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stefan-jansen--machine-lear…/docker-compose.yml
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# 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:
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