update samples from Release-145 as a part of SDK release

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -513,17 +513,7 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = (\n",
+        "conda_run_config.environment = automl_run.get_environment()"
-        "    automl_run.get_environment().python.conda_dependencies\n",
-        ")\n",
-        "\n",
-        "conda_run_config.environment.python.conda_dependencies.add_pip_package(\n",
-        "    \"dotnetcore2==2.1.23\"\n",
-        ")\n",
-        "\n",
-        "conda_run_config.environment.python.conda_dependencies.add_pip_package(\n",
-        "    \"protobuf==3.20.1\"\n",
-        ")"
       ]
     },
     {
@@ -652,28 +642,6 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml* packages using the training environment from the *automl_run*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "conda_dep = conda_run_config.environment.python.conda_dependencies\n",
-        "\n",
-        "with open(\"myenv.yml\", \"w\") as f:\n",
-        "    f.write(conda_dep.serialize_to_string())\n",
-        "with open(\"myenv.yml\", \"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -696,7 +664,7 @@
       "metadata": {},
       "source": [
         "### Deploy the service\n",
-        "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
+        "In the cell below, we deploy the service using the automl training environment and the scoring file from the previous steps. "
       ]
     },
     {
@@ -718,7 +686,7 @@
         "    description=\"Get local explanations for Machine test data\",\n",
         ")\n",
         "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "myenv = automl_run.get_environment()\n",
         "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n",
         "\n",
         "# Use configs and models generated above\n",

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu

@@ -11,6 +11,7 @@ RUN pip install azureml-core
 RUN pip install ray==0.8.7
 RUN pip install ray[rllib,tune,serve]==0.8.7
 RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu

@@ -69,6 +69,8 @@ RUN pip install --no-cache-dir \
 # This is required for ray 0.8.7
 RUN pip install -U aiohttp==3.7.4
 
+RUN pip install 'msrest<0.7.0'
+
 # This is needed for mpi to locate libpython
 ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
 

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb

@@ -97,7 +97,7 @@
         "import azureml.core\n",
         "\n",
         "# Check core SDK version number\n",
-        "print(\"Azure Machine Learning SDK Version: \", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version: \", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb

@@ -90,7 +90,7 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile

@@ -12,6 +12,7 @@ RUN pip install azureml-dataset-runtime
 RUN pip install ray==0.8.7
 RUN pip install ray[rllib,tune,serve]==0.8.7
 RUN pip install tensorflow==1.14.0
+RUN pip install 'msrest<0.7.0'
 
 RUN apt-get update
 RUN apt-get install -y jq

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb

@@ -91,7 +91,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile

@@ -34,3 +34,5 @@ RUN pip install protobuf==3.20.0
 
 RUN pip install --upgrade ray==0.8.3 \
     ray[rllib,dashboard,tune]==0.8.3
+
+RUN pip install 'msrest<0.7.0'

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile

@@ -34,3 +34,5 @@ RUN pip3 install --upgrade \
     ray==0.8.7 \
     ray[rllib]==0.8.7 \
     ray[tune]==0.8.7
+
+RUN pip install 'msrest<0.7.0'

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb

@@ -85,7 +85,7 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "print('Azure Machine Learning SDK Version: ', azureml.core.VERSION)"
+        "print('Azure Machine Learning SDK version: ', azureml.core.VERSION)"
       ]
     },
     {