diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py
index c0079343..753bba48 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/pipeline_helper.py
@@ -149,12 +149,7 @@ def get_backtest_pipeline(
         inputs=[forecasts.as_mount()],
         outputs=[data_results],
         source_directory=PROJECT_FOLDER,
-        arguments=[
-            "--forecasts",
-            forecasts,
-            "--output-dir",
-            data_results,
-        ],
+        arguments=["--forecasts", forecasts, "--output-dir", data_results],
         runconfig=run_config,
         compute_target=compute_target,
         allow_reuse=False,
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
index 16b22f66..c213146a 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/infer.py
@@ -23,11 +23,7 @@ except ImportError:
 
 
 def infer_forecasting_dataset_tcn(
-    X_test,
-    y_test,
-    model,
-    output_path,
-    output_dataset_name="results",
+    X_test, y_test, model, output_path, output_dataset_name="results"
 ):
 
     y_pred, df_all = model.forecast(X_test, y_test)
@@ -71,10 +67,7 @@ def get_model(model_path, model_file_name):
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--model_name",
-        type=str,
-        dest="model_name",
-        help="Model to be loaded",
+        "--model_name", type=str, dest="model_name", help="Model to be loaded"
     )
 
     parser.add_argument(
@@ -108,12 +101,7 @@ def get_args():
     return args
 
 
-def get_data(
-    run,
-    fitted_model,
-    target_column_name,
-    test_dataset_name,
-):
+def get_data(run, fitted_model, target_column_name, test_dataset_name):
 
     # get input dataset by name
     test_dataset = Dataset.get_by_name(run.experiment.workspace, test_dataset_name)
@@ -159,10 +147,7 @@ if __name__ == "__main__":
     fitted_model = get_model(model_path, model_file_name)
 
     X_test_df, y_test = get_data(
-        run,
-        fitted_model,
-        target_column_name,
-        test_dataset_name,
+        run, fitted_model, target_column_name, test_dataset_name
     )
 
     infer_forecasting_dataset_tcn(
diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
index 3b37c55e..9d9e2590 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
@@ -69,17 +69,19 @@
         "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
         "\n",
         "import urllib.request\n",
+        "import os\n",
         "\n",
         "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
         "\n",
         "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
+        "os.mkdir(\"emotion_ferplus\")\n",
         "\n",
         "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
         "# code from the command line instead of the notebook kernel\n",
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf emotion-ferplus-7.tar.gz"
+        "!tar xvzf emotion-ferplus-7.tar.gz -C emotion_ferplus"
       ]
     },
     {
@@ -130,7 +132,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n",
+        "model_dir = \"emotion_ferplus/model\" # replace this with the location of your model files\n",
         "\n",
         "# leave as is if it's in the same folder as this notebook"
       ]
@@ -496,13 +498,12 @@
         "\n",
         "# to use parsers to read in our model/data\n",
         "import json\n",
-        "import os\n",
         "\n",
         "test_inputs = []\n",
         "test_outputs = []\n",
         "\n",
-        "# read in 3 testing images from .pb files\n",
-        "test_data_size = 3\n",
+        "# read in 1 testing images from .pb files\n",
+        "test_data_size = 1\n",
         "\n",
         "for num in np.arange(test_data_size):\n",
         "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n",
@@ -533,7 +534,7 @@
       },
       "source": [
         "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
+        "We use `matplotlib` to plot 1 test images from the dataset."
       ]
     },
     {
@@ -547,7 +548,7 @@
       "outputs": [],
       "source": [
         "plt.figure(figsize = (20, 20))\n",
-        "for test_image in np.arange(3):\n",
+        "for test_image in np.arange(test_data_size):\n",
         "    test_inputs[test_image].reshape(1, 64, 64)\n",
         "    plt.subplot(1, 8, test_image+1)\n",
         "    plt.axhline('')\n",
diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
index 7d481129..67466c82 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
@@ -69,10 +69,12 @@
         "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
         "\n",
         "import urllib.request\n",
+        "import os\n",
         "\n",
         "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
         "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")\n",
+        "os.mkdir(\"mnist\")"
       ]
     },
     {
@@ -86,7 +88,7 @@
         "\n",
         "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
         "\n",
-        "!tar xvzf mnist-7.tar.gz"
+        "!tar xvzf mnist-7.tar.gz -C mnist"
       ]
     },
     {
@@ -137,7 +139,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "model_dir = \"mnist\" # replace this with the location of your model files\n",
+        "model_dir = \"mnist/model\" # replace this with the location of your model files\n",
         "\n",
         "# leave as is if it's in the same folder as this notebook"
       ]
@@ -447,13 +449,12 @@
         "\n",
         "# to use parsers to read in our model/data\n",
         "import json\n",
-        "import os\n",
         "\n",
         "test_inputs = []\n",
         "test_outputs = []\n",
         "\n",
-        "# read in 3 testing images from .pb files\n",
-        "test_data_size = 3\n",
+        "# read in 1 testing images from .pb files\n",
+        "test_data_size = 1\n",
         "\n",
         "for i in np.arange(test_data_size):\n",
         "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
@@ -486,7 +487,7 @@
       },
       "source": [
         "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
+        "We use `matplotlib` to plot 1 test images from the dataset."
       ]
     },
     {
@@ -500,7 +501,7 @@
       "outputs": [],
       "source": [
         "plt.figure(figsize = (16, 6))\n",
-        "for test_image in np.arange(3):\n",
+        "for test_image in np.arange(test_data_size):\n",
         "    plt.subplot(1, 15, test_image+1)\n",
         "    plt.axhline('')\n",
         "    plt.axvline('')\n",
diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
index 2a3c79fb..816df2bf 100644
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
@@ -8,8 +8,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     rm -rf /var/lib/apt/lists/* && \
     rm -rf /usr/share/man/*
 
-RUN conda install -y conda=4.12.0 python=3.7 && conda clean -ay
-RUN pip install ray-on-aml==0.1.6 & \
+RUN conda install -y conda=4.13.0 python=3.7 && conda clean -ay
+RUN pip install ray-on-aml==0.2.1 & \
     pip install --no-cache-dir \
     azureml-defaults \
     azureml-dataset-runtime[fuse,pandas] \