microsoft · aniket-dumbare · Mar 29, 2023 · Mar 29, 2023 · Mar 29, 2023 · Mar 29, 2023
diff --git a/.env.example b/.env.example
@@ -19,7 +19,7 @@ AML_ENV_NAME='diabetes_regression_training_env'
 AML_ENV_TRAIN_CONDA_DEP_FILE="conda_dependencies.yml"
 AML_COMPUTE_CLUSTER_NAME = 'train-cluster'
 AML_COMPUTE_CLUSTER_CPU_SKU = 'STANDARD_DS2_V2'
-AML_CLUSTER_MAX_NODES = '4'
+AML_CLUSTER_MAX_NODES = '1'
 AML_CLUSTER_MIN_NODES = '0'
 AML_CLUSTER_PRIORITY = 'lowpriority'
 # Training Config
@@ -65,7 +65,7 @@ AML_ENV_NAME_SCORING='diabetes_regression_scoring_env'
 AML_ENV_NAME_SCORE_COPY='diabetes_regression_score_copy_env'
 AML_COMPUTE_CLUSTER_NAME_SCORING = 'score-cluster'
 AML_COMPUTE_CLUSTER_CPU_SKU_SCORING = 'STANDARD_DS2_V2'
-AML_CLUSTER_MAX_NODES_SCORING = '4'
+AML_CLUSTER_MAX_NODES_SCORING = '1'
 AML_CLUSTER_MIN_NODES_SCORING = '0'
 AML_CLUSTER_PRIORITY_SCORING = 'lowpriority'
 AML_REBUILD_ENVIRONMENT_SCORING = 'true'

diff --git a/.pipelines/diabetes_regression-cd.yml b/.pipelines/diabetes_regression-cd.yml
@@ -49,7 +49,7 @@ stages:
        azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
        scriptLocation: inlineScript
        workingDirectory: $(Build.SourcesDirectory)
-        inlineScript: 'az extension add --source https://azurecliext.blob.core.windows.net/release/azure_cli_ml-1.27.0-py3-none-any.whl --yes'
+        inlineScript: 'az extension add --source https://azurecliext.blob.core.windows.net/release/azure_cli_ml-1.41.0-py3-none-any.whl --yes'
    - task: AzureCLI@1
      displayName: "Deploy to ACI (CLI)"
      inputs:
@@ -95,7 +95,7 @@ stages:
        azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
        scriptLocation: inlineScript
        workingDirectory: $(Build.SourcesDirectory)
-        inlineScript: 'az extension add --source https://azurecliext.blob.core.windows.net/release/azure_cli_ml-1.27.0-py3-none-any.whl --yes'
+        inlineScript: 'az extension add --source https://azurecliext.blob.core.windows.net/release/azure_cli_ml-1.41.0-py3-none-any.whl --yes'
    - task: AzureCLI@1
      displayName: "Deploy to AKS (CLI)"
      inputs:

diff --git a/.pipelines/diabetes_regression-ci.yml b/.pipelines/diabetes_regression-ci.yml
@@ -16,7 +16,7 @@ trigger:
    - ml_service/pipelines/diabetes_regression_build_train_pipeline.py
    - ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py
    - ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r_on_dbricks.py
-
+    
 variables:
 - template: diabetes_regression-variables-template.yml
 - group: devopsforai-aml-vg

diff --git a/.pipelines/diabetes_regression-variables-template.yml b/.pipelines/diabetes_regression-variables-template.yml
@@ -45,7 +45,7 @@ variables:
  - name: AML_CLUSTER_MIN_NODES
    value: 0
  - name: AML_CLUSTER_MAX_NODES
-    value: 4
+    value: 1
  - name: AML_CLUSTER_PRIORITY
    value: lowpriority

@@ -90,7 +90,7 @@ variables:
  - name: AML_CLUSTER_MIN_NODES_SCORING
    value: 0
  - name: AML_CLUSTER_MAX_NODES_SCORING
-    value: 4
+    value: 1
  - name: AML_CLUSTER_PRIORITY_SCORING
    value: lowpriority
  # The path to the batch scoring script relative to SOURCES_DIR_TRAIN

diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@ products:
 description: "Code which demonstrates how to set up and operationalize an MLOps flow leveraging Azure Machine Learning and Azure DevOps."
 ---

-# MLOps with Azure ML
+# #MLOps with Azure ML Demo

 CI: [![Build Status](https://aidemos.visualstudio.com/MLOps/_apis/build/status/Model-Train-Register-CI?branchName=master)](https://aidemos.visualstudio.com/MLOps/_build/latest?definitionId=160&branchName=master)


diff --git a/diabetes_regression/parameters.json b/diabetes_regression/parameters.json
@@ -1,7 +1,7 @@
 {
    "training":
    {
-        "alpha": 0.4
+        "alpha": 0.5
    },
    "evaluation":
    {

diff --git a/diabetes_regression/training/train.py b/diabetes_regression/training/train.py
@@ -62,7 +62,7 @@ def main():
    print("Running train.py")

    # Define training parameters
-    ridge_args = {"alpha": 0.5}
+    ridge_args = {"alpha": 0.6}

    # Load the training data as dataframe
    data_dir = "data"