seldon-core userguide. #91 @banzo

doc/SELDON.md

@@ -0,0 +1,81 @@
+Seldon
+==========
+
+<p align="left";>
+	<a href="https://www.elastic.co" alt="elk">
+	    <img src="./images/logos/seldon_logo.jpg" align="center" alt="ELK logo" width="200px" />
+    </a>
+</p>
+
+[Seldon Core](https://www.seldon.io/tech/products/core/https://www.seldon.io/tech/products/core/) is an open source platform for deploying machine learning models on a Kubernetes cluster. It extends Kubernetes with **its own custom resource SeldonDeployment** where you can define your runtime inference graph made up of models and other components that Seldon will manage.
+
+
+##Install Seldon Core
+
+#### 1. Deploy Seldon core
+
+Set seldon-core-operator.enabled to `true` in the `values.yaml`.
+
+
+## Deploy your model
+
+#### 1. Wrap your model ( Dockerize it )
+
+To allow your component (model, router etc.) to be managed by seldon-core it needs to be built into a **Docker container** and to expose the appropriate [service microservice APIs over REST or gRPC](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/internal-api.html).
+
+To wrap your model follow these [instructions](https://docs.seldon.io/projects/seldon-core/en/latest/wrappers/README.html).
+
+#### 2. Create your inference graph
+
+Seldon Core extends Kubernetes with its own custom resource **SeldonDeployment** where you can define your runtime [inference graph](https://docs.seldon.io/projects/seldon-core/en/latest/graph/inference-graph.html) made up of models and other components that Seldon will manage. 
+
+A **SeldonDeployment** is a JSON or YAML file that allows you to define your graph of component images and the resources each of those images will need to run (using a Kubernetes PodTemplateSpec). The following is minimal example for a single model,  in YAML:
+
+```
+apiVersion: machinelearning.seldon.io/v1alpha2
+kind: SeldonDeployment
+metadata:
+  name: seldon-model
+spec:
+  name: test-deployment
+  predictors:
+  - componentSpecs:
+    - spec:
+        containers:
+        - name: classifier
+          image: seldonio/mock_classifier:1.0
+    graph:
+      children: []
+      endpoint:
+        type: REST
+      name: classifier
+      type: MODEL
+    name: example
+    replicas: 1
+```
+The key components are:
+
+* A list of Predictors, each with a specification for the number of replicas.
+	* Each defines a graph and its set of deployments. Multiple predictors is useful when you want to split traffic between a main graph and a canary or for other production rollout scenarios.
+* For each predictor a list of componentSpecs. Each componentSpec is a Kubernetes PodTemplateSpec which Seldon will build into a Kubernetes Deployment. Place here the images from your graph and their requirements, e.g. Volumes, ImagePullSecrets, Resources Requests etc.
+* A graph specification that describes how your components are joined together.
+
+To understand the inference graph definition in detail see [here](https://docs.seldon.io/projects/seldon-core/en/latest/reference/seldon-deployment.html)
+
+#### 3. Deploy your model
+
+ Once you have created your inference graph as a JSON or YAML Seldon Deployment resource ( the previous step ) you can deploy to Kubernetes with kubectl:
+
+```
+kubectl apply -f my_deployment.yaml
+```
+To delete ( or manage ) your **SeldonDeployment** you can use kubectl for the custom resource SeldonDeployment, for example to see if there are any models deployed:
+
+```
+kubectl get seldondeployment
+```
+to delete the model `seldon-model`:
+
+```
+kubectl delete seldondeployment seldon-model
+```