.env.example

# Slurm Docker Cluster - Environment Configuration

# Copy this file to .env and customize as needed:
#   cp .env.example .env
#
# If .env doesn't exist, defaults are used automatically.

# Docker Compose project name (affects container and volume name prefixes)
COMPOSE_PROJECT_NAME=slurm

# Slurm version (semantic version format)
# Supported versions: 26.05.x, 25.11.x
# This is used for:
#   - Downloading the Slurm tarball from schedmd.com
#   - Tagging the Docker image
#   - Selecting version-specific configuration files
SLURM_VERSION=26.05.2

# Lmod version (https://github.com/TACC/Lmod/releases)
LMOD_VERSION=9.1.2

# Spack version (https://github.com/spack/spack/releases)
# Spack is cloned at image build time into /usr/local/spack (baked into the image).
# Installed packages and generated Lmod modules are stored in the spack_root volume (/opt/spack).
SPACK_VERSION=v1.1.1

# SlurmDB MySQL credentials
# Default values shown below (suitable for local development/testing only)
MYSQL_USER=slurm
MYSQL_PASSWORD=password
MYSQL_DATABASE=slurm_acct_db

# Elasticsearch for job completion logging (OPTIONAL)
# Uncomment and set to enable monitoring with Elasticsearch/Kibana
# When set, 'make up' automatically starts monitoring containers
# Leave commented for default file-based job completion logging
#ELASTICSEARCH_HOST=http://elasticsearch:9200

# SSHD options
SSH_ENABLE=false
SSH_AUTHORIZED_KEYS=$HOME/.ssh/authorized_keys
SSH_PORT=3022

# CPU worker nodes (scale with: make scale-cpu-workers N=5)
CPU_WORKER_COUNT=2

# Open OnDemand web portal (OPTIONAL)
# Uncomment to enable the Open OnDemand web interface
# When enabled, 'make up' automatically starts the OOD container
# Access at: http://localhost:8080 (login: ood@localhost / password)
#OOD_ENABLE=true
#OOD_PORT=8080

# GPU worker nodes (OPTIONAL)
# Host requirement: nvidia-container-toolkit (one-time install)
# Uses NVIDIA's official CUDA base images (no host CUDA needed)
# When enabled, 'make up' automatically starts GPU workers with GRES configuration
# Scale with: make scale-gpu-workers N=2
#
# Browse available tags at: https://hub.docker.com/r/nvidia/cuda/tags
#GPU_ENABLE=true
#GPU_WORKER_COUNT=1
#BUILDER_BASE=nvidia/cuda:13.1.1-devel-rockylinux9
#RUNTIME_BASE=nvidia/cuda:13.1.1-base-rockylinux9
# Use to disable GPU autodetection on WSL
#IS_WSL=true