KFServing enables serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common machine learning (ML) frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX to solve production model serving use cases.
You can use KFServing to do the following:
Provide a Kubernetes Custom Resource Definition for serving ML models on arbitrary frameworks.
Encapsulate 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.
Enable a simple, pluggable, and complete story for your production ML inference server by providing prediction, pre-processing, post-processing and explainability out of the box.
Our strong community contributions help KFServing to grow. We have a Technical Steering Committee driven by Google, IBM, Microsoft, Seldon, and Bloomberg. Browse the KFServing GitHub repo to give us feedback!
Install with Kubeflow
KFServing works with Kubeflow 0.7. Kustomize installation files are located in the manifests repo.
We frequently add examples to our GitHub repo.
- Join our working group for meeting invitations and discussion.
- Read the docs.
- API docs.
- KFServing 101 slides.
Knative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on your Kubernetes cluster.
Read more about installing Knative on a Kubernetes cluster.
KFServing installation using kubectl
TAG=v0.2.0 kubectl apply -f ./install/$TAG/kfserving.yaml
Install the SDK.
pip install kfserving
Follow the example to use the KFServing SDK to create, patch, roll out, and delete a KFServing instance.
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