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 & 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 seperated multiple serving service running independently)
  • Multi cluster support
  • Out-of-the-box node auto-scaling based on load/usage
• Efficient
  • Multi-container resource utilization
  • Support for CPU & 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

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. Support multiple separated instance, 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 & 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

Installation

Prerequisites

• ClearML-Server : Model repository, Service Health, Control plane
• Kubernetes / Single-instance Machine : Deploying containers
• CLI : Configuration & model deployment interface

Initial Setup

1. Set up your ClearML Server or use the free hosted service

2. Connect clearml SDK to the server, see instructions here

3. Install clearml-serving CLI:

   pip3 install clearml-serving
   

4. Create the Serving Service Controller:

   clearml-serving create --name "serving example"
   

   The new serving service UID should be printed

   New Serving Service created: id=aa11bb22aa11bb22
   

   Write down the Serving Service UID

5. Clone the clearml-serving repository:

   git clone https://github.com/allegroai/clearml-serving.git
   

6. Edit the environment variables file (docker/example.env) with your clearml-server credentials and Serving Service UID. For example, you should have something like

   cat docker/example.env
   

    CLEARML_WEB_HOST="https://app.clear.ml"
    CLEARML_API_HOST="https://api.clear.ml"
    CLEARML_FILES_HOST="https://files.clear.ml"
    CLEARML_API_ACCESS_KEY="<access_key_here>"
    CLEARML_API_SECRET_KEY="<secret_key_here>"
    CLEARML_SERVING_TASK_ID="<serving_service_id_here>"
   

7. Spin up the clearml-serving containers with docker-compose (or if running on Kubernetes, use the helm chart)

   cd docker && docker-compose --env-file example.env -f docker-compose.yml up
   

   If you need Triton support (keras/pytorch/onnx etc.), use the triton docker-compose file

   cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up 
   

   If running on a GPU instance w/ Triton support (keras/pytorch/onnx etc.), use the triton gpu docker-compose file:

   cd docker && docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up
   

:::note Any model that registers with Triton engine will run the pre/post processing code on the Inference service container, and the model inference itself will be executed on the Triton Engine container. :::

Advanced Setup - S3/GS/Azure Access (Optional)

To add access credentials and allow the inference containers to download models from your S3/GS/Azure object-storage, add the respective environment variables to your env files (example.env). See further details on configuring the storage access here.

AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_DEFAULT_REGION

GOOGLE_APPLICATION_CREDENTIALS

AZURE_STORAGE_ACCOUNT
AZURE_STORAGE_KEY

Tutorial

For further details, see the ClearML Serving Tutorial.