document memory recommendations

docs/getting-started/first-abn.md

@@ -11,7 +11,7 @@ This tutorial describes how to do A/B testing of a backend component using the [
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -111,7 +111,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 

docs/getting-started/first-performance.md

@@ -9,7 +9,7 @@ Run your first performance test by load testing a Kubernetes HTTP service and vi
 ![Load test HTTP](images/kubernetesusage.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Deploy the sample HTTP service in the Kubernetes cluster.
     ```shell
     kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
@@ -66,7 +66,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `httpbin-test` with the following parameters:
 

docs/getting-started/first-release.md

@@ -14,7 +14,7 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../tutorials/images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y

docs/tutorials/integrations/kserve-mm/abn.md

@@ -11,11 +11,8 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -131,9 +128,9 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
-[Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
+[Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `modelmesh-serving/backend` with the following parameters:
 
 * URL: `http://iter8.modelmesh-serving:8080/abnDashboard`
 * Query string: `namespace=modelmesh-serving&application=backend`

docs/tutorials/integrations/kserve-mm/blue-green.md

@@ -14,11 +14,8 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../../images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y

docs/tutorials/integrations/kserve-mm/canary.md

@@ -14,11 +14,8 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Canary rollout](../../images/canary.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y

docs/tutorials/integrations/kserve/abn-grpc.md

@@ -11,11 +11,12 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -139,7 +140,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 

docs/tutorials/integrations/kserve/abn-http.md

@@ -11,11 +11,12 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -131,7 +132,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 

docs/tutorials/integrations/kserve/blue-green.md

@@ -14,11 +14,12 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../../images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
 <!-- Istio is installed as part of kserve install -->
 
 ## Install the Iter8 controller

docs/tutorials/integrations/kserve/canary.md

@@ -14,11 +14,12 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Canary rollout](../../images/canary.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
 <!-- Istio is installed as part of kserve install -->
 
 ## Install the Iter8 controller

docs/tutorials/integrations/kserve/grpc.md

@@ -7,12 +7,12 @@ template: main.html
 This tutorial shows how easy it is to run a load test for KServe when using gRPC to make requests. We use a sklearn model to demonstrate. The same approach works for any model type. 
 
 ???+ warning "Before you begin"
-    1. Try [Your first performance test](../../../getting-started/first-performance.md). Understand the main [concepts](../../../getting-started/concepts.md) behind Iter8.
-    2. Ensure that you have the [kubectl](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    3. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     4. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -81,7 +81,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`. 
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `model-test` with the following parameters:
 

docs/tutorials/integrations/kserve/http.md

@@ -7,12 +7,12 @@ template: main.html
 This tutorial shows how easy it is to run a load test for KServe when using HTTP to make requests. We use a sklearn model to demonstrate. The same approach works for any model type. 
 
 ???+ warning "Before you begin"
-    1. Try [Your first performance test](../../../getting-started/first-performance.md). Understand the main [concepts](../../../getting-started/concepts.md) behind Iter8.
-    2. Ensure that you have the [kubectl](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    3. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     4. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -70,7 +70,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `model-test` with the following parameters: