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Serverless Inferencing on Kubernetes
Jsonnet Go Python Other
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.github Added PR and Issue template. (#88) May 14, 2019
cmd V1alpha2 KFService Spec for predict, explain, transform (#300) Aug 26, 2019
config Remove default azure secret name (#315) Aug 27, 2019
docs Remove default azure secret name (#315) Aug 27, 2019
hack update sdk to v1alpha2 and handle changes for new knative and types (#… Aug 22, 2019
install/v0.1.0 Fix same typo in 0.1 install yaml Jul 10, 2019
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python add API watch for SDK client (#305) Aug 27, 2019
release Fix Docker Build Context for Executor (#275) Jul 31, 2019
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tools/tf2openapi TF2OpenAPI: Complete openapi (#261) Jul 22, 2019
vendor Update knative to Release 0.8 (#282) Aug 12, 2019
.gitignore Bundling kserving backend and client in the same pypi (#250) Aug 13, 2019
CONTRIBUTING.md Fixes a markdown formatting issue in CONTRIBUTING (#42) Apr 21, 2019
Dockerfile Initial implementation of the kfserving spec (#12) Apr 12, 2019
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Makefile Update KFServing type API from v1alpha1 to v1alpha2 (#290) Aug 19, 2019
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PROJECT Added kubebuilder boilerplate (#5) Apr 3, 2019
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README.md

KFServing

KFServing provides a Kubernetes Custom Resource Definition for serving ML Models on arbitrary frameworks. It aims to solve 80% of model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.

KFServing encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Mission Critical ML including inference, explainability, outlier detection, and prediction logging.

Learn More

Install

TAG=v0.1.0
kubectl apply -f ./install/$TAG/kfserving.yaml

Use

  • Install the SDK
pip install kfserving
  • Follow the example here to use the KFServing SDK to create, patch, and delete a KFService instance.

Contribute

KFServing

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