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clearml-serving is a command line utility for model deployment and orchestration. It enables model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based solution.


  • Easy to deploy and configure
    • Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
    • Support Deep Learning Models (TensorFlow, PyTorch, ONNX)
    • Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
  • Flexible
    • On-line model deployment
    • On-line endpoint model/version deployment (i.e. no need to take the service down)
    • Per model standalone preprocessing and postprocessing python code
  • Scalable
    • Multi model per container
    • Multi models per serving service
    • Multi-service support (fully separated multiple serving service running independently)
    • Multi cluster support
    • Out-of-the-box node autoscaling based on load/usage
  • Efficient
    • Multi-container resource utilization
    • Support for CPU and GPU nodes
    • Auto-batching for DL models
  • Automatic deployment
    • Automatic model upgrades with canary support
    • Programmable API for model deployment
  • Canary A/B deployment - online Canary updates
  • Model Monitoring
    • Usage Metric reporting
    • Metric Dashboard
    • Model performance metric
    • Model performance Dashboard


ClearML Serving

  • CLI - Secure configuration interface for on-line model upgrade/deployment on running Serving Services

  • Serving Service Task - Control plane object storing configuration on all the endpoints. Supports multiple separate instances, deployed on multiple clusters.

  • Inference Services - Inference containers, performing model serving pre/post-processing. Also supports CPU model inferencing.

  • Serving Engine Services - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference Services for heavier model inference.

  • Statistics Service - Single instance per Serving Service collecting and broadcasting model serving and performance statistics

  • Time-series DB - Statistics collection service used by the Statistics Service, e.g. Prometheus

  • Dashboards - Customizable dashboard solution on top of the collected statistics, e.g. Grafana

Grafana dashboard

Next Steps

See ClearML Serving setup instructions here. For further details, see the ClearML Serving Tutorial.