Merge main (#2627)

* chore: Update service principal name in init_environment.sh (#2621) * fix MLClient params (#2623) * Update with changes in main --------- Co-authored-by: kdestin <[email protected]> Co-authored-by: aggarwal-k <[email protected]>
Azure · Sep 7, 2023 · 351edd1 · 351edd1
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diff --git a/...estore_sample/featurestore/featuresets/transactions/featureset_asset_offline_enabled.yaml b/...estore_sample/featurestore/featuresets/transactions/featureset_asset_offline_enabled.yaml
@@ -9,6 +9,7 @@ specification:
   path: ./spec
 materialization_settings:
   offline_enabled: true
+  online_enabled: false
   resource:
     instance_type: Standard_E8S_V3
   spark_configuration:

diff --git a/...featurestore/featuresets/transactions/featureset_asset_offline_enabled_with_schedule.yaml b/...featurestore/featuresets/transactions/featureset_asset_offline_enabled_with_schedule.yaml
@@ -9,6 +9,7 @@ specification:
   path: ./spec
 materialization_settings:
   offline_enabled: true
+  online_enabled: false
   schedule:
     type: recurrence
     interval: 3

diff --git a/...ebooks/sdk_and_cli/1. Develop a feature set and register with managed feature store.ipynb b/...ebooks/sdk_and_cli/1. Develop a feature set and register with managed feature store.ipynb
@@ -128,11 +128,12 @@
     "* Option 1: Create a new notebook, and execute the instructions in this document step by step. \n",
     "* Option 2: Open the existing notebook named `1. Develop a feature set and register with managed feature store.ipynb`, and run it step by step. The notebooks are available in `featurestore_sample/notebooks` directory. You can select from `sdk_only` or `sdk_and_cli`. You may keep this document open and refer to it for additional explanation and documentation links.\n",
     "\n",
-    "1. Select **AzureML Spark compute** in the top nav \"Compute\" dropdown. This operation might take one to two minutes. Wait for a status bar in the top to display **configure session**.\n",
+    "1. Select **Serverless Spark compute** in the top nav \"Compute\" dropdown. This operation might take one to two minutes. Wait for a status bar in the top to display **configure session**.\n",
     "\n",
     "1. Select \"configure session\" from the top nav (this could take one to two minutes to display):\n",
     "\n",
-    "      1. Select **configure session** in the bottom nav\n",
+    "      1. Select **configure session** in the top status bar\n",
+    "      1. Select **Python packages**\n",
     "      1. Select **Upload conda file**\n",
     "      1. Select file `azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml` located on your local device\n",
     "      1. (Optional) Increase the session time-out (idle time) to reduce the serverless spark cluster startup time."

diff --git a/...re_sample/notebooks/sdk_and_cli/2. Enable materialization and backfill feature data.ipynb b/...re_sample/notebooks/sdk_and_cli/2. Enable materialization and backfill feature data.ipynb
@@ -91,13 +91,11 @@
     }
    },
    "source": [
-    "#### (updated for sdk+cli track) Configure Azure ML spark notebook\n",
+    "#### Configure Azure ML spark notebook\n",
     "\n",
     "1. Running the tutorial: You can either create a new notebook, and execute the instructions in this document step by step or open the existing notebook named `2. Enable materialization and backfill feature data.ipynb`, and run it. The notebooks are available in `featurestore_sample/notebooks` directory. You can select from `sdk_only` or `sdk_and_cli`. You may keep this document open and refer to it for additional explanation and documentation links.\n",
-    "1. In the \"Compute\" dropdown in the top nav, select \"AzureML Spark Compute\". \n",
-    "1. Click on \"configure session\" in bottom nav -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently\n",
-    "\n",
-    "\n"
+    "1. In the \"Compute\" dropdown in the top nav, select \"Serverless Spark Compute\". \n",
+    "1. Click on \"configure session\" in top status bar -> click on \"Python packages\" -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently\n"
    ]
   },
   {
@@ -928,7 +926,7 @@
    },
    "outputs": [],
    "source": [
-    "# The below code creeates a feature stor\n",
+    "# The below code creates a feature store\n",
     "import yaml\n",
     "\n",
     "config = {\n",

diff --git a/...urestore_sample/notebooks/sdk_and_cli/3. Experiment and train models using features.ipynb b/...urestore_sample/notebooks/sdk_and_cli/3. Experiment and train models using features.ipynb
@@ -88,8 +88,8 @@
     "#### (updated)  Configure Azure ML spark notebook\n",
     "\n",
     "1. Running the tutorial: You can either create a new notebook, and execute the instructions in this document step by step or open the existing notebook named `3. Experiment and train models using features`, and run it. The notebooks are available in `featurestore_sample/notebooks` directory. You can select from `sdk_only` or `sdk_and_cli`. You may keep this document open and refer to it for additional explanation and documentation links.\n",
-    "1. In the \"Compute\" dropdown in the top nav, select \"AzureML Spark Compute\". \n",
-    "1. Click on \"configure session\" in bottom nav -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently\n"
+    "1. In the \"Compute\" dropdown in the top nav, select \"Serverless Spark Compute\". \n",
+    "1. Click on \"configure session\" in top status bar -> click on \"Python packages\" -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently\n"
    ]
   },
   {

diff --git a/...e/notebooks/sdk_and_cli/4. Enable recurrent materialization and run batch inference.ipynb b/...e/notebooks/sdk_and_cli/4. Enable recurrent materialization and run batch inference.ipynb
@@ -91,8 +91,8 @@
     "#### (updated)  Configure Azure ML spark notebook\n",
     "\n",
     "1. Running the tutorial: You can either create a new notebook, and execute the instructions in this document step by step or open the existing notebook named `4. Enable recurrent materialization and run batch inference`, and run it. The notebooks are available in `featurestore_sample/notebooks` directory. You can select from `sdk_only` or `sdk_and_cli`. You may keep this document open and refer to it for additional explanation and documentation links.\n",
-    "1. In the \"Compute\" dropdown in the top nav, select \"AzureML Spark Compute\". \n",
-    "1. Click on \"configure session\" in bottom nav -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently"
+    "1. In the \"Compute\" dropdown in the top nav, select \"Serverless Spark Compute\". \n",
+    "1. Click on \"configure session\" in top status bar -> click on \"Python packages\" -> click on \"upload conda file\" -> select the file azureml-examples/sdk/python/featurestore-sample/project/env/conda.yml from your local machine; Also increase the session time out (idle time) if you want to avoid running the prerequisites frequently\n"
    ]
   },
   {