update samples from Release-99 as a part of SDK release

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -54,17 +54,17 @@ try:
     end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
     print("Dataset {0} last updated on {1}".format(args.ds_name,
                                                    end_time_last_slice))
-except Exception:
+except Exception as e:
     print(traceback.format_exc())
     print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
     register_dataset = True
-    end_time_last_slice = datetime.today() - relativedelta(weeks=4)
+    end_time_last_slice = datetime.today() - relativedelta(weeks=2)
 
 end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
 
 if train_df.size > 0:
-    print("Received {0} rows of new data after {1}.".format(
+    print("Received {0} rows of new data after {0}.".format(
         train_df.shape[0], end_time_last_slice))
     folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
                                                                         end_time.month, end_time.day,

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb

@@ -453,7 +453,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_model, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, original_model, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -542,7 +542,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
+        "ExplanationDashboard(downloaded_global_explanation, model, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -305,7 +305,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, clf, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, clf, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb

@@ -409,7 +409,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_svm_model, datasetX=x_test)"
+        "ExplanationDashboard(global_explanation, original_svm_model, dataset=x_test)"
       ]
     },
     {

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb

@@ -184,12 +184,10 @@
         "\n",
         "env = Environment(name=\"mlflow-env\")\n",
         "\n",
-        "env.docker.enabled = True\n",
-        "\n",
         "# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n",
         "cd = CondaDependencies.create(\n",
         "    conda_packages=[\"scikit-learn\", \"matplotlib\"],\n",
-        "    pip_packages=[\"azureml-mlflow\", \"numpy\"]\n",
+        "    pip_packages=[\"azureml-mlflow\", \"pandas\", \"numpy\"]\n",
         "    )\n",
         "\n",
         "env.python.conda_dependencies = cd"

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb

@@ -274,7 +274,7 @@
         "- pip:\n",
         "  - azureml-defaults\n",
         "  - keras\n",
-        "  - tensorflow\n",
+        "  - tensorflow<=2.4.*\n",
         "  - numpy\n",
         "  - scikit-learn\n",
         "  - pandas\n",