update samples from Release-149 as a part of SDK release

update samples from Release-148 as a part of  SDK release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -6,6 +6,7 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

contrib/fairness/upload-fairness-dashboard.yml

@@ -6,6 +6,7 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -13,19 +13,19 @@ dependencies:
 - pytorch::pytorch=1.4.0
 - conda-forge::fbprophet==0.7.1
 - cudatoolkit=10.1.243
-- scipy==1.5.2
+- scipy==1.5.3
 - notebook
 - pywin32==227
 - PySocks==1.7.1
-- jsonschema==4.5.1
+- jsonschema==4.6.0
 - conda-forge::pyqt==5.12.3
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.42.0
+  - azureml-widgets~=1.43.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_win32_requirements.txt [--no-deps]
   - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -14,7 +14,7 @@ dependencies:
 - numpy==1.19.5
 - cython==0.29.14
 - urllib3==1.26.7
-- scipy>=1.4.1,<=1.5.2
+- scipy>=1.4.1,<=1.5.3
 - scikit-learn==0.22.1
 - py-xgboost<=1.3.3
 - holidays==0.10.3
@@ -24,10 +24,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.42.0
+  - azureml-widgets~=1.43.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_linux_requirements.txt [--no-deps]
   - arch==4.14

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -15,7 +15,7 @@ dependencies:
 - numpy==1.19.5
 - cython==0.29.14
 - urllib3==1.26.7
-- scipy>=1.4.1,<=1.5.2
+- scipy>=1.4.1,<=1.5.3
 - scikit-learn==0.22.1
 - py-xgboost<=1.3.3
 - holidays==0.10.3
@@ -25,10 +25,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.42.0
+  - azureml-widgets~=1.43.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.43.0/validated_darwin_requirements.txt [--no-deps]
   - arch==4.14

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

@@ -91,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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,

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(

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",

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",

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/gpu_tree_explainer.py

@@ -2,6 +2,8 @@
 # Licensed under the MIT license.
 
 from azureml.core.run import Run
+from azureml.interpret import ExplanationClient
+from interpret_community.adapter import ExplanationAdapter
 import joblib
 import os
 import shap
@@ -11,9 +13,11 @@ OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
 run = Run.get_context()
+client = ExplanationClient.from_run(run)
 
 # get a dataset on income prediction
 X, y = shap.datasets.adult()
+features = X.columns.values
 
 # train an XGBoost model (but any other tree model type should work)
 model = xgboost.XGBClassifier()
@@ -26,6 +30,12 @@ shap_values = explainer(X_shap)
 print("computed shap values:")
 print(shap_values)
 
+# Use the explanation adapter to convert the importances into an interpret-community
+# style explanation which can be uploaded to AzureML or visualized in the
+# ExplanationDashboard widget
+adapter = ExplanationAdapter(features, classification=True)
+global_explanation = adapter.create_global(shap_values.values, X_shap, expected_values=shap_values.base_values)
+
 # write X_shap out as a pickle file for later visualization
 x_shap_pkl = 'x_shap.pkl'
 with open(x_shap_pkl, 'wb') as file:
@@ -42,3 +52,8 @@ with open(model_file_name, 'wb') as file:
 run.upload_file('xgboost_model.pkl', os.path.join('./outputs/', model_file_name))
 original_model = run.register_model(model_name='xgboost_with_gpu_tree_explainer',
                                     model_path='xgboost_model.pkl')
+
+# Uploading model explanation data for storage or visualization in webUX
+# The explanation can then be downloaded on any compute
+comment = 'Global explanation on classification model trained on adult census income dataset'
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb

@@ -106,7 +106,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -225,36 +225,73 @@
         "\n",
         "from azureml.core import Environment\n",
         "\n",
-        "environment_name = \"shap-gpu-tree\"\n",
-        "\n",
+        "environment_name = \"shapgpu\"\n",
         "env = Environment(environment_name)\n",
         "\n",
         "env.docker.enabled = True\n",
         "env.docker.base_image = None\n",
-        "env.docker.base_dockerfile = \"\"\"\n",
-        "FROM rapidsai/rapidsai:cuda10.0-devel-ubuntu18.04\n",
+        "\n",
+        "\n",
+        "# Note: this is to pin the pandas and xgboost versions to be same as notebook.\n",
+        "# In production scenario user would choose their dependencies\n",
+        "import pkg_resources\n",
+        "available_packages = pkg_resources.working_set\n",
+        "xgboost_ver = None\n",
+        "pandas_ver = None\n",
+        "for dist in list(available_packages):\n",
+        "    if dist.key == 'xgboost':\n",
+        "        xgboost_ver = dist.version\n",
+        "    elif dist.key == 'pandas':\n",
+        "        pandas_ver = dist.version\n",
+        "xgboost_dep = 'xgboost'\n",
+        "pandas_dep = 'pandas'\n",
+        "if pandas_ver:\n",
+        "    pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
+        "if xgboost_dep:\n",
+        "    xgboost_dep = 'xgboost=={}'.format(xgboost_ver)\n",
+        "\n",
+        "# Note: we build shap at commit 690245 for Tesla K80 GPUs\n",
+        "env.docker.base_dockerfile = f\"\"\"\n",
+        "FROM nvidia/cuda:10.2-devel-ubuntu18.04\n",
+        "ENV PATH=\"/root/miniconda3/bin:${{PATH}}\"\n",
+        "ARG PATH=\"/root/miniconda3/bin:${{PATH}}\"\n",
         "RUN apt-get update && \\\n",
         "apt-get install -y fuse && \\\n",
         "apt-get install -y build-essential && \\\n",
         "apt-get install -y python3-dev && \\\n",
-        "source activate rapids && \\\n",
+        "apt-get install -y wget && \\\n",
+        "apt-get install -y git && \\\n",
+        "rm -rf /var/lib/apt/lists/* && \\\n",
+        "wget \\\n",
+        "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \\\n",
+        "mkdir /root/.conda && \\\n",
+        "bash Miniconda3-latest-Linux-x86_64.sh -b && \\\n",
+        "rm -f Miniconda3-latest-Linux-x86_64.sh && \\\n",
+        "conda init bash && \\\n",
+        ". ~/.bashrc && \\\n",
+        "conda create -n shapgpu python=3.7 && \\\n",
+        "conda activate shapgpu && \\\n",
         "apt-get install -y g++ && \\\n",
         "printenv && \\\n",
         "echo \"which nvcc: \" && \\\n",
         "which nvcc && \\\n",
+        "pip install numpy==1.20.3 && \\\n",
         "pip install azureml-defaults && \\\n",
         "pip install azureml-telemetry && \\\n",
+        "pip install azureml-interpret && \\\n",
+        "pip install {pandas_dep} && \\\n",
         "cd /usr/local/src && \\\n",
-        "git clone https://github.com/slundberg/shap && \\\n",
+        "git clone https://github.com/slundberg/shap.git --single-branch && \\\n",
         "cd shap && \\\n",
+        "git reset --hard 690245c6ab043edf40cfce3d8438a62e29ab599f && \\\n",
         "mkdir build && \\\n",
         "python setup.py install --user && \\\n",
         "pip uninstall -y xgboost && \\\n",
-        "rm /conda/envs/rapids/lib/libxgboost.so && \\\n",
-        "pip install xgboost==1.4.2\n",
+        "pip install {xgboost_dep} \\\n",
         "\"\"\"\n",
         "\n",
         "env.python.user_managed_dependencies = True\n",
+        "env.python.interpreter_path = '/root/miniconda3/envs/shapgpu/bin/python'\n",
         "\n",
         "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
@@ -266,6 +303,176 @@
         "run = experiment.submit(config=src)\n",
         "run"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Download \n",
+        "1. Download model explanation data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.interpret import ExplanationClient\n",
+        "\n",
+        "# Get model explanation data\n",
+        "client = ExplanationClient.from_run(run)\n",
+        "global_explanation = client.download_model_explanation()\n",
+        "local_importance_values = global_explanation.local_importance_values\n",
+        "expected_values = global_explanation.expected_values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the top k (e.g., 4) most important features with their importance values\n",
+        "global_explanation_topk = client.download_model_explanation(top_k=4)\n",
+        "global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
+        "global_importance_names = global_explanation_topk.get_ranked_global_names()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print('global importance values: {}'.format(global_importance_values))\n",
+        "print('global importance names: {}'.format(global_importance_names))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "2. Download model file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Retrieve model for visualization and deployment\n",
+        "from azureml.core.model import Model\n",
+        "import joblib\n",
+        "original_model = Model(ws, 'xgboost_with_gpu_tree_explainer')\n",
+        "model_path = original_model.download(exist_ok=True)\n",
+        "original_model = joblib.load(model_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "3. Download test dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Retrieve x_test for visualization\n",
+        "x_test_path = './x_shap_adult_census.pkl'\n",
+        "run.download_file('x_shap_adult_census.pkl', output_file_path=x_test_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "x_test = joblib.load('x_shap_adult_census.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize\n",
+        "Load the visualization dashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from raiwidgets import ExplanationDashboard"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from interpret_community.common.model_wrapper import wrap_model\n",
+        "from interpret_community.dataset.dataset_wrapper import DatasetWrapper\n",
+        "# note we need to wrap the XGBoost model to output predictions and probabilities in the scikit-learn format\n",
+        "class WrappedXGBoostModel(object):\n",
+        "    \"\"\"A class for wrapping an XGBoost model to output integer predicted classes.\"\"\"\n",
+        "\n",
+        "    def __init__(self, model):\n",
+        "        self.model = model\n",
+        "\n",
+        "    def predict(self, dataset):\n",
+        "        return self.model.predict(dataset).astype(int)\n",
+        "\n",
+        "    def predict_proba(self, dataset):\n",
+        "        return self.model.predict_proba(dataset)\n",
+        "\n",
+        "wrapped_model = WrappedXGBoostModel(wrap_model(original_model, DatasetWrapper(x_test), model_task='classification'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ExplanationDashboard(global_explanation, wrapped_model, dataset=x_test)"
+      ]
     }
   ],
   "metadata": {

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml

@@ -1,5 +1,18 @@
 name: train-explain-model-gpu-tree-explainer
 dependencies:
+- py-xgboost==1.3.3
 - pip:
   - azureml-sdk
   - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - ipywidgets
+  - raiwidgets~=0.19.0
+  - itsdangerous==2.0.1
+  - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

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

@@ -11,6 +11,8 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

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

@@ -10,7 +10,9 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/explain-model/azure-integration/scoring-time/score_local_explain.py

@@ -18,7 +18,9 @@ def init():
     original_model_path = Model.get_model_path('local_deploy_model')
     scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
 
+    # Load the original model into the environment
     original_model = joblib.load(original_model_path)
+    # Load the scoring explainer into the environment
     scoring_explainer = joblib.load(scoring_explainer_path)
 
 
@@ -29,5 +31,15 @@ def run(raw_data):
     predictions = original_model.predict(data)
     # Retrieve model explanations
     local_importance_values = scoring_explainer.explain(data)
+    # Retrieve the feature names, which we may want to return to the user.
+    # Note: you can also get the raw_features and engineered_features
+    # by calling scoring_explainer.raw_features and
+    # scoring_explainer.engineered_features but you may need to pass
+    # the raw or engineered feature names in the ScoringExplainer
+    # constructor, depending on if you are using feature maps or
+    # transformations on the original explainer.
+    features = scoring_explainer.features
     # You can return any data type as long as it is JSON-serializable
-    return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}
+    return {'predictions': predictions.tolist(),
+            'local_importance_values': local_importance_values,
+            'features': features}

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

@@ -10,7 +10,9 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

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

@@ -12,6 +12,8 @@ dependencies:
   - azureml-dataset-runtime
   - azureml-core
   - ipywidgets
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - scipy>=1.5.3
+  - protobuf==3.20.0

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.yml

@@ -3,3 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-widgets
+  - protobuf==3.20.0

how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb

@@ -437,7 +437,8 @@
         "  - azureml-defaults\n",
         "  - tensorflow-gpu==2.0.0\n",
         "  - keras<=2.3.1\n",
-        "  - matplotlib"
+        "  - matplotlib\n",
+        "  - protobuf==3.20.1"
       ]
     },
     {
@@ -989,6 +990,7 @@
         "cd.add_conda_package('h5py<=2.10.0')\n",
         "cd.add_conda_package('keras<=2.3.1')\n",
         "cd.add_pip_package(\"azureml-defaults\")\n",
+        "cd.add_pip_package(\"protobuf==3.20.1\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"

how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -943,6 +943,7 @@
         "cd.add_conda_package('numpy')\n",
         "cd.add_pip_package('tensorflow==2.2.0')\n",
         "cd.add_pip_package(\"azureml-defaults\")\n",
+        "cd.add_pip_package(\"protobuf==3.20.1\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",
         "print(cd.serialize_to_string())"

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] \

how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml

@@ -8,8 +8,9 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.18.1
+  - raiwidgets~=0.19.0
   - liac-arff
   - packaging>=20.9
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
+  - protobuf==3.20.0

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -43,6 +43,7 @@
         "        1. Logging numeric metrics\n",
         "        1. Logging vectors\n",
         "        1. Logging tables\n",
+        "        1. Logging when additional Metric Names are required\n",
         "        1. Uploading files\n",
         "1. [Analyzing results](#Analyzing-results)\n",
         "    1. Tagging a run\n",
@@ -100,7 +101,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.42.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.43.0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -367,7 +368,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Logging for when more Metric Names are required\n",
+        "### Logging when additional Metric Names are required\n",
         "\n",
         "Limits on logging are internally enforced to ensure a smooth experience, however these can sometimes be limiting, particularly in terms of the limit on metric names.\n",
         "\n",

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.43.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },