mlflow-triton-control

MLflow deployment target for models that already exist in a Triton S3 model repository. Install this Python package in the environment running the MLflow CLI or Python API. Triton Control creates the Kubernetes deployment and resolves the selected user-owned S3 profile.

Requires Python 3.10+ and MLflow >=3.14,<4. Automatic CI tests the version pinned in charts/triton-control/values.yaml.

python -m build
python -m pip install dist/mlflow_triton_control-*.whl
mlflow deployments help -t triton-control

From this directory, run the tests after installing the wheel:

python -m pip check
python -m unittest discover -s tests -v

The smoke tests verify installed plugin discovery, deployment requests with a mock HTTP transport, and real model logging, registration, and retrieval with a temporary SQLite tracking database.

Smoke tests

Run from the repository root; each command uses a fresh temporary environment:

# Test a specific MLflow version
bash plugins/mlflow-triton-control/smoke-latest.sh 3.14.0

# Test the latest supported MLflow version
bash plugins/mlflow-triton-control/smoke-latest.sh

Replace 3.14.0 with the version to test. No live Kubernetes cluster is needed. Manual CI runs accept an additional version through mlflow_version.

How MLflow finds the plugin

mlflow deployments create is a command provided by MLflow. This package registers the deployment target through its Python package metadata:

[project.entry-points."mlflow.deployments"]
triton-control = "mlflow_triton_control.deployment_client"

For --target triton-control://triton-control:8000, MLflow looks up the triton-control entry point among installed packages, loads this module, and calls TritonControlDeploymentClient.create_deployment(). That class inherits from MLflow's BaseDeploymentClient and sends the deployment request to the Triton Control API. MLflow reads entry-point metadata; it does not search through every package's source files. The package must be installed in the same Python environment as the mlflow CLI.

The short hostname works when the client runs in the Service's namespace. For another namespace, use triton-control://<service>.<namespace>.svc.cluster.local:8000, for example triton-control://triton-control.blabla.svc.cluster.local:8000. Use the actual Service name created by your Helm release.

Log a Triton model in MLflow

Like NVIDIA's Triton MLflow plugin, this package provides a triton model flavor for a complete Triton model directory. The directory must contain at least one numbered version; config.pbtxt may be included at its root. The MLflow Model contains an MLmodel file with a triton flavor entry and a copy of the entire directory under model/.

from mlflow_triton_control import triton

model_info = triton.log_model(
    triton_model_path="/tmp/model-repository/iris_classifier",
    name="triton-model",
    registered_model_name="iris-serving",
)
print(model_info.registered_model_version)

triton.load_model("models:/iris-serving/1") downloads the registered model and returns the local Triton model directory. Omit registered_model_name to save a model without creating a Registry version. This API is in the plugin package, not in MLflow's mlflow.triton namespace.

The current deployment client still accepts an already-published s3:// Triton repository URI. Deploying directly from models:/... and copying that version to the selected S3 profile are separate planned changes. The current Iris workflow therefore continues to use the S3 URI.

Argo Workflows

Submit the workflow through Triton Control's authenticated Argo proxy. Mark the deployment template with these Workflow annotations:

metadata:
  name: train-iris
  annotations:
    triton-control.ai/mlflow-deploy-template: deploy
    triton-control.ai/mlflow-deployment-name: iris-classifier
    triton-control.ai/mlflow-s3-profile-name: workflow-training

The backend resolves the profile name and its linked Argo artifact repository for the submitting user. It sets spec.artifactRepositoryRef and injects TRITON_CONTROL_TOKEN, TRITON_CONTROL_S3_PROFILE_ID, and TRITON_CONTROL_S3_BUCKET into the named script or container template. Use the injected ID with -C s3_profile_id="$TRITON_CONTROL_S3_PROFILE_ID". The token is valid for 60 minutes and limited to the specified deployment and S3 profile. The Workflow receives a five-minute completion TTL unless it already defines one, and the Secret receives a Workflow owner reference for garbage collection.

The opted-in Workflow must have a fixed metadata.name and run in the Triton Control namespace. Direct submissions to Argo bypass this token injection.

S3 deployment of logged models (0.3.0)

The Triton flavor now stores data under model/<original-model-name>/ inside the MLflow Model. Pass the concrete S3 URI of that subdirectory to the deployment plugin, using an S3 profile that can read the artifact bucket. Resolve proxy artifact locations against the managed server's S3 root; the deployment client continues to accept only S3 URIs. Existing models using data: model remain loadable. See the Iris auto-deployment workflow for the complete example.

Publishing

Publish manually from the repository root, using a fresh output directory for each release:

python -m pip install --upgrade build twine
python -m build --outdir dist/mlflow-triton-control-0.3.0 plugins/mlflow-triton-control
python -m twine check dist/mlflow-triton-control-0.3.0/*
python -m twine upload dist/mlflow-triton-control-0.3.0/*

When prompted, enter __token__ as the username and a PyPI API token scoped to mlflow-triton-control as the password.