clearml-docs/docs/clearml_serving/clearml_serving.md

title
Introduction

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.

Features

• 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

Components

• 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

Next Steps

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