker compose --profile databases up -d # ============================================ timescaledb: image: timescale/timescaledb:latest-pg16 container_name: ml4t-timescaledb environment: - POSTGRES_PASSWORD=benchmark - POSTGRES_DB=ml4t - POSTGRES_USER=postgres ports: - "5437:5432" profiles: ["benchmark", "benchmark-full", "databases"] healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 10s timeout: 5s retries: 5 postgres: image: postgres:16 container_name: ml4t-postgres environment: - POSTGRES_PASSWORD=benchmark - POSTGRES_DB=ml4t - POSTGRES_USER=postgres ports: - "5436:5432" profiles: ["benchmark", "benchmark-full", "databases"] healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 10s timeout: 5s retries: 5 clickhouse: image: clickhouse/clickhouse-server:latest container_name: ml4t-clickhouse environment: - CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1 - CLICKHOUSE_USER=default - CLICKHOUSE_PASSWORD= ports: - "8123:8123" profiles: ["benchmark", "benchmark-full", "databases"] deploy: resources: limits: memory: 4G healthcheck: test: ["CMD", "wget", "--spider", "-q", "http://localhost:8123/ping"] interval: 10s timeout: 5s retries: 5 questdb: image: questdb/questdb:latest container_name: ml4t-questdb ports: - "9000:9000" - "9009:9009" profiles: ["benchmark", "benchmark-full", "databases"] deploy: resources: limits: memory: 2G healthcheck: test: ["CMD", "curl", "-f", "http://localhost:9000"] interval: 10s timeout: 5s retries: 5 influxdb: image: influxdb:2.7 container_name: ml4t-influxdb environment: - DOCKER_INFLUXDB_INIT_MODE=setup - DOCKER_INFLUXDB_INIT_USERNAME=admin - DOCKER_INFLUXDB_INIT_PASSWORD=benchmark123 - DOCKER_INFLUXDB_INIT_ORG=ml4t - DOCKER_INFLUXDB_INIT_BUCKET=market_data - DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=benchmark-token ports: - "8086:8086" profiles: ["benchmark", "benchmark-full", "databases"] healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8086/health"] interval: 10s timeout: 5s retries: 5 networks: default: name: ml4t-review-network volumes: neo4j_data: neo4j_logs: