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Seldon Core API

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release-0.2 Build Status
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Seldon Core is an open source platform for deploying machine learning models on Kubernetes.

Goals

Machine learning deployment has many challenges. Seldon Core intends to help with these challenges. Its high level goals are:

  • Allow data scientists to create models using any machine learning toolkit or programming language. We plan to initially cover the tools/languages below:
    • Python based models including
      • Tensorflow models
      • Sklearn models
    • Spark models
    • H2O models
    • R models
  • Expose machine learning models via REST and gRPC automatically when deployed for easy integration into business apps that need predictions.
  • Allow complex runtime inference graphs to be deployed as microservices. These graphs can be composed of:
    • Models - runtime inference executable for machine learning models
    • Routers - route API requests to sub-graphs. Examples: AB Tests, Multi-Armed Bandits.
    • Combiners - combine the responses from sub-graphs. Examples: ensembles of models
    • Transformers - transform request or responses. Example: transform feature requests.
  • Handle full lifecycle management of the deployed model:
    • Updating the runtime graph with no downtime
    • Scaling
    • Monitoring
    • Security

Prerequisites

A Kubernetes Cluster.
Kubernetes can be deployed into many environments, both in cloud and on-premise.

Important: V1Alpha2 Update

We have updated our core API to v1alpha2 which has a breaking change from v1alpha1 in the SeldonDeployments CRD

Read details of how to update your kubernetes SeldonDeployment resources.

  • 0.2 releases will now respect the v1alpha2 API.
  • 0.1 releases respect the v1alpha1 API and will not be worked on further.

It is possible to deploy Seldon with two operators that can handle both v1alpha1 resources and v1alpha2 resources though this is not part of our standard deployment docs. If you need this please get in touch.

Quick Start

Read the overview to using seldon-core.

Example Components

Seldon-core allows various types of components to be built and plugged into the runtime prediction graph. These include models, routers, transformers and combiners. Some example components that are available as part of the project are:

Integrations

Install

Follow the install guide for details on ways to install seldon onto your Kubernetes cluster.

Deployment Guide

API

Three steps:

  1. Wrap your runtime prediction model.
    • We provide easy to use wrappers for python, R and Java
  2. Define your runtime inference graph in a seldon deployment custom resource.
  3. Deploy the graph.

Advanced Tutorials

Reference

Articles/Blogs/Videos

Testing

Configuration

Community

Developer

Latest Seldon Images

Description Image URL Stable Version Development
Seldon Operator seldonio/cluster-manager 0.2.2 0.2.3-SNAPSHOT
Seldon Service Orchestrator seldonio/engine 0.2.2 0.2.3-SNAPSHOT
Seldon API Gateway seldonio/apife 0.2.2 0.2.3-SNAPSHOT
Seldon Python 3 Wrapper for S2I seldonio/seldon-core-s2i-python3 0.1 0.2-SNAPSHOT
Seldon Python 2 Wrapper for S2I seldonio/seldon-core-s2i-python2 0.1 0.2-SNAPSHOT
Seldon Core Python Wrapper seldonio/core-python-wrapper 0.7
Seldon Java Build Wrapper for S2I seldonio/seldon-core-s2i-java-build 0.1
Seldon Java Runtime Wrapper for S2I seldonio/seldon-core-s2i-java-runtime 0.1
Seldon R Wrapper for S2I seldonio/seldon-core-s2i-r 0.1

Java Packages

Description Package Version
Seldon Core Wrapper seldon-core-wrapper 0.1.2
Seldon Core JPMML seldon-core-jpmml 0.0.1

Usage Reporting

Tools that help the development of Seldon Core from anonymous usage.

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Machine Learning Deployment for Kubernetes

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