Configuration, Daemon Runner & Worker Scaffolding

The snoozeml.config module enables declarative programmatic generation of models.toml and models.json files, provides an automated local daemon runner for testing, and scaffolds process-isolated custom Python workers.

Programmatic Configuration Builder (SnoozeConfig)

Generate configuration files programmatically using a fluent builder:

from snoozeml import SnoozeConfig, GlobalDefaultsConfig

config = (
    SnoozeConfig(defaults=GlobalDefaultsConfig(max_loaded_models=8, idle_timeout_secs=60))
    # Register an ONNX model
    .add_model("bert_embeddings", "bert.onnx", pinned=True, max_batch_size=32)
    # Register an XGBoost tree model
    .add_model("fraud_detector", "fraud.xgb.json", idle_timeout_secs=120)
    # Configure RBAC API keys
    .add_api_key(role="admin", key_env="SNOOZE_ADMIN_KEY", name="cluster-admin")
    .add_api_key(role="inference", key_env="SNOOZE_INFER_KEY", name="scoring-pod")
    # Configure HMAC JWT authentication
    .set_jwt(secret_env="SNOOZE_JWT_SECRET", algorithm="HS256")
    # Bind an event streaming pipeline
    .add_stream(
        name="fraud-stream",
        broker="redis",
        endpoint="redis://127.0.0.1:6379",
        input_topic="transactions.in",
        output_topic="transactions.out",
        error_topic="transactions.dlq",
        model_id="fraud_detector",
    )
)

# Save configuration to disk
config.save("models/models.toml")

Automated Local Daemon Context Manager (SnoozeDaemon)

Launch an ephemeral SnoozeML daemon subprocess directly from Python. When the context block exits, the daemon is cleanly terminated:

from snoozeml import SnoozeDaemon

# Automatically finds 'snooze' binary in target/release, target/debug, or PATH
with SnoozeDaemon(models_dir="models", port=8080) as client:
    # client is a fully initialized SnoozeClient connected to the ephemeral server!
    client.wait_until_ready(timeout=10.0)

    response = client.infer("iris_classifier", {"input": [[5.1, 3.5, 1.4, 0.2]]})
    print(f"Result: {response.get_numpy('output')}")

Custom Python Worker Scaffolding (scaffold_worker)

Process-isolated Python workers allow running proprietary models or frameworks (such as PyTorch generative networks or Amazon Chronos-Bolt) inside an isolated child process with IPC framing:

from snoozeml import scaffold_worker

# Scaffolds models/custom_worker/snooze_model.py and snooze_model.json
# and runs an isolated smoke test to verify IPC protocol compliance
scaffold_worker(
    target_dir="models/custom_worker",
    tensor_format="numpy",
    verify=True,
)

To inspect or verify an existing worker from the command line:

python -m snoozeml.worker --check models/custom_worker