KFServing

Model serving using KFServing

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 Bloomberg, IBM Cloud, Seldon, Amazon Web Services (AWS) and NVIDIA. Browse the KFServing GitHub repo to give us feedback!

Install with Kubeflow

KFServing works with Kubeflow 1.3. Kustomize installation files are located in the manifests repo. Check the examples running KFServing on Istio/Dex in the kubeflow/kfserving repository. For installation on major cloud providers with Kubeflow, follow their installation docs.

Kubeflow 1.3 includes KFServing v0.5.1 which promoted the core InferenceService API from v1alpha2 to v1beta1 stable and added v1alpha1 version of Multi-Model Serving. Additionally, LFAI Trusted AI Projects on AI Fairness, AI Explainability and Adversarial Robustness have been integrated in KFServing, and we have made KFServing available on OpenShift as well. To know more, please read the release blog and follow the release notes

KFServing

Examples

Deploy models with out-of-the-box model servers

Deploy models with custom model servers

Deploy models on GPU

Autoscaling and Rollouts

Model explainability and outlier detection

Integrations

Model Storages

Sample notebooks

We frequently add examples to our GitHub repo.

Learn more

Standalone KFServing

Install Knative/Istio

Knative Serving (v0.17.4 +), Istio (v1.9+), and Cert Manager(v1.0.0+) should be available on your Kubernetes cluster. For installing KFServing prerequisites, refer to the README section.

KFServing installation

Once you meet the above prerequisites KFServing can be installed standalone. Check the KFServing install directory for other available releases.

Monitoring

Use SDK

  1. Install the SDK with PiPy.

    pip install kfserving
    
  2. Follow the example(s) to use the KFServing SDK to create, patch, roll out, and delete a KFServing instance.

Contribute