update samples from Release-140 as a part of 1.44.0 SDK stable release

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

@@ -17,8 +17,9 @@ dependencies:
 - notebook
 - pywin32==227
 - PySocks==1.7.1
+- jsonschema==4.6.0
 - conda-forge::pyqt==5.12.3
-- jsonschema==4.9.1
+- jsonschema==4.7.2
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.

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

@@ -269,6 +269,7 @@
         "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",
@@ -280,9 +281,7 @@
         "mkdir build && \\\n",
         "python setup.py install --user && \\\n",
         "pip uninstall -y xgboost && \\\n",
-        "conda install py-xgboost==1.3.3 && \\\n",
-        "pip uninstall -y numpy && \\\n",
-        "conda install numpy==1.20.3 \\\n",
+        "conda install py-xgboost==1.3.3 \\\n",
         "\"\"\"\n",
         "\n",
         "env.python.user_managed_dependencies = True\n",

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

@@ -370,8 +370,8 @@
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "myenv = CondaDependencies.create(\n",
         "    python_version=python_version,\n",
-        "    conda_packages=['pip==20.2.4', numpy_dep],\n",
-        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep, numba_dep] + azureml_pip_packages)\n",
+        "    conda_packages=['pip==20.2.4'],\n",
+        "    pip_packages=['pyyaml', sklearn_dep, pandas_dep, numpy_dep, numba_dep] + azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())\n",

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

@@ -554,7 +554,7 @@
         "cd = CondaDependencies.create()\n",
         "cd.add_conda_package('numpy')\n",
         "cd.add_pip_package('chainer==5.1.0')\n",
-        "cd.add_pip_package(\"azureml-defaults==1.43.0\")\n",
+        "cd.add_pip_package(\"azureml-defaults\")\n",
         "cd.add_pip_package(\"azureml-opendatasets\")\n",
         "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n",
         "\n",

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

@@ -264,7 +264,7 @@
         "- python=3.6.2\n",
         "- pip=21.3.1\n",
         "- pip:\n",
-        "  - azureml-defaults==1.43.0\n",
+        "  - azureml-defaults\n",
         "  - torch==1.6.0\n",
         "  - torchvision==0.7.0\n",
         "  - future==0.17.1\n",
@@ -539,7 +539,7 @@
       "metadata": {},
       "source": [
         "## Deploy model as web service\n",
-        "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-deploy-and-where)."
+        "Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)."
       ]
     },
     {