From abed5ccd8d187fb2951043ca629ba1b4d3923e8f Mon Sep 17 00:00:00 2001 From: simmscg <57739491+simmscg@users.noreply.github.com> Date: Thu, 14 Nov 2019 16:01:34 +1100 Subject: [PATCH] WtD fixit: Readability improvements for KFServing (#1399) * WtD fixit: Readability improvements for KFServing Fixed: https://github.com/kubeflow/website/issues/1396 Additional note: It's not clear what the "Examples" are of. I'm not familiar enough with the software to update the heading to something more appropriate, though. * Update kfserving.md * Update kfserving.md * Update kfserving.md --- content/docs/components/serving/kfserving.md | 45 +++++++++----------- 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/content/docs/components/serving/kfserving.md b/content/docs/components/serving/kfserving.md index 4cce8755..bcc0d5dc 100644 --- a/content/docs/components/serving/kfserving.md +++ b/content/docs/components/serving/kfserving.md @@ -1,33 +1,31 @@ +++ title = "KFServing" -description = "Model Serving using KFServing" +description = "Model serving using KFServing" weight = 2 +++ -KFServing enables Serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX to solve production model serving use cases +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. -KFServing: +You can use KFServing to do the following: -* Provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving ML models on arbitrary frameworks. +* Provide a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving ML models on arbitrary frameworks. -* 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 +* 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. -* Enables 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. +* 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. -* Is evolving with strong community contributions, and has a Technical Steering Committee driven by Google, IBM, Microsoft, Seldon, and Bloomberg +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](https://github.com/kubeflow/kfserving) to give us feedback! -Please browse through the [KFServing GitHub repo](https://github.com/kubeflow/kfserving) and give us feedback! - -## Installation with Kubeflow v0.7 ## -KFServing can be installed with Kubeflow v0.7, and KFServing kustomize installation files are [located in the manifests repo](https://github.com/kubeflow/manifests/tree/master/kfserving). +## Install with Kubeflow +KFServing works with Kubeflow 0.7. Kustomize installation files are [located in the manifests repo](https://github.com/kubeflow/manifests/tree/master/kfserving). KFServing ## Examples -* [Tensorflow](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorflow) +* [TensorFlow](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorflow) * [PyTorch](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch) * [XGBoost](https://github.com/kubeflow/kfserving/tree/master/docs/samples/xgboost) -* [Scikit-Learn](https://github.com/kubeflow/kfserving/tree/master/docs/samples/sklearn) +* [scikit-learn](https://github.com/kubeflow/kfserving/tree/master/docs/samples/sklearn) * [ONNX](https://github.com/kubeflow/kfserving/tree/master/docs/samples/onnx) * [Custom](https://github.com/kubeflow/kfserving/tree/master/docs/samples/custom) * [TensorRT](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorrt) @@ -36,24 +34,24 @@ KFServing can be installed with Kubeflow v0.7, and KFServing kustomize installat * [Pipelines](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pipelines) * [Explainability](https://github.com/kubeflow/kfserving/tree/master/docs/samples/explanation/alibi) -## Sample Notebooks -* [SDK Client](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) -* [Transformer (Pre/Post Processing)](https://github.com/kubeflow/kfserving/blob/master/docs/samples/transformer/image_transformer/kfserving_sdk_transformer.ipynb) +## Sample notebooks +* [SDK client](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) +* [Transformer (pre/post processing)](https://github.com/kubeflow/kfserving/blob/master/docs/samples/transformer/image_transformer/kfserving_sdk_transformer.ipynb) * [ONNX](https://github.com/kubeflow/kfserving/blob/master/docs/samples/onnx/mosaic-onnx.ipynb) -Please be on the lookout, we are constantly adding [more examples](https://github.com/kubeflow/kfserving/tree/master/docs/samples/) about available features +We frequently add examples to our [GitHub repo](https://github.com/kubeflow/kfserving/tree/master/docs/samples/). -## Learn More -* Join our [Working Group](https://groups.google.com/forum/#!forum/kfserving) for meeting invites and discussion. +## Learn more +* Join our [working group](https://groups.google.com/forum/#!forum/kfserving) for meeting invitations and discussion. * [Read the docs](https://github.com/kubeflow/kfserving/tree/master/docs). * [API docs](https://github.com/kubeflow/kfserving/tree/master/docs/apis/README.md). * [Roadmap](https://github.com/kubeflow/kfserving/tree/master/ROADMAP.md). * [KFServing 101 slides](https://drive.google.com/file/d/16oqz6dhY5BR0u74pi9mDThU97Np__AFb/view). ## Prerequisites -KNative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on Kubernetes Cluster. +Knative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on your Kubernetes cluster. -If you want to install Knative, you may find this [installation instruction](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#install-knative-on-a-kubernetes-cluster) useful. +Read more about [installing Knative on a Kubernetes cluster](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#install-knative-on-a-kubernetes-cluster). ## KFServing installation using kubectl ``` @@ -62,12 +60,11 @@ kubectl apply -f ./install/$TAG/kfserving.yaml ``` ## Use -* Install the SDK +1. Install the SDK. ``` pip install kfserving ``` -* Follow the [example](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) to use the KFServing SDK to create, patch, rollout and delete a KFService instance. +1. [Follow the example](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) to use the KFServing SDK to create, patch, roll out, and delete a KFServing instance. ## Contribute * [Developer guide](https://github.com/kubeflow/kfserving/tree/master/docs/DEVELOPER_GUIDE.md). - -- GitLab