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
- 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)
- 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
- 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
- Multi-container resource utilization
- Support for CPU and GPU nodes
- Auto-batching for DL models
- Automatic deployment
- Automatic model upgrades w/ 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
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