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**ClearML Serving - Model deployment made easy**
## **`clearml-serving` </br> Model Serving (ML/DL) Orchestration and Repository Made Easy**
## **`clearml-serving` </br> :sparkles: Model Serving (ML/DL) Made Easy :tada:**
[![GitHub license](https://img.shields.io/github/license/allegroai/clearml-serving.svg)](https://img.shields.io/github/license/allegroai/clearml-serving.svg)
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* 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)
* Flexibility
* 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
* Scalability
* Scalable
* Multi model per container
* Multi models per serving service
* Multi-service support (fully seperated multiple serving service running independently)
* Multi cluster support
* Out-of-the-box node auto-scaling based on load/usage
* Efficiency
* Efficient
* multi-container resource utilization
* Support for CPU & GPU nodes
* Auto-batching for DL models
@ -55,11 +55,7 @@ Features:
### ClearML Serving Design Principles
* Modular
* Scalable
* Flexible
* Customizable
* Open Source
**Modular** , **Scalable** , **Flexible** , **Customizable** , **Open Source**
<a href="https://excalidraw.com/#json=v0ip945hun2SnO4HVLe0h,QKHfB04TFQLds3_4aqeBjQ"><img src="https://github.com/allegroai/clearml-serving/blob/dev/docs/design_diagram.png?raw=true" width="100%"></a>
@ -67,13 +63,19 @@ Features:
### Concepts
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. Support multiple separated instance, deployed on multiple clusters.
Inference Services - Inference containers, performing model serving pre/post processing. Also support 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 & performance statistics
Time-series DB - Statistics collection service used by the Statistics Service, e.g. Prometheus
Dashboard Service - Customizable dashboard-ing solution on top of the collected statistics, e.g. Grafana
**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. Support multiple separated instance, deployed on multiple clusters.
**Inference Services** - Inference containers, performing model serving pre/post processing. Also support 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 & performance statistics
**Time-series DB** - Statistics collection service used by the Statistics Service, e.g. Prometheus
**Dashboards** - Customizable dashboard-ing solution on top of the collected statistics, e.g. Grafana
### prerequisites