Standalone scripts to support pipeline template (#55)

* Standalone scripts & instructions to run pipeline template in Synapse gallery

Co-authored-by: karthick-rn <kanarend@microsoft.com>
Co-authored-by: Tai Yee <taiyee@microsoft.com>

.gitignore

@@ -130,3 +130,6 @@ dmypy.json
 
 # Packaged zip
 *.zip
+
+# Mac 
+.DS_Store

deploy/gallery/create_service.sh

@@ -0,0 +1,61 @@
+#! /usr/bin/env bash
+
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# This script creates the required linked service & the spark 
+# job definition on the Synapse workspace and should be run 
+# prior to executing the pipeline template from Synapse gallery.
+
+set -e
+
+ENV_CODE=${1:-${ENV_CODE}}
+
+if [[ -z ${ENV_CODE} ]]
+  then
+    echo "Missing Environment Code"
+    exit 1
+fi
+
+base_dir="$(cd "$(dirname "$0")/../../" && pwd)"
+
+# Uncompress the custom vision model v2 package
+if ! unzip "${base_dir}"/custom-vision-model-v2.zip -d "${base_dir}"/custom-vision-model-v2-"${ENV_CODE}";
+then
+    echo "Unzip failed please check .."
+    exit 1
+else
+    echo "Unzip completed successfully"
+fi
+
+pkg_dir=${base_dir}/custom-vision-model-v2-${ENV_CODE}
+
+# The following one's are created on the Synapse workspace to run 
+# the pipeline template in Synapse gallery.
+services=("linked-service" "spark-job-definition")
+
+for item in "${services[@]}"
+do
+    if [[ $item == "linked-service" ]]
+    then
+       folder_name="linkedService"
+    else
+       folder_name="sparkJobDefinition"
+    fi
+
+    for file in "${pkg_dir}/${folder_name}"/*.json
+    do
+        full_name="${file}"
+        file_name=$(basename -s .json "${file}")
+        # Edit the json file format to work with Synapse CLI commands
+        if [[ $folder_name == "sparkJobDefinition" ]]
+        then
+            contents="$(jq '.properties' "${full_name}")" && echo -E "${contents}" > "${full_name}"
+            sleep 5
+        fi
+        # Create the linked services and spark job definition on Synapse workspace
+        echo "Creating ${item} please wait.."
+        az synapse "${item}" create --workspace-name "${ENV_CODE}-pipeline-syn-ws" \
+            --name "${file_name}" --file @"${full_name}"
+    done
+done

deploy/gallery/instructions.md

@@ -0,0 +1,76 @@
+If you've landed on this page from Synapse gallery then Congrats! -- you are in the right place.
+
+The instructions on this page will guide you through configuring the pipeline template to successfully run a custom vision model for object detection (for example, detecting swimming pool objects). You can also use the pipeline template to explore other object detection use cases as well. Currently, the template only supports custom vision model v2. 
+
+**Prerequisites**
+
+* Follow the steps in the [readme](https://github.com/Azure/Azure-Orbital-Analytics-Samples/blob/main/deploy/README.md) to deploy the infrastructure. This includes creation of the custom vision model v2 package as well. 
+
+* Run the following command to create the linked services and spark job definition on the Synapse workspace: 
+
+	```
+	./deploy/gallery/create_service.sh <environmentCode> 
+	```
+
+	NOTE	
+	**environmentCode** should be the same as the one used in the deployment steps
+
+* Run the following command to copy the sample GeoTiff image and the required configurations into the storage account for detecting swimming pools using the Object Detection CV model: 
+
+	```
+	./deploy/scripts/copy_geotiff.sh <environmentCode>
+	```
+ 
+**Switch back to Synapse gallery**
+
+1. The input page appears:
+   
+   ![](./images/1.png)
+
+2. Select the values from the dropdown as shown below and click **Open pipeline**:
+
+    ![](./images/2.png)
+
+3. On the right side, there is a list of mandatory fields. Click each of them and select the respective names from the dropdown as shown below. This additional step is only interim; we are working with the Synapse product group for a long term fix.
+
+- Transforms
+
+    ![](./images/3.png)
+
+ - Pool Geolocation
+     ![](./images/4.png)
+ 
+ - Copy Tiles
+ 
+    ![](./images/5.png)
+
+- Copy Config file
+ 
+    ![](./images/6.png)
+
+- Copy Xml from Convert Transform
+ 
+    ![](./images/7.png)
+
+4. When all the required fields are provided as shown on the right side of the image below, go ahead and publish.
+
+    ![](./images/8.png)
+
+5. When published successfully, you are just one step away from running the pipeline. Enable the Integration Runtime by moving the **Data flow debug** toggle button. While this is warming up, enter the value for the parameters.
+
+   ![](./images/9.png) 
+   
+
+    |No |Parameter | Value | Comments |
+    |--| ---- | --- | ------- |
+    | 1|Prefix| \<environmentCode>-test-container     |          |
+    | 2|StorageAccountName|  rawdata<6-character-random-string>  |    Get the storage account name from \<environmentCode>-data-rg |
+    | 3|AOI     |   -117.063550 32.749467 -116.999386 32.812946    | Sample bounding box |
+    | 4|BatchAccountName | | Get the batch account name from \<environmentCode>-orc-rg |
+    | 5|BatchJobName | \<environmentCode>-data-cpu-pool | Get the jobname from the batch account|
+    | 6|BatchLocation | | Get the region from the batch account. Usually be the deployment region|
+    | 7|SparkPoolName | pool<6-character-random-string>| Get the spark pool name from \<environmentCode>-pipeline-rg | 
+    | 8|EnvCode | \<environmentCode> | Same as used in the deployment steps| 
+
+6. All set to start the pipeline now. Press **Debug**.
+

deploy/scripts/copy_geotiff.sh

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+#!/usr/bin/env bash
+
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# This script automates the following activities & should be run prior to
+# executing the pipeline template from Synapse gallery.
+#       * Create storage container under "<envcode>-data-rg" resource group
+#       * Copy the sample TIF into the newly created storage container
+#       * Copy the configurations required for Object Detection
+
+set -e
+
+ENV_CODE=${1:-${ENV_CODE}}
+
+if [[ -z "$ENV_CODE" ]]
+then
+    echo "Missing Environment Code"
+    exit 1
+fi
+
+# Default name for the container
+container_name="${ENV_CODE}-test-container"
+
+# Retrieve the respective values for the environment variables
+AZURE_STORAGE_ACCOUNT=$(az storage account list --resource-group "${ENV_CODE}-data-rg" --query [0].name -o tsv)
+AZURE_STORAGE_KEY=$(az storage account keys list --account-name "$AZURE_STORAGE_ACCOUNT" --query [0].value -o tsv)
+AZURE_STORAGE_AUTH_MODE=key
+
+export AZURE_STORAGE_ACCOUNT
+export AZURE_STORAGE_KEY
+export AZURE_STORAGE_AUTH_MODE
+
+# Declare an associated array for dictionaries
+declare -A array
+
+key1='sample_4326.tif'
+value1='https://aoigeospatial.blob.core.windows.net/public/samples/sample_4326.tif'
+
+key2='custom_vision_object_detection.json'
+value2='https://raw.githubusercontent.com/Azure/Azure-Orbital-Analytics-Samples/main/src/aimodels/custom_vision_object_detection_offline/specs/custom_vision_object_detection.json'
+
+key3='config.json'
+value3='https://raw.githubusercontent.com/Azure/Azure-Orbital-Analytics-Samples/main/src/aimodels/custom_vision_object_detection_offline/config/config.json'
+
+array["raw/$key1"]="$value1"
+array["config/$key2"]="$value2"
+array["config/$key3"]="$value3"
+
+echo "Creating storage container $container_name.."
+if ! az storage container create --name "$container_name" --fail-on-exist;
+then
+    echo "Failed during storage container creation"
+    exit 1
+else
+    echo "Container $container_name created successfully"
+fi
+
+for key in "${!array[@]}"; do
+    echo "Copying $key from source"
+    az storage blob copy start --destination-blob $key --destination-container "$container_name" --source-uri "${array[$key]}"
+done