Update docs crd

docs/crd/readme.md

@@ -0,0 +1,77 @@
+# Custom Resource Definitions
+
+## Seldon Deployment
+
+The runtime inference graph for a machine learning deployment is described as a SeldonDeployment Kubernetes resource. The structure of this manifest is defined as a [proto buffer](../reference/seldon-deployment.md). This doc will describe the SeldonDeployment resource in general and how to create one for your runtime inference graph.
+
+## Creating your resource definition
+
+The full specification can be found [here](../reference/seldon-deployment.md). Below we highlight various parts and describe their intent.
+
+The core goal is to describe your runtime inference graph(s) and deploy it with appropriate resources and scale. Example illustrative graphs are shown below:
+
+![graph](../reference/graph.png)
+
+
+The core deployment spec consists of a set of ```predictors```. Each predictor represents a seperate runtime serving graph. To allow an OAuth API to be provisioned you should specify an OAuth key and secret.
+
+```proto
+
+message DeploymentSpec {
+  optional string name = 1; // A unique name within the namespace.
+  repeated PredictorSpec predictors = 2; // A list of 1 or more predictors describing runtime machine learning deployment graphs.
+  optional string oauth_key = 6; // The oauth key for external users to use this deployment via an API.
+  optional string oauth_secret = 7; // The oauth secret for external users to use this deployment via an API.
+  map<string,string> annotations = 8; // Arbitrary annotations.
+}
+
+```
+
+For each predictor you should at a minimum specify:
+
+ * A unique name
+ * A PredictiveUnit graph that presents the tree of components to deploy.
+ * A componentSpec which describes the set of images for parts of your container graph that will be instigated as microservice containers. These containers will have been wrapped to work within the [internal API](../reference/internal-api.md). This component spec is a standard [PodTemplateSpec](https://kubernetes.io/docs/api-reference/extensions/v1beta1/definitions/#_v1_podtemplatespec).
+     * If you leave the ports empty for each container they will be added automatically and matched to the ports in the graph specification. If you decide to specify the ports manually they should match the port specified for the matching component in the graph specification.
+ * the number of replicas of this predictor to deploy
+
+```proto
+
+message PredictorSpec {
+  required string name = 1; // A unique name not used by any other predictor in the deployment.
+  required PredictiveUnit graph = 2; // A graph describing how the predictive units are connected together.
+  required k8s.io.api.core.v1.PodTemplateSpec componentSpec = 3; // A description of the set of containers used by the graph. One for each microservice defined in the graph.
+  optional int32 replicas = 4; // The number of replicas of the predictor to create.
+  map<string,string> annotations = 5; // Arbitrary annotations.
+}
+
+```
+
+The predictive unit graph is a tree. Each node is of a particular type. If the implementation is not specified then a microservice is assumed and you must define a matching named container within the componentSpec above. Each type of PredictiveUnit has a standard set of methods it is expected to manage, see [here](../reference/seldon-deployment.md). 
+
+For each node in the graph:
+
+ * A unique name. If the node describes a microservice then it must match a named container with the componentSpec.
+ * The children nodes.
+ * The type of the predictive unit : MODEL, ROUTER, COMBINER, TRANSFORMER or OUTPUT_TRANSFORMER.
+ * The implementation. This can be left blank if it will be a microserice as this is the default otherwise choose from the available appropriate implementations provided internally.
+ * Methods. This can be left blank if you wish to follow the standard methods for your PredictiveNode type : see [here](../reference/seldon-deployment.md). 
+ * Endpoint. In here you should minimally if this a microservice specify whether the PredictiveUnit will use REST or gRPC. Ports will be defined automatically if not specified.
+ * Parameters. Specify any parameters you wish to pass to the PredictiveUnit. These will be passed in an environment variable called PREDICTIVE_UNIT_PARAMETERS as a JSON list.
+
+```proto
+
+message PredictiveUnit {
+
+
+  required string name = 1; //must match container name of component if no implementation
+  repeated PredictiveUnit children = 2; // The child predictive units.
+  optional PredictiveUnitType type = 3;
+  optional PredictiveUnitImplementation implementation = 4;
+  repeated PredictiveUnitMethod methods = 5;
+  optional Endpoint endpoint = 6; // The exposed endpoint for this unit.
+  repeated Parameter parameters = 7; // Customer parameter to pass to the unit.
+}
+
+
+```

docs/developer/readme.md

@@ -0,0 +1,12 @@
+# Developer
+
+We welcome new contributors. Please read the [code of conduct](../../CODE_OF_CONDUCT.md) and [contributing guidelines](../../CONTRIBUTING.md)
+
+## Release process
+
+To be completed.
+
+## Tools we use
+
+ - [github-changelog-generator](https://github.com/skywinder/github-changelog-generator)
+ - [Grip - Local Markdown viewer](https://github.com/joeyespo/grip)

readme.md

@@ -52,10 +52,16 @@ helm install seldon-core --name seldon-core \
 ![API](./docs/deploy.png)
 
  - [Wrap your runtime prediction model](./docs/wrappers/readme.md).
- - Define your runtime inference graph in a seldon deployment custom resource.
+ - [Define your runtime inference graph in a seldon deployment custom resource](./docs/crd/readme.md).
  - Deploy.
 
 ## Reference
 
  - [Prediction API](./docs/reference/prediction.md)
  - [Seldon Deployment Custom Resource](./docs/reference/seldon-deployment.md)
+
+
+## Developer
+
+ - [CHANGELOG](CHANGELOG.md)
+ - [Developer Guide](./docs/developer/readme.md)