From c00b41d83f0d84e46075ca89743e59c6f6e94c55 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 11:59:15 -0700 Subject: [PATCH 01/19] Updated Bash manual steps to manually configure the Data Factory start and end dates --- scripts/README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index d12535f7..76a2166d 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -49,9 +49,8 @@ Follow these steps to deploy the starter kit using Bash: ``` npm install ``` -1. Open **/scripts/ARM/scenario_complete.params.nocomments.json** in a text editor and update the - **sqlServerAdminPassword** and **sqlAnalyticsServerAdminPassword** parameters. The first password is for the Mobile App back end database; the second password is for the analytic database that supports - Power BI queries. Choose a suitable password for each one and save the updated file. +1. Open **/scripts/ARM/scenario_complete.params.nocomments.json** in a text editor and update the **sqlServerAdminPassword** and **sqlAnalyticsServerAdminPassword** parameters. The first password is for the Mobile App back end database; the second password is for the analytic database that supports + Power BI queries. Choose a suitable password for each one and save the updated file. Next, set **dataFactoryStartDate** and **dataFactoryEndDate** to specify the period during which you intend to run the solution. ``` "parameters": { @@ -59,7 +58,9 @@ Follow these steps to deploy the starter kit using Bash: "storageAccountType": { "value": "Standard_LRS" }, ... "sqlServerAdminPassword": { "value": "" }, - "sqlAnalyticsServerAdminPassword": { "value": "" } + "sqlAnalyticsServerAdminPassword": { "value": "" }, + "dataFactoryStartDate": { "value": "" ], + "dataFactoryEndDate": { "value": "" ] } ``` 1. Launch the deployment script. From 54c355382a1510aa65189fbf62e26fa0bf6c5c3e Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 12:05:55 -0700 Subject: [PATCH 02/19] Minor update to Bash manual steps --- scripts/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/scripts/README.md b/scripts/README.md index 76a2166d..f2fcbe24 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -50,7 +50,9 @@ Follow these steps to deploy the starter kit using Bash: npm install ``` 1. Open **/scripts/ARM/scenario_complete.params.nocomments.json** in a text editor and update the **sqlServerAdminPassword** and **sqlAnalyticsServerAdminPassword** parameters. The first password is for the Mobile App back end database; the second password is for the analytic database that supports - Power BI queries. Choose a suitable password for each one and save the updated file. Next, set **dataFactoryStartDate** and **dataFactoryEndDate** to specify the period during which you intend to run the solution. + Power BI queries. Choose a suitable password for each one. + + Next, set **dataFactoryStartDate** and **dataFactoryEndDate** to specify the period during which you intend to run the solution. Save the updated file. ``` "parameters": { From 6eeb0fca47a8f6e7fcd2fd750c57fcc096483d1a Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 12:51:07 -0700 Subject: [PATCH 03/19] Update PowerShell script to prompt user to provide the number of days to run the solution --- scripts/PowerShell/deploy.ps1 | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/scripts/PowerShell/deploy.ps1 b/scripts/PowerShell/deploy.ps1 index 3ec1b026..a24a617c 100644 --- a/scripts/PowerShell/deploy.ps1 +++ b/scripts/PowerShell/deploy.ps1 @@ -74,6 +74,27 @@ if ($Subscriptions.Length -gt 1) { $subscription = $Subscriptions } +$numberOfDays = 0 +$inputOK = $false +do +{ + try + { + [int]$numberOfDays = Read-Host "Enter the number of days during which you intend to run the solution" + if ($numberOfDays -gt 0) { + $inputOK = $true + } + else { + Write-Host -ForegroundColor red "Please enter a number greater than 0." + } + } + catch + { + Write-Host -ForegroundColor red "Please enter a numeric value." + } +} +until ($inputOK) + # Create or update the resource group using the specified template file and template parameters file Write-Output "" Write-Output "**************************************************************************************************" @@ -110,7 +131,7 @@ Write-Output "****************************************************************** # Create required services $templateParams = New-Object -TypeName Hashtable $TemplateParams.Add(("dataFactoryStartDate"), (Get-Date).ToUniversalTime().AddDays(1).ToString("s")) -$TemplateParams.Add(("dataFactoryEndDate"), (Get-Date).ToUniversalTime().AddDays(91).ToString("s")) +$TemplateParams.Add(("dataFactoryEndDate"), (Get-Date).ToUniversalTime().AddDays($numberOfDays + 1).ToString("s")) if ($MobileAppRepositoryUrl) { Write-Warning "Overriding the mobile app repository URL..." $templateParams.Add("mobileAppRepositoryUrl", $MobileAppRepositoryUrl) From ccf0516b496bdfeb480ac99b8d12a372ff0180ab Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 14:50:38 -0700 Subject: [PATCH 04/19] Updated wording in user input prompt --- scripts/PowerShell/deploy.ps1 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/PowerShell/deploy.ps1 b/scripts/PowerShell/deploy.ps1 index a24a617c..57dfa17d 100644 --- a/scripts/PowerShell/deploy.ps1 +++ b/scripts/PowerShell/deploy.ps1 @@ -80,7 +80,7 @@ do { try { - [int]$numberOfDays = Read-Host "Enter the number of days during which you intend to run the solution" + [int]$numberOfDays = Read-Host "Enter the number of days you want to run the ADF pipeline" if ($numberOfDays -gt 0) { $inputOK = $true } From 0bf59a194956b7bd26d0190a61ddf2b1387eb714 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 18:03:11 -0700 Subject: [PATCH 05/19] Update PowerShell script to upload sample data --- scripts/PowerShell/deploy.ps1 | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/scripts/PowerShell/deploy.ps1 b/scripts/PowerShell/deploy.ps1 index 57dfa17d..f5b3eacd 100644 --- a/scripts/PowerShell/deploy.ps1 +++ b/scripts/PowerShell/deploy.ps1 @@ -117,16 +117,16 @@ if ($deployment1 -and $deployment1.ProvisioningState -ne "Succeeded") { exit 1 } - # Upload the HQL queries to the storage account container Write-Output "" Write-Output "**************************************************************************************************" Write-Output "* Uploading files to blob storage..." Write-Output "**************************************************************************************************" +Write-Output "Uploading hive scripts..." . .\scripts\Copy-ArtifactsToBlobStorage.ps1 -StorageAccountName $deployment1.Outputs.storageAccountName.Value ` - -StorageAccountKey $deployment1.Outputs.storageAccountKey.Value ` - -StorageContainerName $deployment1.Outputs.assetsContainerName.Value - + -StorageAccountKey $deployment1.Outputs.storageAccountKey.Value ` + -StorageContainerName $deployment1.Outputs.assetsContainerName.Value ` + -ArtifactsPath '..\..\..\src\HDInsight' # Create required services $templateParams = New-Object -TypeName Hashtable @@ -165,6 +165,12 @@ $storageAccountKey = $deployment2.Outputs.storageAccountKeyAnalytics.Value . .\scripts\setupStorage.ps1 -StorageAccountName $storageAccountName -StorageAccountKey $storageAccountKey -ContainerName $deployment2.Outputs.tripdataContainerName.Value . .\scripts\setupStorage.ps1 -StorageAccountName $storageAccountName -StorageAccountKey $storageAccountKey -ContainerName $deployment2.Outputs.referenceContainerName.Value +Write-Output "Uploading sample data..." +. .\scripts\Copy-ArtifactsToBlobStorage.ps1 -StorageAccountName $storageAccountName ` + -StorageAccountKey $storageAccountKey ` + -StorageContainerName $deployment2.Outputs.tripdataContainerName.Value ` + -ArtifactsPath '..\..\Assets' + # Initialize SQL databases Write-Output "" Write-Output "**************************************************************************************************" From 52d526a36a518c202a87978793ba26a048af136e Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 18:10:38 -0700 Subject: [PATCH 06/19] Documented manual steps to configure the ML retrain endpoint --- scripts/README.md | 102 +++++++++++++++++++++++++++++++--------------- 1 file changed, 70 insertions(+), 32 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index f2fcbe24..f8b78d5d 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -82,7 +82,76 @@ Follow these steps to deploy the starter kit using Bash: 1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select **mydriving-archive**. 2. Click the **START** button at the bottom of the page. 3. Similarly, start the **mydriving-vinlookup** job. - + +### Machine Learning Configuration +1. At the portal, obtain the credentials for the storage account whose name is prefixed with **"mydrivingstr"**. It can be found under the resource group of the solution. + + ![Storage Account](Images/storage-account.png?raw=true "Storage Account") + + _Storage Account_ + +1. Go to the classic portal, select the **Machine Learning** service and then the **mydriving** workspace. Open the workspace by selecting **Sign-in to ML Studio**. + +1. Click the **Reader** module at the top of the experiment and set the **Account Name** and **Account Key** properties to the values obtained previously. + + ![Configuring the ML Storage Account](Images/ml-storage-account.png?raw=true "Configuring the ML Storage Account") + + _Configuring the ML Storage Account_ + +1. Click **Run** to run the **MyDriving** experiment. + +1. Now, select the **Train Model** module, click **Setup Web Service**, and then **Deploy Web Service**. Reply **yes** when prompted for confirmation. + + ![Deploying an ML Web Service](Images/ml-deploy-web-service.png?raw=true "Deploying an ML Web Service") + + _Deploying an ML Web Service_ + +1. Go back to the classic portal, select the **Machine Learning** service and then the **mydriving** workspace. Now switch to the **Web Services** tab and select the **MyDriving** web service. + + ![Configuring ML Web Services](Images/ml-web-services.png?raw=true "Configuring ML Web Services") + + _Configuring ML Web Services_ + +1. Click **Add Endpoint**, enter **_retrain_** as the name of the new endpoint, and then click the checkmark. + + ![Adding an ML Web Service Endpoint](Images/ml-adding-endpoint.png?raw=true "Adding an ML Web Service Endpoint") + + _Adding an ML Web Service Endpoint_ + +1. Select the newly added endpoint and copy the API key shown in the dashboard, under the **Quick Glance** section. + +1. Next, click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. + +1. Make a note of the API Key and web service endpoint. You'll need them later to configure the Data Factory's **TrainingEndpoint-AMLLinkedService** linked service. + +### Azure Data Factory configuration + +1. At the portal, select the resource group where the solution is deployed and under **Resources**, select the Data Factory resource. + + ![Data Factory](Images/data-factory.png?raw=true "Data Factory") + + _Data Factory_ + +1. In the data factory blade, select the **Author and deploy** action. +1. In the authoring blade, expand **Linked Services** and then select **AzureMLScoringandUpdateLinkedService**. + + ![Configuring the Data Factory](Images/configuring-data-factory.png?raw=true "Configuring the Data Factory") + + _Configuring the Data Factory_ + +1. Update the linked service definition by entering the following information from the Machine Learning experiment. + + ![Configuring a Linked Service](Images/configuring-linked-service.png?raw=true "Configuring a Linked Service") + + _Configuring a Linked Service_ + + - **mlEndpoint**: [TBD] endpoint for the Machine Learning experiment + - **apiKey**: [TBD] API key + - **updateResourceEndpoint**: [TBD] +1. Click **Deploy**. + +1. Next, under **Linked Services**, select **TrainingEndpoint-AMLLinkedService** and enter the values of the **mlEndpoint** and **apiKey** of the **retrain** endpoint you obtained earlier when you configured the ML experiment. Click **Deploy**. + ### Configuring Power BI Outputs for Azure Streaming Analytics 1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select **mydriving-hourlypbi**. @@ -132,37 +201,6 @@ Follow these steps to deploy the starter kit using Bash: _SQL Databases_ -### Azure Data Factory configuration - -1. At the portal, select the resource group where the solution is deployed and under **Resources**, select the Data Factory resource. - - ![Data Factory](Images/data-factory.png?raw=true "Data Factory") - - _Data Factory_ - -1. In the data factory blade, select the **Author and deploy** action. -1. In the authoring blade, expand **Linked Services** and then select **AzureMLScoringandUpdateLinkedService**. - - ![Configuring the Data Factory](Images/configuring-data-factory.png?raw=true "Configuring the Data Factory") - - _Configuring the Data Factory_ - -1. Update the linked service definition by entering the following information from the Machine Learning experiment. - - ![Configuring a Linked Service](Images/configuring-linked-service.png?raw=true "Configuring a Linked Service") - - _Configuring a Linked Service_ - - - **mlEndpoint**: [TBD] endpoint for the Machine Learning experiment - - **apiKey**: [TBD] API key - - **updateResourceEndpoint**: [TBD] -1. Click **Deploy**. -1. Next, under **Linked Services**, select **TrainingEndpoint-AMLLinkedService**. -1. Update the linked service definition by entering the following information: - - **mlEndpoint**: [TBD] endpoint for the Machine Learning experiment - - **apiKey**: [TBD] API key -1. Click **Deploy**. - ##### _Additional Information_ You can use the supplied **scripts\PowerShell\scripts\copyMLExperiment.ps1** to import previously packaged ML experiments at these locations: From c4f56a3847b4c7c929484e03adb5924380e13421 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Mon, 28 Mar 2016 18:11:32 -0700 Subject: [PATCH 07/19] Added screenshots --- scripts/Images/ml-adding-endpoint.png | Bin 0 -> 22204 bytes scripts/Images/ml-deploy-web-service.png | Bin 0 -> 74403 bytes scripts/Images/ml-storage-account.png | Bin 0 -> 89696 bytes scripts/Images/ml-web-services.png | Bin 0 -> 23131 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 scripts/Images/ml-adding-endpoint.png create mode 100644 scripts/Images/ml-deploy-web-service.png create mode 100644 scripts/Images/ml-storage-account.png create mode 100644 scripts/Images/ml-web-services.png diff --git a/scripts/Images/ml-adding-endpoint.png b/scripts/Images/ml-adding-endpoint.png new file mode 100644 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+bdacff24-3981-4acf-905b-3f2ed6582764,1,2016-03-19 16:11:38,0.0,0.0,1.0 +c4ccb675-b7f9-4e51-96c0-dc24fc3a327e,1,2016-03-19 21:28:42,0.0,0.0,\N +c5aaf97f-1092-4b16-8332-28d21e0bf465,1,2016-03-19 20:30:54,0.0,0.0,0.0 +c6beb9de-4231-44b0-bb26-5a03969e7d4b,Facebook:1047585061949223,2016-03-19 02:46:43,1277.4848966230484,198.8651525531811,0.3584905660377358 +c8596bf1-6a8a-4186-a85d-966e8a00bce6,1,2016-03-19 20:30:58,0.0,0.0,0.0 +cade5c47-6380-4c29-8116-6179db668d7b,Twitter:19249356,2016-03-19 23:26:50,1159.7060386269802,53.84239268220639,0.22093023255813954 +cb3ad9cd-44b7-452e-8356-3eace54fd7d2,Facebook:203538940012375,2016-03-19 18:30:01,1307.3224504614784,135.82832059578422,0.30434782608695654 +cd7ba09d-0c10-4969-8f5d-4e41b4e1f849,1,2016-03-19 21:28:36,0.0,0.0,\N +d51f0747-5c39-4b5a-a29c-62b7c46f1480,1,2016-03-19 19:56:01,0.0,0.0,0.0 +d5efd317-9eb9-46da-9650-e4ba142166cb,1,2016-03-19 21:28:18,0.0,0.0,0.0 +d5f4808c-4910-4160-8401-2bf028182a42,Facebook:1221443151217187,2016-03-19 22:45:52,0.0,0.0,0.0 +d5f5b615-a545-4110-812c-7e02c46c16c4,1,2016-03-19 19:55:37,0.0,0.0,0.0 +e0bb996e-d0f4-4748-b9f3-1cc203fd0c50,1,2016-03-19 16:11:06,0.0,0.0,\N +e1511f69-c0a2-4563-98fc-fa4c7fd90146,1,2016-03-19 19:55:50,0.0,0.0,\N +e470f3fe-b90b-43ac-8c7d-b5d174a2a5a1,1,2016-03-19 20:30:35,0.0,0.0,1.0 +e756ced7-d63a-45f3-b820-bcc9678b9bbc,1,2016-03-19 19:55:50,0.0,0.0,\N +e784de92-a01e-49b1-bfd9-bdc61efe000d,1,2016-03-19 20:37:40,0.0,0.0,\N +ed216364-78cc-4c83-8c70-e2e02b5ef412,Facebook:203538940012375,2016-03-19 00:26:37,1255.0295598841362,128.8520576599708,0.0196078431372549 +eef727f2-c980-404b-b29e-b19e60495779,1,2016-03-19 19:56:06,0.0,0.0,0.0 +f1c7aad3-daa6-43d4-951b-931c78f2c81f,1,2016-03-19 16:11:02,0.0,0.0,0.0 +f3d49a1e-abca-4081-a6f3-1738cf37bf77,1,2016-03-19 22:21:02,0.0,0.0,0.0 +f4be8138-ee67-4ddc-bbd7-d0f5b752cbd0,1,2016-03-19 21:28:48,0.0,0.0,0.0 +f73354b1-5bb3-4f61-85a6-92b950d70481,Facebook:108531336210491,2016-03-19 14:55:43,491.5629920898315,143.62849861218248,0.1811320754716981 +f96142e7-1ecc-4373-a7b7-351845fec052,1,2016-03-19 08:37:16,0.0,0.0,0.0 +fa30cf97-6af6-4b70-9ca4-44f75d446f41,1,2016-03-19 21:28:17,0.0,0.0,0.0 +fbb1a1e1-29e4-404b-aec7-fcca09592e72,1,2016-03-19 19:55:42,0.0,0.0,0.0 +fc677760-557c-40b8-b328-b45dcd833f22,Facebook:1047585061949223,2016-03-19 02:19:26,913.5,166.0,0.0 +fd31c6d9-3432-47ba-b6bd-392bae1029e3,1,2016-03-19 19:56:10,0.0,0.0,0.0 +fd7b0769-cdc6-4792-8805-1c282fe6194c,Facebook:1221443151217187,2016-03-19 18:05:32,703.527318232917,27.770262872360426,0.0 +fff6151f-dfb7-400c-a112-1d0b1d401e41,1,2016-03-19 16:14:35,0.0,0.0,0.0 diff --git a/scripts/Assets/tables/mlinput/2016/03/20/000000_0 b/scripts/Assets/tables/mlinput/2016/03/20/000000_0 new file mode 100644 index 00000000..6bbb92ad --- /dev/null +++ b/scripts/Assets/tables/mlinput/2016/03/20/000000_0 @@ -0,0 +1,41 @@ +003fc3f0-bd9b-4673-bd33-a343940a326d,1,2016-03-20 08:11:06,0.0,0.0,1.0 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08:10:24,0.0,0.0,\N +98346ebc-96e7-4f8a-baf7-633a1e88344b,1,2016-03-20 08:10:50,0.0,0.0,0.0 +a0eb0031-c4af-4049-b225-798e3b08c8e1,Facebook:203538940012375,2016-03-20 06:07:02,1247.2508898462338,180.18914753360949,0.2962962962962963 +af3beb3a-4238-43e5-9a23-f6dc43e4ed10,Facebook:203538940012375,2016-03-20 08:45:50,438.4281583110282,11.180339887498949,0.5 +b0132ff6-8e92-4d7b-a6b3-0ceaef3f9afb,1,2016-03-20 00:03:00,0.0,0.0,0.0 +b1bdbefa-2833-4225-bde2-550041fbdaea,1,2016-03-20 16:11:17,0.0,0.0,0.0 +b58afdba-f60b-42ae-bca9-8c4d7f74c6be,1,2016-03-20 16:10:56,0.0,0.0,0.0 +b8a3500c-89df-47a0-9a22-381e5acf3575,1,2016-03-20 08:10:59,0.0,0.0,0.0 +b94dca78-6859-47f5-bef1-a7f679bbaf89,1,2016-03-20 16:10:47,0.0,0.0,1.0 +c05899dd-267b-453b-b244-69445f71e3ce,1,2016-03-20 08:10:28,0.0,0.0,0.0 +c08d750e-d7e5-4afc-9514-dfb258f01a94,1,2016-03-20 08:11:01,0.0,0.0,1.0 +c0dcda52-a310-4632-ab77-94e783a8b2f0,Facebook:203538940012375,2016-03-20 09:23:19,1203.6208336146026,32.50337114519768,0.125 +c6e149b4-ff1e-45d5-bbed-26bf42c5c115,Facebook:203538940012375,2016-03-20 06:45:49,1314.5186262798125,154.01090606655845,0.3939393939393939 +d2f1b183-44e9-48fa-8c29-a8374c797490,1,2016-03-20 16:10:37,0.0,0.0,0.0 +d7455553-bce5-4207-ab91-94e650562f81,1,2016-03-20 08:10:44,0.0,0.0,\N +da877def-9408-4852-b71b-bf1b109c105c,1,2016-03-20 08:10:47,0.0,0.0,0.0 +e6f97648-f834-42a6-8451-53651bec0eed,Facebook:203538940012375,2016-03-20 08:43:53,1201.5935419267198,65.98297918150307,0.3157894736842105 +f064e7f8-8e35-48e7-aa66-cc936486f9a2,Facebook:10156612592435524,2016-03-20 14:47:04,1021.2743754066578,27.507708244341824,0.23076923076923078 +f09627ce-5efc-4977-8172-855545334c58,1,2016-03-20 16:10:39,0.0,0.0,0.0 +f21413fb-d216-47e4-8b72-bf2ea7a2e7ef,1,2016-03-20 16:11:23,0.0,0.0,0.0 +f50dad83-430e-4e19-a959-647ba75bdfb1,Facebook:203538940012375,2016-03-20 08:46:03,423.55456817536776,11.197718021493882,0.16666666666666666 From 1f45b8e8c8c349128f64a8e3098179ecbd0dfdb4 Mon Sep 17 00:00:00 2001 From: Nahuel Beni Date: Mon, 28 Mar 2016 23:10:10 -0300 Subject: [PATCH 09/19] added logic to upload sample data to blob storage --- scripts/Bash/deploy.sh | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/scripts/Bash/deploy.sh b/scripts/Bash/deploy.sh index 545dfb30..4f92bf84 100644 --- a/scripts/Bash/deploy.sh +++ b/scripts/Bash/deploy.sh @@ -7,7 +7,8 @@ DEPLOYMENTNAME="scenario_complete-$(date -u +%m%d-%H%M)" TEMPLATEFILE="../ARM/scenario_complete.nocomments.json" PREREQ_TEMPLATEFILE="../ARM/prerequisites.nocomments.json" PARAMETERSFILE="../ARM/scenario_complete.params.nocomments.json" -ASSETS_DIRECTORY="../../src/HDInsight" +HIVE_SCRIPTS_DIRECTORY="../../src/HDInsight" +SAMPLE_DATA_DIRECTORY="../../Assets" SQLSERVER_DDL_SCRIPT="../../src/SQLDatabase/MyDrivingDB.sql" SQLANALYTICS_DDL_SCRIPT="../../src/SQLDatabase/MyDrivingAnalyticsDB.sql" @@ -129,13 +130,13 @@ echo "* Uploading files to blob storage..." echo "**************************************************************************************************" # uploads files in a specified directory uploadAssets() { - for i in "$1"/*;do + for i in "$4"/*;do if [ -d "$i" ];then - uploadAssets "$i" + uploadAssets $1 $2 $3 "$i" elif [ -f "$i" ]; then - blobName=${i: ${#ASSETS_DIRECTORY} + 1} + blobName=${i: ${#3} + 1} echo "Uploading file '$blobName' to blob storage..." - azure storage blob upload -q $i $ASSETS_CONTAINER_NAME $blobName + azure storage blob upload -a $1 -k $2 -q $i $3 $blobName fi done } @@ -145,8 +146,8 @@ echo "Creating the '$ASSETS_CONTAINER_NAME' blob container to store assets..." azure storage container create $ASSETS_CONTAINER_NAME || : # upload the assets to the container -echo "Copying the assets in '$ASSETS_DIRECTORY' to blob storage..." -uploadAssets $ASSETS_DIRECTORY +echo "Copying the hive scripts in '$HIVE_SCRIPTS_DIRECTORY' to blob storage..." +uploadAssets $AZURE_STORAGE_ACCOUNT $AZURE_STORAGE_ACCESS_KEY $ASSETS_CONTAINER_NAME $HIVE_SCRIPTS_DIRECTORY # create the deployment with the solution template echo @@ -180,6 +181,7 @@ eval $(echo "$OUTPUT1" | awk -F ': ' \ $2 ~ /tripdataContainerName/ {split($2, a, " "); print "export TRIP_DATA_CONTAINER_NAME=" a[3] ";"} \ $2 ~ /referenceContainerName/ {split($2, a, " "); print "export REFERENCE_CONTAINER_NAME=" a[3] ";"}') + echo echo "**************************************************************************************************" echo "* Initializing blob storage..." @@ -196,6 +198,10 @@ azure storage container create $TRIP_DATA_CONTAINER_NAME -a $STORAGE_ACCOUNT_NAM echo "Creating the '$REFERENCE_CONTAINER_NAME' blob container..." azure storage container create $REFERENCE_CONTAINER_NAME -a $STORAGE_ACCOUNT_NAME_ANALYTICS -k $STORAGE_ACCOUNT_KEY_ANALYTICS || : +# upload the assets to the container +echo "Copying the sample data files '$SAMPLE_DATA_DIRECTORY' to blob storage..." +uploadAssets $STORAGE_ACCOUNT_NAME_ANALYTICS $STORAGE_ACCOUNT_KEY_ANALYTICS $REFERENCE_CONTAINER_NAME $SAMPLE_DATA_DIRECTORY + # Initialize SQL databases echo echo "**************************************************************************************************" From 39a70ba020f25e6ee234fe10a6bc8992819a63c1 Mon Sep 17 00:00:00 2001 From: Nahuel Beni Date: Tue, 29 Mar 2016 02:04:39 -0300 Subject: [PATCH 10/19] minor fixes to bash script --- scripts/Bash/deploy.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/Bash/deploy.sh b/scripts/Bash/deploy.sh index 4f92bf84..39296660 100644 --- a/scripts/Bash/deploy.sh +++ b/scripts/Bash/deploy.sh @@ -8,7 +8,7 @@ TEMPLATEFILE="../ARM/scenario_complete.nocomments.json" PREREQ_TEMPLATEFILE="../ARM/prerequisites.nocomments.json" PARAMETERSFILE="../ARM/scenario_complete.params.nocomments.json" HIVE_SCRIPTS_DIRECTORY="../../src/HDInsight" -SAMPLE_DATA_DIRECTORY="../../Assets" +SAMPLE_DATA_DIRECTORY="../Assets" SQLSERVER_DDL_SCRIPT="../../src/SQLDatabase/MyDrivingDB.sql" SQLANALYTICS_DDL_SCRIPT="../../src/SQLDatabase/MyDrivingAnalyticsDB.sql" @@ -134,7 +134,7 @@ uploadAssets() { if [ -d "$i" ];then uploadAssets $1 $2 $3 "$i" elif [ -f "$i" ]; then - blobName=${i: ${#3} + 1} + blobName=${i: ${#4} + 1} echo "Uploading file '$blobName' to blob storage..." azure storage blob upload -a $1 -k $2 -q $i $3 $blobName fi @@ -200,7 +200,7 @@ azure storage container create $REFERENCE_CONTAINER_NAME -a $STORAGE_ACCOUNT_NAM # upload the assets to the container echo "Copying the sample data files '$SAMPLE_DATA_DIRECTORY' to blob storage..." -uploadAssets $STORAGE_ACCOUNT_NAME_ANALYTICS $STORAGE_ACCOUNT_KEY_ANALYTICS $REFERENCE_CONTAINER_NAME $SAMPLE_DATA_DIRECTORY +uploadAssets $STORAGE_ACCOUNT_NAME_ANALYTICS $STORAGE_ACCOUNT_KEY_ANALYTICS $TRIP_DATA_CONTAINER_NAME $SAMPLE_DATA_DIRECTORY # Initialize SQL databases echo From 32156388602f610aa61b8060b96ee798e946a535 Mon Sep 17 00:00:00 2001 From: Nahuel Beni Date: Tue, 29 Mar 2016 13:15:02 -0300 Subject: [PATCH 11/19] fixed script --- scripts/Bash/deploy.sh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/Bash/deploy.sh b/scripts/Bash/deploy.sh index 39296660..d8970614 100644 --- a/scripts/Bash/deploy.sh +++ b/scripts/Bash/deploy.sh @@ -132,9 +132,9 @@ echo "************************************************************************** uploadAssets() { for i in "$4"/*;do if [ -d "$i" ];then - uploadAssets $1 $2 $3 "$i" + uploadAssets $1 $2 $3 "$i" $5 elif [ -f "$i" ]; then - blobName=${i: ${#4} + 1} + blobName=${i: ${#5} + 1} echo "Uploading file '$blobName' to blob storage..." azure storage blob upload -a $1 -k $2 -q $i $3 $blobName fi @@ -147,7 +147,7 @@ azure storage container create $ASSETS_CONTAINER_NAME || : # upload the assets to the container echo "Copying the hive scripts in '$HIVE_SCRIPTS_DIRECTORY' to blob storage..." -uploadAssets $AZURE_STORAGE_ACCOUNT $AZURE_STORAGE_ACCESS_KEY $ASSETS_CONTAINER_NAME $HIVE_SCRIPTS_DIRECTORY +uploadAssets $AZURE_STORAGE_ACCOUNT $AZURE_STORAGE_ACCESS_KEY $ASSETS_CONTAINER_NAME $HIVE_SCRIPTS_DIRECTORY $HIVE_SCRIPTS_DIRECTORY # create the deployment with the solution template echo @@ -200,7 +200,7 @@ azure storage container create $REFERENCE_CONTAINER_NAME -a $STORAGE_ACCOUNT_NAM # upload the assets to the container echo "Copying the sample data files '$SAMPLE_DATA_DIRECTORY' to blob storage..." -uploadAssets $STORAGE_ACCOUNT_NAME_ANALYTICS $STORAGE_ACCOUNT_KEY_ANALYTICS $TRIP_DATA_CONTAINER_NAME $SAMPLE_DATA_DIRECTORY +uploadAssets $STORAGE_ACCOUNT_NAME_ANALYTICS $STORAGE_ACCOUNT_KEY_ANALYTICS $TRIP_DATA_CONTAINER_NAME $SAMPLE_DATA_DIRECTORY $SAMPLE_DATA_DIRECTORY # Initialize SQL databases echo From f04dd31994aa136c61c6661a2ab15e0e813eb522 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Tue, 29 Mar 2016 10:56:01 -0700 Subject: [PATCH 12/19] Reviewed README and added some screenshots --- scripts/README.md | 121 +++++++++++++++++++++++++++------------------- 1 file changed, 70 insertions(+), 51 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index f8b78d5d..ced4798b 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -16,7 +16,7 @@ Auto-deploy scripts allow you to deploy the entire starter kit service set on Az * [An active Azure subscription](https://azure.microsoft.com) with at least 24 available cores (for on-demand HDInsight cluster) ### Azure Management Certificate -When copying Machine Learning work spaces, the script uses an Azure management certificate for authentication. You'll need to create and upload a management certificate and provides the certificate's thumbprint to the script when prompted. For details on creating a management certificate, please see [this article](https://azure.microsoft.com/en-us/documentation/articles/cloud-services-certs-create/). +When copying Machine Learning workspaces, the script uses an Azure management certificate for authentication. You'll need to create and upload a management certificate and provide the certificate's thumbprint to the script when prompted. For details on creating a management certificate, please see [this article](https://azure.microsoft.com/en-us/documentation/articles/cloud-services-certs-create/). ## Use PowerShell script Follow these steps to deploy the starter kit using PowerShell: @@ -27,10 +27,13 @@ Follow these steps to deploy the starter kit using PowerShell: * _< location >_ is the Azure datacenter where you want the services to be deployed, such as "WestUS". * _< resource group name >_ is the name of the deployed resource group. + 2. During deployment, the script will ask you to provide two SQL Database passwords: **sqlServerAdminPassword** and **sqlAnalyticsServerAdminPassword**. The first password is for the Mobile App back end database; the second password is for the analytic database that supports Power BI queries. + 3. Before the Machine Learning workspaces are imported, you'll be asked to provide the thumbprint of your Azure management certificate. Paste in the thumbprint and press [Enter] to continue. If you press [Enter] directly, the script will attempt to sign in using AAD. You can use the latter option if you are the owner of your subscription. + 4. Once the service deployment is complete, the script will ask if you want to provision the Visual Studio Team Services continuous integration pipelines. If you answer 'y' (for yes), you'll be prompted to enter the following values before it copies the MyDriving source code to the specified local folder, creates a new VSTS project, checks in the source to the project, and creates all build definitions: - * **your VSTS account**: The name of your VSTS account. It should have format of _https://[account name].visualstudio.com_. + * **your VSTS account**: The name of your VSTS account. It should have format of _https://[account-name].visualstudio.com_. * **your PAT**: The personal access token (see [http://blog.devmatter.com/personal-access-tokens-vsts/](http://blog.devmatter.com/personal-access-tokens-vsts/)). * **project name**: The name of the VSTS project to be created. * **local working folder**: The local folder where the MyDriving source code will be copied to. @@ -42,8 +45,11 @@ Follow these steps to deploy the starter kit using PowerShell: Follow these steps to deploy the starter kit using Bash: 1. Install [Node.js](http://nodejs.org). + 1. Install the [Azure CLI](https://azure.microsoft.com/en-us/documentation/articles/xplat-cli-install/). + 1. Open a Terminal window and go to the **scripts/bash** folder. + 1. Install the required dependencies. ``` @@ -52,7 +58,7 @@ Follow these steps to deploy the starter kit using Bash: 1. Open **/scripts/ARM/scenario_complete.params.nocomments.json** in a text editor and update the **sqlServerAdminPassword** and **sqlAnalyticsServerAdminPassword** parameters. The first password is for the Mobile App back end database; the second password is for the analytic database that supports Power BI queries. Choose a suitable password for each one. - Next, set **dataFactoryStartDate** and **dataFactoryEndDate** to specify the period during which you intend to run the solution. Save the updated file. + Next, set **dataFactoryStartDate** and **dataFactoryEndDate** to specify the period during which you intend to run the Azure Data Factory pipelines. Save the updated file. ``` "parameters": { @@ -65,6 +71,7 @@ Follow these steps to deploy the starter kit using Bash: "dataFactoryEndDate": { "value": "" ] } ``` + 1. Launch the deployment script. ``` @@ -79,34 +86,74 @@ Follow these steps to deploy the starter kit using Bash: ### Start Azure Stream Analytics jobs -1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select **mydriving-archive**. +1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select the **mydriving-archive** job. + 2. Click the **START** button at the bottom of the page. + 3. Similarly, start the **mydriving-vinlookup** job. +### Configure Azure Streaming Analytics Power BI Outputs + +1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select the **mydriving-hourlypbi** job. + +1. Click the **STOP** button at the bottom of the page. You need to stop the job in order to add a new output. + +1. Click **OUTPUTS** at the top of the page, and then click **Add Output**. + +1. In the **Add an Output** dialog box, select **Power BI** and then click next. + +1. In the **Add a Microsoft Power BI output**, supply a work or school account for the Stream Analytics job output. If you already have a Power BI account, select **Authorize Now**. If not, choose **Sign up now**. + + ![Adding a Power BI output](Images/adding-powerbi-output.png?raw=true "Adding a Power BI output") + + _Adding a Power BI output_ + +1. Next, set the following values and click the checkmark: + + - **Output Alias**: PowerBiSink + - **Dataset Name**: ASA-HourlyData + - **Table Name**: HourlyTripData + - **Workspace**: You can use the default + + ![Power BI output settings](Images/asa-powerbi-output-settings.png?raw=true "Power BI output settings") + + _Power BI output settings_ + +1. Click the **START** button to restart the job. + +1. Repeat the same steps to configure the **mydriving-sqlpbi** job using the following values: + + - **Output Alias**: PowerBiSink + - **Dataset Name**: MyDriving-ASAdataset + - **Table Name**: TripPointData + - **Workspace**: You can use the default + +1. Make sure that **mydriving-archive**, **mydriving-sqlpbi**, **mydriving-hourlypbi**, and **mydriving-vinlookup** jobs are all running. + ### Machine Learning Configuration -1. At the portal, obtain the credentials for the storage account whose name is prefixed with **"mydrivingstr"**. It can be found under the resource group of the solution. +1. In the [Azure portal](https://portal.azure.com), obtain the credentials for the storage account whose name is prefixed with **"mydrivingstr"**. It can be found under the resource group of the solution. ![Storage Account](Images/storage-account.png?raw=true "Storage Account") _Storage Account_ -1. Go to the classic portal, select the **Machine Learning** service and then the **mydriving** workspace. Open the workspace by selecting **Sign-in to ML Studio**. +1. Go to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Open the workspace by selecting **Sign-in to ML Studio**. -1. Click the **Reader** module at the top of the experiment and set the **Account Name** and **Account Key** properties to the values obtained previously. +1. Click the **Reader** module at the top of the experiment diagram to select it and in the **Properties** pane, set the **Account Name** and **Account Key** properties to the values obtained previously. ![Configuring the ML Storage Account](Images/ml-storage-account.png?raw=true "Configuring the ML Storage Account") _Configuring the ML Storage Account_ -1. Click **Run** to run the **MyDriving** experiment. +1. Click **Run** at the bottom of the page to run the **MyDriving** experiment. -1. Now, select the **Train Model** module, click **Setup Web Service**, and then **Deploy Web Service**. Reply **yes** when prompted for confirmation. +1. Once the run is complete, select the **Train Model** module in the diagram, click **Setup Web Service**, and then **Deploy Web Service**. Reply **yes** when prompted for confirmation. ![Deploying an ML Web Service](Images/ml-deploy-web-service.png?raw=true "Deploying an ML Web Service") _Deploying an ML Web Service_ -1. Go back to the classic portal, select the **Machine Learning** service and then the **mydriving** workspace. Now switch to the **Web Services** tab and select the **MyDriving** web service. +1. Go back to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Now switch to the **Web Services** tab and select the **MyDriving** web service. ![Configuring ML Web Services](Images/ml-web-services.png?raw=true "Configuring ML Web Services") @@ -122,17 +169,18 @@ Follow these steps to deploy the starter kit using Bash: 1. Next, click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. -1. Make a note of the API Key and web service endpoint. You'll need them later to configure the Data Factory's **TrainingEndpoint-AMLLinkedService** linked service. +1. Make a note of the API Key and the request URI of the **retrain** endpoint. You'll need these values later to configure the Data Factory's **TrainingEndpoint-AMLLinkedService** linked service. ### Azure Data Factory configuration -1. At the portal, select the resource group where the solution is deployed and under **Resources**, select the Data Factory resource. +1. In the [Azure portal](https://portal.azure.com), select the resource group where the solution is deployed and under **Resources**, select the Data Factory resource. ![Data Factory](Images/data-factory.png?raw=true "Data Factory") _Data Factory_ 1. In the data factory blade, select the **Author and deploy** action. + 1. In the authoring blade, expand **Linked Services** and then select **AzureMLScoringandUpdateLinkedService**. ![Configuring the Data Factory](Images/configuring-data-factory.png?raw=true "Configuring the Data Factory") @@ -148,47 +196,15 @@ Follow these steps to deploy the starter kit using Bash: - **mlEndpoint**: [TBD] endpoint for the Machine Learning experiment - **apiKey**: [TBD] API key - **updateResourceEndpoint**: [TBD] + 1. Click **Deploy**. 1. Next, under **Linked Services**, select **TrainingEndpoint-AMLLinkedService** and enter the values of the **mlEndpoint** and **apiKey** of the **retrain** endpoint you obtained earlier when you configured the ML experiment. Click **Deploy**. - -### Configuring Power BI Outputs for Azure Streaming Analytics - -1. In the [Azure classic portal](https://manage.windowsazure.com/), go to **Stream Analytics** and select **mydriving-hourlypbi**. - -1. Click the **STOP** button at the bottom of the page. You need to stop the job in order to add a new output. - -1. Click **OUTPUTS** at the top of the page, and then click **Add Output**. - -1. In the **Add an Output** dialog box, select **Power BI** and then click next. - -1. In the **Add a Microsoft Power BI output**, supply a work or school account for the Stream Analytics job output. If you already have a Power BI account, select **Authorize Now**. If not, choose **Sign up now**. - - ![Adding a Power BI output](Images/adding-powerbi-output.png?raw=true "Adding a Power BI output") - - _Adding a Power BI output_ - -1. Next, set the following values: - - - **Output Alias**: PowerBiSink - - **Dataset Name**: ASA-HourlyData - - **Table Name**: HourlyTripData - - **Workspace**: You can use the default - -1. Click the **START** button to restart the job. - -1. Repeat the same steps to configure the **mydriving-sqlpbi** Stream Analytics job using the following values: - - - **Output Alias**: PowerBiSink - - **Dataset Name**: MyDriving-ASAdataset - - **Table Name**: TripPointData - - **Workspace**: You can use the default - -1. Make sure that **mydriving-archive**, **mydriving-sqlpbi**, **mydriving-hourlypbi**, and **mydriving-vinlookup** are all running. ### Machine Learning configuration 1. Open the Machine Learning workspace (named MyDriving). + 2. Modify the Reader components in both experiments to enter a storage account key. The storage account is under the same resource group and should have a name prefixed with **"mydrivingstr"**. ![Storage Account](Images/storage-account.png?raw=true "Storage Account") @@ -213,7 +229,7 @@ For example: .\copyMLExperiment.ps1 "[your Azure subscription Id]" "[name of the workspace to be created]" "[location]" "[owner email]" "[storage account name]" "[storage account key]" "[experiement package URL (see above)]" ``` -The PowerShell script also provides other useful functions with several other tasks, such as finding a work space/experiment, and packaging an experiment. For example, to package an existing experiment (so that it can be unpacked to a new work space), use the following cmdlet: +The PowerShell script also provides other useful functions with several other tasks, such as finding a workspace/experiment, and packaging an experiment. For example, to package an existing experiment (so that it can be unpacked to a new workspace), use the following cmdlet: ``` ExportExperiment "[subscription id]" "[workspace name]" "[experiment name]" "[ML key]" @@ -225,13 +241,14 @@ ExportExperiment "[subscription id]" "[workspace name]" "[experiment name]" "[ML This script copies the MyDriving source code into a working folder, creates a new VSTS project, checks in the code into the new project, and then imports all build definitions under the **scripts\VSTS** folder. Some build steps such as downloading certificates are removed from the build pipelines. After you import the build pipelines, you should review them and make the necessary adjustments before using them. 1. Before using the following script to import the build definitions, you'll need to create a Personal Access Token (PAT) following the instructions on this page: [http://blog.devmatter.com/personal-access-tokens-vsts/](http://blog.devmatter.com/personal-access-tokens-vsts/). + 2. Launch **importVSTSBuildDefinition.ps1** under the **scripts\PowerShell\scripts** folder: ``` .\importVSTSBuildDefinition.ps1 "" "" "" "" "" ``` - * _< your VSTS account >_ is the name of your VSTS account. It should have format of _https://[account name].visualstuio.com_. + * _< your VSTS account >_ is the name of your VSTS account. It should have format of _https://[account-name].visualstudio.com_. * _< your PAT >_ is the PAT you generated in step 1. * _< project name >_ is the name of the VSTS project to be created. * _< Folder to the build definition file >_ is the folder that holds exported build definition (JSON) files. You should point this to the **scripts\VSTS** folder. @@ -243,6 +260,7 @@ Service Fabric is used as one of the possible extension processing unit hosts. I It's recommended that you protect your Service Fabric with a certificate. We provide a PowerShell script that helps you to create a self-signed certificate for testing purposes. For production environments, you should use a certificate from a trusted CA. 1. In Windows PowerShell, sign in to your Azure subscription using the **Login-AzureRmAccount** cmdlet. + 2. Launch **setupCert.ps1** under the **scripts\PowerShell\scripts** folder: ``` @@ -262,6 +280,7 @@ It's recommended that you protect your Service Fabric with a certificate. We pro ``` .\setupCert.ps1 mytest.westus.cloudapp.azure.com P@ssword456 testVault1h testKey1h testgrp "West US" c:\src\abc.pfx ``` + 3. The above script generates several outputs, including the resource id of the Key Vault, the secret id and the certificate thumbprint. For example: ``` @@ -269,14 +288,14 @@ It's recommended that you protect your Service Fabric with a certificate. We pro Secret Id: + https://testvault1h.vault.azure.net:443/secrets/testKeyh/xxxxxxxxxxxxxxxxx Cert Thumbprint: xxxxxxxxxxx ``` + 4. Use the Microsoft Azure Portal to create a new Service Fabric cluster. When configuring cluster security, enter the information items in step 3. + 5. Import the certificate to the Trusted People store. ``` Import-PfxCertificate -Exportable -CertStoreLocation Cert:\CurrentUser\TrustedPeople -FilePath '[path to the .pfx file]' -Password $password ``` 6. Open **src\Extensions\ServiceFabirc\VINLookUpApplicaiton\VINLookUpApplicaiton.sln** in Visual Studio 2015. -7. Right-click **VINLookUpApplication** and select the **Publish** menu to publish the application. Select the Service Fabric cluster you provisioned. - - +7. Right-click **VINLookUpApplication** and select the **Publish** menu to publish the application. Select the Service Fabric cluster you provisioned. 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Make sure that **mydriving-archive**, **mydriving-sqlpbi**, **mydriving-hourlypbi**, and **mydriving-vinlookup** jobs are all running. ### Machine Learning Configuration -1. In the [Azure portal](https://portal.azure.com), obtain the credentials for the storage account whose name is prefixed with **"mydrivingstr"**. It can be found under the resource group of the solution. +1. Before you can proceed, you need to obtain the credentials for the storage account and SQL databases in the solution. Go + to the [Azure portal](https://portal.azure.com), click **Resource Groups**, select the solution's resource group, then **All Settings**, and then **Resources**. + +1. In the list of resources, click the storage account whose name is prefixed with **"mydrivingstr"**, then **All Settings**, and then **Access Keys**. Make a note of the **Storage Account Name** and **Key1**. - ![Storage Account](Images/storage-account.png?raw=true "Storage Account") + ![Storage Account Credentials](Images/storage-account-credentials.png?raw=true "Storage Account Credentials") - _Storage Account_ + _Storage Account Credentials_ -1. Go to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Open the workspace by selecting **Sign-in to ML Studio**. +1. Next, locate the **myDrivingAnalyticsDB** SQL Database in the solution, open its **All Settings** blade, and then its **Properties** blade. Make a note of the, database name, **Server Name** and **Server Admin Login** properties. + + ![SQL Database Credentials](Images/sql-database-credentials.png?raw=true "SQL Database Credentials") + + _SQL Database Credentials_ + +1. Repeat the previous step to obtain the database name, **Server Name** and **Server Admin Login** properties of the **myDrivingDB** SQL Database. + +1. Now, go to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Open the workspace by selecting **Sign-in to ML Studio**. -1. Click the **Reader** module at the top of the experiment diagram to select it and in the **Properties** pane, set the **Account Name** and **Account Key** properties to the values obtained previously. +1. Click the **Reader** module at the top of the experiment diagram to select it and in the **Properties** pane, set the **Account Name** and **Account Key** properties to the storage account values obtained previously. - ![Configuring the ML Storage Account](Images/ml-storage-account.png?raw=true "Configuring the ML Storage Account") + ![Configuring the ML Reader](Images/ml-configure-reader.png?raw=true "Configuring the ML Reader") - _Configuring the ML Storage Account_ + _Configuring the ML Reader_ 1. Click **Run** at the bottom of the page to run the **MyDriving** experiment. @@ -152,8 +163,22 @@ Follow these steps to deploy the starter kit using Bash: ![Deploying an ML Web Service](Images/ml-deploy-web-service.png?raw=true "Deploying an ML Web Service") _Deploying an ML Web Service_ + +1. Switch to the **Predictive Experiment** tab and configure the **Reader** module property by updating the **Account Name** and **Account Key** properties with the same storage account information that you used previously to configure the **Training Experiment**. + +1. Select one of the two **Writer** modules in the diagram and in the **Properties** pane, update the **Database server name**, **Server user account name**, and **Server user account password** properties with the values obtained previously. Use the values corresponding to the database shown in the **Database name** property. For the **Server user account name** set the value as <user name>@<server name>. + + ![Configuring the ML Writer](Images/ml-configure-writer.png?raw=true "Configuring the ML Writer") + + _Configuring the ML Writer_ + +1. Repeat the previous step to configure the **Writer** module for the other database. + +1. Now, click **Run** + +1. After the run completes, click **Deploy Web Service**. -1. Go back to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Now switch to the **Web Services** tab and select the **MyDriving** web service. +1. Go back to the [Azure classic portal](https://manage.windowsazure.com/), select the **Machine Learning** service and then the **mydriving** workspace. Now switch to the **Web Services** tab and select the **MyDriving [Predictive Exp.]** web service. ![Configuring ML Web Services](Images/ml-web-services.png?raw=true "Configuring ML Web Services") @@ -165,11 +190,19 @@ Follow these steps to deploy the starter kit using Bash: _Adding an ML Web Service Endpoint_ -1. Select the newly added endpoint and copy the API key shown in the dashboard, under the **Quick Glance** section. +1. Click **retrain** in the list of endpoints to shown its **Dashboard** and then copy the API key , under the **Quick Glance** section. + +1. Click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. + +1. Return to the **retrain** endpoint dashboard, click **UPDATE RESOURCE** and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. + +1. Keep a record of the API Key and the batch execution and update resource request URIs of the **retrain** endpoint. You'll need these values later to configure the Data Factory's **AzureMLScoringandUpdateLinkedService** linked service. + +1. Return to the **Web Services** list and select the **MyDriving** web service, then select the **default** endpoint to show its **Dashboard**, and then copy the API key , under the **Quick Glance** section. -1. Next, click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. +1. Click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. -1. Make a note of the API Key and the request URI of the **retrain** endpoint. You'll need these values later to configure the Data Factory's **TrainingEndpoint-AMLLinkedService** linked service. +1. Keep a record of the API Key and the batch execution request URI of the **default** endpoint. You'll need these values later to configure the Data Factory's **TrainingEndpoint-AMLLinkedService** linked service. ### Azure Data Factory configuration @@ -187,35 +220,24 @@ Follow these steps to deploy the starter kit using Bash: _Configuring the Data Factory_ -1. Update the linked service definition by entering the following information from the Machine Learning experiment. +1. Update the linked service definition by entering the information that you obtained previously from the **retrain** endpoint of the **MyDriving [Predictive Exp.]** web service. + + - **mlEndpoint**: request URI of the batch execution operation for the **retrain** endpoint of the **MyDriving [Predictive Exp.]** web service + - **apiKey**: API key for the **retrain** endpoint of the **MyDriving [Predictive Exp.]** web service + - **updateResourceEndpoint**: request URI of the update resource operation for the **retrain** endpoint of the **MyDriving [Predictive Exp.]** web service ![Configuring a Linked Service](Images/configuring-linked-service.png?raw=true "Configuring a Linked Service") _Configuring a Linked Service_ - - **mlEndpoint**: [TBD] endpoint for the Machine Learning experiment - - **apiKey**: [TBD] API key - - **updateResourceEndpoint**: [TBD] - 1. Click **Deploy**. -1. Next, under **Linked Services**, select **TrainingEndpoint-AMLLinkedService** and enter the values of the **mlEndpoint** and **apiKey** of the **retrain** endpoint you obtained earlier when you configured the ML experiment. Click **Deploy**. - -### Machine Learning configuration +1. Next, under **Linked Services**, select **TrainingEndpoint-AMLLinkedService** and update its definition by entering the information that you obtained previously from the **default** endpoint of the **MyDriving** web service. -1. Open the Machine Learning workspace (named MyDriving). + - **mlEndpoint**: request URI of the batch execution operation of the **default** endpoint of the **MyDriving** web service + - **apiKey**: API key for the **default** endpoint of the **MyDriving** web service -2. Modify the Reader components in both experiments to enter a storage account key. The storage account is under the same resource group and should have a name prefixed with **"mydrivingstr"**. - - ![Storage Account](Images/storage-account.png?raw=true "Storage Account") - - _Storage Account_ - -3. Modify the Writer components in both experiments to enter correct SQL Database connection info. You need to provide correct SQL Database server names and login credentials for both mydrivingDB database (on a server with a "mydrivingdbserver" prefix) and mydrivingAnalyticsDB (on a server with a "mydriving-dbserver" prefix). - - ![SQL Databases](Images/sql-databases.png?raw=true "SQL Databases") - - _SQL Databases_ +1. Click **Deploy**. ##### _Additional Information_ You can use the supplied **scripts\PowerShell\scripts\copyMLExperiment.ps1** to import previously packaged ML experiments at these locations: From cdc14d89a3c13d20a266ced4d5b105d0b3201a74 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Tue, 29 Mar 2016 17:21:34 -0700 Subject: [PATCH 19/19] A few minor updates to README --- scripts/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index c38169bf..5bdb54b1 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -166,7 +166,7 @@ Follow these steps to deploy the starter kit using Bash: 1. Switch to the **Predictive Experiment** tab and configure the **Reader** module property by updating the **Account Name** and **Account Key** properties with the same storage account information that you used previously to configure the **Training Experiment**. -1. Select one of the two **Writer** modules in the diagram and in the **Properties** pane, update the **Database server name**, **Server user account name**, and **Server user account password** properties with the values obtained previously. Use the values corresponding to the database shown in the **Database name** property. For the **Server user account name** set the value as <user name>@<server name>. +1. Select one of the two **Writer** modules in the diagram and in the **Properties** pane, update the **Database server name**, **Server user account name**, and **Server user account password** properties with the values obtained previously. Use the values corresponding to the database shown in the **Database name** property. For the **Server user account name** set the value as <user name>@<server name>. Use the password that you specified when you ran the deployment script. ![Configuring the ML Writer](Images/ml-configure-writer.png?raw=true "Configuring the ML Writer") @@ -190,7 +190,7 @@ Follow these steps to deploy the starter kit using Bash: _Adding an ML Web Service Endpoint_ -1. Click **retrain** in the list of endpoints to shown its **Dashboard** and then copy the API key , under the **Quick Glance** section. +1. Click **retrain** in the list of endpoints to shown its **Dashboard** and then copy the API key, under the **Quick Glance** section. 1. Click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation. @@ -198,7 +198,7 @@ Follow these steps to deploy the starter kit using Bash: 1. Keep a record of the API Key and the batch execution and update resource request URIs of the **retrain** endpoint. You'll need these values later to configure the Data Factory's **AzureMLScoringandUpdateLinkedService** linked service. -1. Return to the **Web Services** list and select the **MyDriving** web service, then select the **default** endpoint to show its **Dashboard**, and then copy the API key , under the **Quick Glance** section. +1. Return to the **Web Services** list and select the **MyDriving** web service, then select the **default** endpoint to show its **Dashboard**, and then copy the API key, under the **Quick Glance** section. 1. Click **BATCH EXECUTION** to open the API documentation page and copy the **Request URI** of the **Submit (but not start) a Batch Execution job** operation.

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zskyZ^SXr^mNs-+2`|;8M69iv88tY-#)m^ z>%}6RM2W7uHKbC*OuF|dEJtR@&15tmnEOmVsw4+mk&iW~Y1e5iXI$ArE#r7K_!43DmTq&$1q>piVyQ*~d6( zn_IAOq|<8k?rAMvExzr3x-%d_teSt(rVv_|=Te>{1VcN7#t#fWi{!ilCPhVsU0u-l zV5+p1x{eNsKpG8TJV6sMv6(cK=<>1%eOKd$iVVD3Bk9KHY6|<^Zu4QU{WM-29@s zxp_&SN!o3HW#o0!Iy&Yu2Vv)+l{yz%EV13jG2d8m6?@-U|1Miglo9&;MbFugR9MF6 zhH|I#=i!hXTII9ei#Zdy(nODnH{oO1{zlc_5g9Gt4{X;CyB!ei+)4&F8rAGSM__2g z7CgRQduE}OnFW=1ZwRxBSoqL4Fo3ru=&#$9^tC;dv+5Vk5`xVR#pe~i#_ZYAz&jS7 zF%V{%w;whPnA9~vfI%jVLZOhID7vB3x6+OpZ1g+`8s-$)^r>I(ZUcqtB3HHzjOPg^ z&_VQD!lvEkY=^#gRj$3RCRO=l<#=KJpOTbyUhoz+& zYN0O5tubb?v9Xcv{M+?-bQOnat7JO47z8UicZU%f>Avs3KX%5R=h^~(mH^v%`o;FA zyWt7@fVKCFjC2VN4UJlRQYPn_u;UkaYVF>DvgDF+?ZpseOL1zM^Bq+$MEEzBI@;OYURfBUfN;9u20w-F3VeV^xj@B8 zFcK(j($-yk=lPZTT6-|xE+OFs8l5r}#@Pr5Tn=e=XNonjq4(jv8Sem6XT} ziHP`LxNvPM`lk?0X^S1#&GU&;HDkfpdHnp!AJCodxIdZnZ?9|rGu0i|34(xq=End From 708698bc832a6e3075b9ac58cf5b408c26ac365e Mon Sep 17 00:00:00 2001 From: HaishiBai Date: Tue, 29 Mar 2016 14:31:55 -0700 Subject: [PATCH 17/19] creating ML project --- scripts/PowerShell/deploy.ps1 | 3 +- .../PowerShell/scripts/copyMLExperiment.ps1 | 62 ++++++++++++++++--- 2 files changed, 56 insertions(+), 9 deletions(-) diff --git a/scripts/PowerShell/deploy.ps1 b/scripts/PowerShell/deploy.ps1 index 3ec1b026..b5cf1623 100644 --- a/scripts/PowerShell/deploy.ps1 +++ b/scripts/PowerShell/deploy.ps1 @@ -176,8 +176,7 @@ Write-Output "****************************************************************** $context = Get-AzureRmContext $thumbprint = Read-Host "Please provide the thumbprint of your Azure management certificate. Press [Enter] directly to sign in using AAD." -.\scripts\CopyMLExperiment.ps1 $subscription.SubscriptionId 'MyDriving' $ResourceGroupLocation $context.Account.Id $deployment1.Outputs.mlStorageAccountName.Value $deployment1.Outputs.mlStorageAccountKey.Value 'https://storage.azureml.net/directories/2e55da807f4a4273bfa99852d3d6e304/items' $thumbprint -.\scripts\CopyMLExperiment.ps1 $subscription.SubscriptionId 'MyDriving' $ResourceGroupLocation $context.Account.Id $deployment1.Outputs.mlStorageAccountName.Value $deployment1.Outputs.mlStorageAccountKey.Value 'https://storage.azureml.net/directories/a9fb6aeb3a164eedaaa28da34f02c3b0/items' $thumbprint +.\scripts\CopyMLExperiment.ps1 $subscription.SubscriptionId 'MyDriving' $ResourceGroupLocation $context.Account.Id $deployment1.Outputs.mlStorageAccountName.Value $deployment1.Outputs.mlStorageAccountKey.Value 'https://storage.azureml.net/directories/2e55da807f4a4273bfa99852d3d6e304/items' 'MyDriving' 'https://storage.azureml.net/directories/a9fb6aeb3a164eedaaa28da34f02c3b0/items' 'MyDriving [Predictive Exp.]' $thumbprint # Deploy VSTS build definitions $confirmation = Read-Host "Do you want to deploy VSTS CI? [y/n]" diff --git a/scripts/PowerShell/scripts/copyMLExperiment.ps1 b/scripts/PowerShell/scripts/copyMLExperiment.ps1 index 7c90b8c4..97a9312c 100644 --- a/scripts/PowerShell/scripts/copyMLExperiment.ps1 +++ b/scripts/PowerShell/scripts/copyMLExperiment.ps1 @@ -1,4 +1,4 @@ -Param ([string] $subscriptionId, [string] $workspaceName, [string]$location, [string]$ownerId, [string] $storageAccountName, [string] $storageAccountKey, [string] $packageLocation, [string]$thumbprint) +Param ([string] $subscriptionId, [string] $workspaceName, [string]$location, [string]$ownerId, [string] $storageAccountName, [string] $storageAccountKey, [string] $packageLocation1, [string]$packageName1, [string]$packageLocation2, [string]$packageName2, [string]$thumbprint) $global:authResult = $null @@ -14,7 +14,7 @@ function Authenticate() $resourceAppIdURI = "https://management.core.windows.net/" $authority = "https://login.windows.net/common/oauth2/authorize" $authContext = New-Object "Microsoft.IdentityModel.Clients.ActiveDirectory.AuthenticationContext" -ArgumentList $authority - $global:authResult = $authContext.AcquireToken($resourceAppIdURI, $clientId, $redirectUri, "Always") + $global:authResult = $authContext.AcquireToken($resourceAppIdURI, $clientId, $redirectUri, "Auto") } function InvokeManagementAPI([string]$subscriptionId, [string]$uri, [string]$thumbprint) @@ -57,13 +57,24 @@ function InvokeManagementAPI_PUT([string]$subscriptionId, [string]$uri, [string] return $res } -function InvokeMLAPI([string]$key, [string]$uri, [string]$method) +function InvokeMLAPI([string]$key, [string]$uri, [string]$method, [string]$json) { $URI = "https://studioapi.azureml.net/$uri" $headers = @{“x-ms-metaanalytics-authorizationtoken”= "$key"} - $res = Invoke-RestMethod -Uri $URI -Method $method -Headers $headers + if ($method -eq "POST") { + $res = Invoke-RestMethod -Uri $URI -Method $method -Headers $headers -Body $json -ContentType 'application/json' + } + else { + $res = Invoke-RestMethod -Uri $URI -Method $method -Headers $headers + } + return $res +} + +function GetDataSources([string]$key, [string]$workspaceId) +{ + $res = InvokeMLAPI $key "api/workspaces/$workspaceId/datasources" "GET" return $res } @@ -100,7 +111,33 @@ function FindExperiment([string]$key, [string]$workspaceId, [string]$experimentN } return $null } +function GetExperiment([string]$key, [string]$workspaceId, [string]$experiementId) +{ + $res = InvokeMLAPI $key "api/workspaces/$workspaceId/experiments/$experimentId" "GET" + return $res +} +function CreateProject([string]$key, [string]$projectName, [string]$workspaceId, $trainingExperiment, $scoringExperiment) +{ + $payload = @{} + + $experiment1 = @{} + $experiment1.Add("ExperimentId", $trainingExperiment.ExperimentId) + $experiment1.Add("Role", "Training") + $experiment1.Add("Experiment", $trainingExperiment) + + $experiment2 = @{} + $experiment2.Add("ExperimentId", $scoringExperiment.ExperimentId) + $experiment2.Add("Role", "Scoring") + $experiment2.Add("Experiment", $scoringExperiment) + + $array = @($experiment1, $experiment2) + $payload.Add("Experiments", $array) + + $json = $payload | ConvertTo-Json -Depth 5 + $res = InvokeMLAPI $key "api/workspaces/$workspaceId/projects" "POST" $json + +} function PackageExperiment([string]$key, [string]$workspaceId, [string]$experimentId) { @@ -148,6 +185,8 @@ function UnpackExperiment([string]$key, [string]$workspaceId, [string] $packageL } } until ($res.Status -eq "Complete") + + return $res.ExperimentId } function CreateWorkspace([string]$subscriptionId, [string]$workspaceName, [string]$location, [string]$storageAccountName, [string]$storageAccountKey, [string]$ownerId, [string]$thumbprint) @@ -166,7 +205,10 @@ function CreateWorkspace([string]$subscriptionId, [string]$workspaceName, [strin $res = InvokeManagementAPI_PUT $subscriptionId "cloudservices/$workspaceName/resources/machinelearning/~/workspaces/$guid" $json $thumbprint } -function ImportExperiment([string]$subscriptionId, [string]$workspaceName, [string]$location, [string]$storageAccountName, [string]$storageAccountKey, [string]$ownerId, [string]$packageLocation, [string]$thumbprint) +function ImportExperiment([string]$subscriptionId, [string]$workspaceName, [string]$location, [string]$storageAccountName, [string]$storageAccountKey, [string]$ownerId, + [string]$packageLocation1, [string]$experimentName1, + [string]$packageLocation2, [string]$experimentName2, + [string]$thumbprint) { if (!$thumbprint) { Authenticate @@ -184,7 +226,13 @@ function ImportExperiment([string]$subscriptionId, [string]$workspaceName, [stri $workspace = GetWorkspace $subscriptionId $workspaceName $original.Id $thumbprint - UnpackExperiment $workspace.AuthorizationToken.PrimaryToken $original.Id $packageLocation + $experimentId1 = UnpackExperiment $workspace.AuthorizationToken.PrimaryToken $original.Id $packageLocation1 + $exp1 = FindExperiment $workspace.AuthorizationToken.PrimaryToken $workspace.Id $experimentName1 + + $experimentId2 = UnpackExperiment $workspace.AuthorizationToken.PrimaryToken $original.Id $packageLocation2 + $exp2 = FindExperiment $workspace.AuthorizationToken.PrimaryToken $workspace.Id $experimentName2 + + CreateProject $workspace.AuthorizationToken.PrimaryToken $workspaceName $workspace.Id $exp1 $exp2 } @@ -215,4 +263,4 @@ function ExportExperiment([string]$subscriptionId, [string]$workspaceName, [stri #ExportExperiment "[subscription id]" "[workspace name]" "MyDriving [Predictive Exp.]" "[ML key]" -ImportExperiment $subscriptionId $workspaceName $location $storageAccountName $storageAccountKey $ownerId $packageLocation $thumbprint +ImportExperiment $subscriptionId $workspaceName $location $storageAccountName $storageAccountKey $ownerId $packageLocation1 $packageName1 $packageLocation2 $packageName2 $thumbprint From 972ce94c1c980b3974fb01880ed1c916697d75a8 Mon Sep 17 00:00:00 2001 From: Fernando Tubio Date: Tue, 29 Mar 2016 17:15:44 -0700 Subject: [PATCH 18/19] Updated Machine Learning manual setup steps --- ...ge-account.png => ml-configure-reader.png} | Bin scripts/Images/ml-configure-writer.png | Bin 0 -> 114922 bytes scripts/Images/ml-web-services.png | Bin 23131 -> 21114 bytes scripts/Images/sql-database-credentials.png | Bin 0 -> 150592 bytes .../Images/storage-account-credentials.png | Bin 0 -> 100455 bytes scripts/README.md | 84 +++++++++++------- 6 files changed, 53 insertions(+), 31 deletions(-) rename scripts/Images/{ml-storage-account.png => ml-configure-reader.png} (100%) create mode 100644 scripts/Images/ml-configure-writer.png create mode 100644 scripts/Images/sql-database-credentials.png create mode 100644 scripts/Images/storage-account-credentials.png diff --git a/scripts/Images/ml-storage-account.png b/scripts/Images/ml-configure-reader.png similarity index 100% rename from scripts/Images/ml-storage-account.png rename to scripts/Images/ml-configure-reader.png diff --git 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