# The name of the Nomad job.
job_name = "immich"

# The Nomad namespace to deploy into.
namespace = "default"

# The datacenters to deploy to.
datacenters = ["*"]

# The Immich server image. Pin a tag in production (keep it in sync with machine_learning_image).
image = "ghcr.io/immich-app/immich-server:release"

# The Immich machine-learning image. Keep its tag in sync with the server image.
machine_learning_image = "ghcr.io/immich-app/immich-machine-learning:release"

# The Immich PostgreSQL image. This is a special build with the required vector extensions — do not swap it for stock postgres.
postgres_image = "ghcr.io/immich-app/postgres:14-vectorchord0.4.3-pgvectors0.2.0"

# The Redis/Valkey image used as the job queue.
redis_image = "valkey/valkey:8-bookworm"

# Host port for the Immich web UI / API.
port = 2283

# Host port for the bundled PostgreSQL (loopback only).
db_port = 5432

# Host port for the bundled Redis/Valkey (loopback only).
redis_port = 6379

# Host port for the machine-learning service (loopback only). Fixed at 3003 inside the image.
ml_port = 3003

# PostgreSQL username.
db_user = "postgres"

# PostgreSQL password. CHANGE THIS. Use alphanumeric characters only (no special characters).
db_password = "change-me-immich-postgres"

# PostgreSQL database name.
db_name = "immich"

# Named volume mounted at /data — your photo and video library. This grows large; back it up.
upload_volume = "immich_upload"

# Named volume for the PostgreSQL data directory.
db_data_volume = "immich_db"

# Named volume for the machine-learning model cache (/cache).
model_cache_volume = "immich_model_cache"

# Named volume for Redis/Valkey persistence (/data).
redis_data_volume = "immich_redis"

# Placement constraints. Pin to the node holding the volumes. On a nomploy cluster: attribute = "$${meta.nomploy_control_plane}", operator = "=", value = "true".
constraints = []

# Resources for the Immich server task.
resources = {
    cpu    = 1000
    memory = 1024
  }

# Resources for the machine-learning task. Model inference is CPU/RAM heavy.
machine_learning_resources = {
    cpu    = 1000
    memory = 1536
  }

# Resources for the PostgreSQL task.
postgres_resources = {
    cpu    = 500
    memory = 512
  }

# Resources for the Redis/Valkey task.
redis_resources = {
    cpu    = 200
    memory = 128
  }