The project is configured through environment variables defined in a .env file
at the project root. Docker Compose reads this file and injects the variables
into each service container.
Label identifying the container hardware config (e.g. CPU, GPU_GTX1060_6GB). Set in Docker Compose per service, read by the performance monitor and VQE runner for tagging results.
MLflow tracking server URI. Used by the quantum_pipeline.ml.tracking module to locate the tracking server. Not set in .env.ml.example (defaults to http://localhost:5000 in code).
These variables control the performance monitoring subsystem. The code default for MONITORING_ENABLED is false (in settings.py), but .env.ml.example sets it to true so monitoring is on by default in Docker Compose deployments.
Variable
Default
Description
MONITORING_ENABLED
false (code) / true (.env example)
Enable performance metric collection. Accepts true, 1, yes, on.
MONITORING_INTERVAL
10
Metric collection interval in seconds.
PUSHGATEWAY_URL
http://localhost:9091
Prometheus PushGateway URL for pushing performance metrics.
MONITORING_EXPORT_FORMAT
prometheus
Export format: json, prometheus, or both (comma-separated, e.g. json,prometheus).
GID of the docker group on the host. Used to build the Airflow image so it can access the Docker socket for batch generation DAGs. Check with stat -c '%g' /var/run/docker.sock.
CUDA compute capabilities for the GPU image build. The Dockerfile.gpu default covers Pascal to Hopper and Blackwell (via PTX). A single value builds about 4x faster and is only useful for local builds; .env.ml.example sets 6.1 (Pascal) to match the project's test hardware.
QUANTUM_PIPELINE_HOST_ROOT
$HOME/code/quantum-pipeline
Absolute path to the quantum-pipeline repo on the host. Used by docker-compose.ml.yaml and the batch generation script (scripts/generate_ml_batch.py) to resolve volume mount paths. The compose file defaults to the maintainer's local path; override this for your own system.