docs: Minor documentation fixes

.gitignore

@@ -1,5 +1,8 @@
 # general
 .env
+.DS_Store
+.run/
+temp/
 
 public/
 target/
@@ -30,7 +33,7 @@ bin
 .vscode
 *~
 
-# Modelmesh development related artifacts
+# ModelMesh development related artifacts
 devbuild
 .develop_image_name
 .dev/

controllers/modelmesh/modelmesh.go

@@ -261,29 +261,30 @@ func (m *Deployment) addMMEnvVars(deployment *appsv1.Deployment) error {
 	}
 
 	if m.EnableAccessLogging {
-		// See https://github.com/kserve/modelmesh/blob/v0.11.0/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L55
+		// See https://github.com/kserve/modelmesh/blob/v0.11.1/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L55
 		if err := setEnvironmentVar(ModelMeshContainerName, "MM_LOG_EACH_INVOKE", "true", deployment); err != nil {
 			return err
 		}
 	}
 
 	if m.GrpcMaxMessageSize > 0 {
-		// See https://github.com/kserve/modelmesh/blob/v0.11.0/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L38
+		// See https://github.com/kserve/modelmesh/blob/v0.11.1/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L38
 		if err := setEnvironmentVar(ModelMeshContainerName, "MM_SVC_GRPC_MAX_MSG_SIZE", strconv.Itoa(m.GrpcMaxMessageSize), deployment); err != nil {
 			return err
 		}
 	}
 
-	// See https://github.com/kserve/modelmesh/blob/v0.11.0/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L31
+	// See https://github.com/kserve/modelmesh/blob/v0.11.1/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L31
 	if err := setEnvironmentVar(ModelMeshContainerName, "MM_KVSTORE_PREFIX", ModelMeshEtcdPrefix, deployment); err != nil {
 		return err
 	}
-	// See https://github.com/kserve/modelmesh/blob/v0.11.0/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L68
+	// See https://github.com/kserve/modelmesh/blob/v0.11.1/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L68
 	if err := setEnvironmentVar(ModelMeshContainerName, "MM_DEFAULT_VMODEL_OWNER", m.DefaultVModelOwner, deployment); err != nil {
 		return err
 	}
 
 	if len(m.PayloadProcessors) > 0 {
+		// See https://github.com/kserve/modelmesh/blob/v0.11.1/src/main/java/com/ibm/watson/modelmesh/ModelMeshEnvVars.java#L26
 		if err := setEnvironmentVar(ModelMeshContainerName, "MM_PAYLOAD_PROCESSORS", m.PayloadProcessors, deployment); err != nil {
 			return err
 		}

docs/model-formats/openvino-ir.md

@@ -2,13 +2,13 @@
 
 ## Format
 
-Full documentation on OpenVINO IR format can be found [here](https://docs.openvino.ai/2022.1/openvino_docs_MO_DG_IR_and_opsets.html#intermediate-representation-used-in-openvino).
+Full documentation on OpenVINO IR format can be found [here](https://docs.openvino.ai/2022.3/openvino_docs_MO_DG_IR_and_opsets.html#intermediate-representation-used-in-openvino).
 
 OpenVINO™ toolkit introduces its own format of graph representation and its own operation set. A graph is represented with two files: an XML file and a binary file. This representation is commonly referred to as the Intermediate Representation or IR.
 
-An example of a small IR XML file can be found in the same [link above](https://docs.openvino.ai/2022.1/openvino_docs_MO_DG_IR_and_opsets.html#intermediate-representation-used-in-openvino). The XML file doesn’t have big constant values, like convolution weights. Instead, it refers to a part of the accompanying binary file that stores such values in a binary format.
+An example of a small IR XML file can be found in the same [link above](https://docs.openvino.ai/2022.3/openvino_docs_MO_DG_IR_and_opsets.html#intermediate-representation-used-in-openvino). The XML file doesn’t have big constant values, like convolution weights. Instead, it refers to a part of the accompanying binary file that stores such values in a binary format.
 
-Models trained in other formats (Caffe, TensorFlow, MXNet, PaddlePaddle and ONNX) can be converted to OpenVINO IR format. To do so, use OpenVINO’s [Model Optimizer](https://docs.openvino.ai/2022.1/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
+Models trained in other formats (Caffe, TensorFlow, MXNet, PaddlePaddle and ONNX) can be converted to OpenVINO IR format. To do so, use OpenVINO’s [Model Optimizer](https://docs.openvino.ai/2022.3/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
 
 ## Configuration
 
@@ -20,7 +20,7 @@ Here is an example of client code:
 input_tensorname = 'input'
 request.inputs[input_tensorname].CopyFrom(make_tensor_proto(img, shape=(1, 3, 224, 224)))
 
-.....
+...
 
 output_tensorname = 'resnet_v1_50/predictions/Reshape_1'
 predictions = make_ndarray(result.outputs[output_tensorname])
@@ -46,21 +46,22 @@ More details on model configuration can be found [here](https://docs.openvino.ai
 
 The OpenVINO models need to be placed and mounted in a particular directory structure:
 
-```
+```shell
 tree models/
+
 models/
 ├── model1
-│   ├── 1
-│   │   ├── ir_model.bin
-│   │   └── ir_model.xml
-│   └── 2
-│       ├── ir_model.bin
-│       └── ir_model.xml
+│   ├── 1
+│   │   ├── ir_model.bin
+│   │   └── ir_model.xml
+│   └── 2
+│       ├── ir_model.bin
+│       └── ir_model.xml
 └── model2
-│   └── 1
-│       ├── ir_model.bin
-│       ├── ir_model.xml
-│       └── mapping_config.json
+│   └── 1
+│       ├── ir_model.bin
+│       ├── ir_model.xml
+│       └── mapping_config.json
 └── model3
     └── 1
         └── model.onnx

docs/predictors/run-inference.md

@@ -190,7 +190,7 @@ This would give a response similar to the following:
     {
       "name": "predict",
       "datatype": "FP32",
-      "shape": [1],
+      "shape": [1, 1],
       "data": [8]
     }
   ]

docs/predictors/setup-storage.md

@@ -105,7 +105,7 @@ Models can be stored on [Kubernetes Persistent Volumes](https://kubernetes.io/do
 There are two ways to enable PVC support in ModelMesh:
 
 1. The Persistent Volume Claims can be added in the `storage-config` secret. This way all PVCs will be mounted to all serving runtime pods.
-2. The `allowAnyPVC` configuration flag can be set to `true`. This way the Modelmesh controller will dynamically mount the PVC to a runtime pod at the time a predictor or inference service requiring it is being deployed.
+2. The `allowAnyPVC` configuration flag can be set to `true`. This way the ModelMesh controller will dynamically mount the PVC to a runtime pod at the time a predictor or inference service requiring it is being deployed.
 
 Follow the example instructions below to create a PVC, store a model on it, and configure ModelMesh to mount the PVC to the runtime serving pods so that the model can be loaded for inferencing.
 
@@ -230,7 +230,7 @@ As an alternative to preconfiguring all _allowed_ PVCs in the `storage-config` s
 
 Let's update (or create) the `model-serving-config` ConfigMap.
 
-Note, if you already have a `model-serving-config` ConfigMap, you might want to retain the existing config overrides. You can check your current configuration flags by running:
+**Note**, if you already have a `model-serving-config` ConfigMap, you might want to retain the existing config overrides. You can check your current configuration flags by running:
 
 ```shell
 kubectl get cm "model-serving-config" -o jsonpath="{.data['config\.yaml']}"`
@@ -319,7 +319,7 @@ The response should look like the following:
 {
   "model_name": "sklearn-pvc-example__isvc-3d2daa3370",
   "outputs": [
-    {"name": "predict", "datatype": "INT64", "shape": [1], "data": [8]}
+    {"name": "predict", "datatype": "INT64", "shape": [1, 1], "data": [8]}
   ]
 }
 ```

docs/quickstart.md

@@ -87,6 +87,7 @@ Here, we deploy an SKLearn MNIST model which is served from the local MinIO cont
 
 ```shell
 kubectl apply -f - <<EOF
+---
 apiVersion: serving.kserve.io/v1beta1
 kind: InferenceService
 metadata:
@@ -109,6 +110,7 @@ using the `storageUri` field in lieu of the storage spec:
 
 ```shell
 kubectl apply -f - <<EOF
+---
 apiVersion: serving.kserve.io/v1beta1
 kind: InferenceService
 metadata:
@@ -127,7 +129,7 @@ EOF
 
 After applying this `InferenceService`, you should see that it is likely not yet ready.
 
-```
+```shell
 kubectl get isvc
 
 NAME                    URL   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION   AGE
@@ -229,7 +231,7 @@ This should give you output like the following:
     {
       "name": "predict",
       "datatype": "INT64",
-      "shape": ["1"],
+      "shape": ["1", "1"],
       "contents": {
         "int64Contents": ["8"]
       }
@@ -264,8 +266,8 @@ This should give you a response like the following:
   "outputs": [
     {
       "name": "predict",
-      "datatype": "FP32",
-      "shape": [1],
+      "datatype": "INT64",
+      "shape": [1, 1],
       "data": [8]
     }
   ]
@@ -281,4 +283,5 @@ command from the root of the project:
 
 ```shell
 ./scripts/delete.sh --namespace modelmesh-serving
+kubectl delete namespace modelmesh-serving
 ```

fvt/README.md

@@ -30,6 +30,7 @@ The FVTs rely on a set of models existing in a configured `localMinIO` storage.
 If starting with a fresh namespace, install ModelMesh Serving configured for the FVTs with:
 
 ```Shell
+kubectl create namespace modelmesh-serving
 ./scripts/install.sh --namespace modelmesh-serving --fvt --dev-mode-logging
 ```