added notebook for azure stack edge gpu

Author: initmahesh

how-to-use-azureml/deployment/deloy-to-azure-edge-devices/deploying-with-iotedge/README.md

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+# Model Deployment with Azure ML service to Azure stack edge using Iot edge
+You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization). Here we will show you how you can deploy a model from cloud to Azure stack edge device using IoT Edge.
+For more information please check out this article: https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-machine-learning-edge-04-train-model
+
+## Get Started
+To begin, you will need an ML workspace.
+For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
+you also need to get you Azure stacck edge setup and run a sample a gpu sample as : https://docs.microsoft.com/en-us/azure/databox-online/azure-stack-edge-gpu-deploy-sample-module-marketplace
+
+
+## Deploy to the Azure stack edge
+You can deploy to the Azure stack edge as 
+- Notebook example: [model-register-and-deploy](./production-deploy-to-ase-gpu.ipynb).

how-to-use-azureml/deployment/deloy-to-azure-edge-devices/deploying-with-iotedge/img/conf_file_download.JPG

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+{
+
+  "modulesContent": {
+
+    "$edgeAgent": {
+
+      "properties.desired": {
+
+        "schemaVersion": "1.0",
+
+        "runtime": {
+
+          "type": "docker",
+
+          "settings": {
+
+            "minDockerVersion": "v1.25",
+
+            "loggingOptions": "",
+
+            "registryCredentials": {
+
+              "__REGISTRY_NAME": {
+
+          			"username": "__REGISTRY_USER_NAME",
+
+          			"password":"__REGISTRY_PASSWORD",
+
+          			"address":"__REGISTRY_NAME.azurecr.io"
+
+          		}
+
+            }
+
+          }
+
+        },
+
+        "systemModules": {
+
+          "edgeAgent": {
+
+            "type": "docker",
+
+            "settings": {
+
+              "image": "mcr.microsoft.com/azureiotedge-agent:1.0",
+
+              "createOptions": "{}",
+
+              "env": {
+
+                "UpstreamProtocol": {
+
+                  "value": "MQTT"
+
+                }
+
+              }
+
+            }
+
+          },
+
+          "edgeHub": {
+
+            "type": "docker",
+
+            "status": "running",
+
+            "restartPolicy": "always",
+
+            "settings": {
+
+              "image": "mcr.microsoft.com/azureiotedge-hub:1.0",
+
+              "createOptions": "{\"User\":\"root\",\"HostConfig\":{\"PortBindings\":{\"5671/tcp\":[{\"HostPort\":\"5671\"}], \"8883/tcp\":[{\"HostPort\":\"8883\"}],\"443/tcp\":[{\"HostPort\":\"443\"}]}}}",
+
+              "env": {
+
+                "UpstreamProtocol": {
+
+                  "value": "MQTT "
+
+                }
+
+              }
+
+            }
+
+          }
+
+        },
+
+        "modules": {
+
+          
+          "__MODULE_NAME": {
+
+            "version": "1.0",
+
+            "type": "docker",
+
+            "status": "running",
+
+            "restartPolicy": "always",
+
+            "settings": {
+
+              "image": "__REGISTRY_IMAGE_LOCATION",
+
+              "createOptions": "{\"HostConfig\":{\"PortBindings\":{\"5001/tcp\":[{\"HostPort\":\"5001\"}], \"8883/tcp\":[{\"HostPort\":\"5002\"}],},\"runtime\":\"nvidia\"},\"WorkingDir\":\"/var/azureml-app\"}"
+			        
+
+            }
+
+          }
+
+        }
+
+      }
+
+    },
+
+    "$edgeHub": {
+
+      "properties.desired": {
+
+        "schemaVersion": "1.0",
+
+        "routes": {
+
+      
+          "machineLearningToIoTHub": "FROM /messages/modules/__MODULE_NAME/outputs/amlOutput INTO $upstream"
+
+        },
+
+        "storeAndForwardConfiguration": {
+
+          "timeToLiveSecs": 7200
+
+        }
+
+      }
+
+    },
+
+
+    "__MODULE_NAME": {
+
+      "properties.desired": {}
+
+    }
+
+  }
+
+}