Link to new docs across our gh repo (#6448)

* Link to new docs

* Remove broken link

* Update ansible.md removing old links

* Update k8s-pvc.ipynb

* Update k8s-pvc.md

Author: paulb-seldon

README.md

@@ -78,8 +78,8 @@ make undeploy-k8s
 
 ## Documentation
 
-[Seldon Core 2 docs](https://docs.seldon.io/projects/seldon-core/en/v2/)
+[Seldon Core 2 docs](https://docs.seldon.ai/seldon-core-2)
 
 ## License
 
-[License](LICENSE)
+[License](LICENSE)

ansible/README.md

@@ -236,7 +236,4 @@ You may want to also remove cache resources used for the installation with
 rm -rf ~/.cache/seldon/
 ```
 
-```{note}
 If you used Ansible to install Seldon Core 2 and its ecosystem into K8s cluster other than Kind you need to manually remove all the components.
-Notes on how to remove Seldon Core 2 Helm installation itself you can find [here](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/kubernetes-installation/helm.html#uninstall).
-```

docs-gb/getting-started/kubernetes-installation/ansible.md

@@ -2,7 +2,7 @@
 
 {% hint style="warning" %}
 **Warning**: The Ansible installation of a Seldon Core and associated ecosystem is meant for **dev/testing** purposes.
-For production use cases follow [Helm installation](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/kubernetes-installation/helm.html).
+For production use cases follow [Helm installation](https://docs.seldon.ai/seldon-core-2/installation/production-environment).
 {% endhint %}
 
 ## Installing Ansible
@@ -241,5 +241,4 @@ rm -rf ~/.cache/seldon/
 
 {% hint style="info" %}
 If you used Ansible to install Seldon Core 2 and its ecosystem into K8s cluster other than Kind you need to manually remove all the components.
-Notes on how to remove Seldon Core 2 Helm installation itself you can find [here](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/kubernetes-installation/helm.html#uninstall).
 {% endhint %}

samples/examples/k8s-pvc/k8s-pvc.ipynb

@@ -96,7 +96,7 @@
    "id": "32da65b2",
    "metadata": {},
    "source": [
-    "[**Now you should finish the Seldon install following the docs.**](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/index.html)"
+    "[**Now you should finish the Seldon install following the docs.**](https://docs.seldon.ai/seldon-core-2/installation/production-environment)"
    ]
   },
   {

samples/examples/k8s-pvc/k8s-pvc.md

@@ -1,36 +1,36 @@
-## Kubernetes PVC Example
-
-
-```python
-import os
-os.environ["NAMESPACE"] = "seldon-mesh"
-```
-
-
-```python
-MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
-MESH_IP=MESH_IP[0]
-import os
-os.environ['MESH_IP'] = MESH_IP
-MESH_IP
-```
-
-
-
-
-    '172.19.255.1'
-
-
-
-### Kind cluster setup
-
-To run this example in Kind we need to start Kind with access to a local folder where are models are location. In this example we will use a folder in `/tmp` and associate that with a path in the container.
-
-
-```python
-!cat kind-config.yaml
-```
-
+## Kubernetes PVC Example
+
+
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+
+
+
+    '172.19.255.1'
+
+
+
+### Kind cluster setup
+
+To run this example in Kind we need to start Kind with access to a local folder where are models are location. In this example we will use a folder in `/tmp` and associate that with a path in the container.
+
+
+```python
+!cat kind-config.yaml
+```
+
     apiVersion: kind.x-k8s.io/v1alpha4
     kind: Cluster
     nodes:
@@ -39,33 +39,33 @@ To run this example in Kind we need to start Kind with access to a local folder
         - hostPath: /tmp/models
           containerPath: /models
 
-
-
-To start a Kind cluster with these settings using our ansible script you can run from the project root folder
-
-```
-ansible-playbook ansible/playbooks/kind-cluster.yaml -e kind_config_file=${PWD}/samples/examples/local-pvc/kind-config.yaml
-```
-
-[**Now you should finish the Seldon install following the docs.**](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/index.html)
-
-Create the local folder we will use for our models and copy an example iris sklearn model to it.
-
-
-```python
-!mkdir -p /tmp/models
-!gsutil cp -r gs://seldon-models/mlserver/iris /tmp/models
-```
-
-### Create Server with PVC
-
-Here we create a storage class and associated persistent colume referencing the `/models` folder where our models are stored.
-
-
-```python
-!cat pvc.yaml
-```
-
+
+
+To start a Kind cluster with these settings using our ansible script you can run from the project root folder
+
+```
+ansible-playbook ansible/playbooks/kind-cluster.yaml -e kind_config_file=${PWD}/samples/examples/local-pvc/kind-config.yaml
+```
+
+[**Now you should finish the Seldon install following the docs.**](https://docs.seldon.ai/seldon-core-2/installation/production-environment)
+
+Create the local folder we will use for our models and copy an example iris sklearn model to it.
+
+
+```python
+!mkdir -p /tmp/models
+!gsutil cp -r gs://seldon-models/mlserver/iris /tmp/models
+```
+
+### Create Server with PVC
+
+Here we create a storage class and associated persistent colume referencing the `/models` folder where our models are stored.
+
+
+```python
+!cat pvc.yaml
+```
+
     apiVersion: storage.k8s.io/v1
     kind: StorageClass
     metadata:
@@ -107,17 +107,17 @@ Here we create a storage class and associated persistent colume referencing the
       selector:
         matchLabels:
           type: local
-
-
-Now we create a new Server based on the provided MLServer configuration but extend it with our PVC by adding this to the rclone container which will allow rclone to move models from this PVC onto the server. 
-
-We also add a new capability `pvc` to allow us to schedule models to this server that has the PVC.
-
-
-```python
-!cat server.yaml
-```
-
+
+
+Now we create a new Server based on the provided MLServer configuration but extend it with our PVC by adding this to the rclone container which will allow rclone to move models from this PVC onto the server. 
+
+We also add a new capability `pvc` to allow us to schedule models to this server that has the PVC.
+
+
+```python
+!cat server.yaml
+```
+
     apiVersion: mlops.seldon.io/v1alpha1
     kind: Server
     metadata:
@@ -136,17 +136,17 @@ We also add a new capability `pvc` to allow us to schedule models to this server
           volumeMounts:
           - name: models-pvc
             mountPath: /var/models
-
-
-### SKLearn Model
-
-We use a simple sklearn iris classification model with the added `pvc` requirement so our MLServer with the PVC will be targeted during scheduling.
-
-
-```python
-!cat ./iris.yaml
-```
-
+
+
+### SKLearn Model
+
+We use a simple sklearn iris classification model with the added `pvc` requirement so our MLServer with the PVC will be targeted during scheduling.
+
+
+```python
+!cat ./iris.yaml
+```
+
     apiVersion: mlops.seldon.io/v1alpha1
     kind: Model
     metadata:
@@ -156,29 +156,29 @@ We use a simple sklearn iris classification model with the added `pvc` requireme
       requirements:
       - sklearn
       - pvc
-
-
-
-```python
-!kubectl create -f iris.yaml -n ${NAMESPACE}
-```
-
+
+
+
+```python
+!kubectl create -f iris.yaml -n ${NAMESPACE}
+```
+
     model.mlops.seldon.io/iris created
-
-
-
-```python
-!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
-```
-
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
     model.mlops.seldon.io/iris condition met
-
-
-
-```python
-!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
-```
-
+
+
+
+```python
+!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
     {
       "conditions": [
         {
@@ -194,14 +194,14 @@ We use a simple sklearn iris classification model with the added `pvc` requireme
       ],
       "replicas": 1
     }
-
-
-
-```python
-!seldon model infer iris --inference-host ${MESH_IP}:80 \
-  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' 
-```
-
+
+
+
+```python
+!seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' 
+```
+
     {
     	"model_name": "iris_1",
     	"model_version": "1",
@@ -225,16 +225,16 @@ We use a simple sklearn iris classification model with the added `pvc` requireme
     		}
     	]
     }
-
-
-Do a gRPC inference call
-
-
-```python
-!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP}:80 \
-   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
-```
-
+
+
+Do a gRPC inference call
+
+
+```python
+!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP}:80 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
     {
       "modelName": "iris_1",
       "modelVersion": "1",
@@ -254,17 +254,17 @@ Do a gRPC inference call
         }
       ]
     }
-
-
-
-```python
-!kubectl delete -f ./iris.yaml -n ${NAMESPACE}
-```
-
+
+
+
+```python
+!kubectl delete -f ./iris.yaml -n ${NAMESPACE}
+```
+
     model.mlops.seldon.io "iris" deleted
-
-
-
-```python
-
-```
+
+
+
+```python
+
+```