update samples from Release-131 as a part of 1.41.0 SDK stable release

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

@@ -15,9 +15,8 @@ dependencies:
 - cudatoolkit=10.1.243
 - scipy==1.5.2
 - notebook
-- pywin32==227
+- pywin32==225
 - PySocks==1.7.1
-- Pygments==2.11.2
 - conda-forge::pyqt==5.12.3
 
 - pip:

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb

@@ -82,7 +82,7 @@
       "source": [
         "## Create trained model\n",
         "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
       ]
     },
     {
@@ -279,9 +279,7 @@
         "\n",
         "\n",
         "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
@@ -480,9 +478,7 @@
         "\n",
         "\n",
         "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -105,9 +105,7 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "environment=Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'numpy',\n",

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

@@ -358,7 +358,6 @@
         "# 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'],\n",
         "    pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",

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

@@ -242,7 +242,11 @@
         "    register(workspace=ws)\n",
         "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n",
         "\n",
-        "ray_cpu_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "while ray_cpu_build_details.status not in ['Succeeded', 'Failed']:\n",
+        "    print(f'Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}')\n",
+        "    time.sleep(30)\n",
+        "print(f'status={ray_cpu_build_details.status}')"
       ]
     },
     {
@@ -275,7 +279,11 @@
         "    register(workspace=ws)\n",
         "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n",
         "\n",
-        "ray_gpu_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "while ray_gpu_build_details.status not in ['Succeeded', 'Failed']:\n",
+        "    print(f'Awaiting completion of ray GPU environment build. Current status is: {ray_gpu_build_details.status}')\n",
+        "    time.sleep(30)\n",
+        "print(f'status={ray_gpu_build_details.status}')"
       ]
     },
     {

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

@@ -255,7 +255,11 @@
         "    register(workspace=ws)\n",
         "ray_env_build_details = ray_environment.build(workspace=ws)\n",
         "\n",
-        "ray_env_build_details.wait_for_completion(show_output=True)"
+        "# import time\n",
+        "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n",
+        "    print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n",
+        "    time.sleep(30)\n",
+        "print(f'status={ray_env_build_details.status}')"
       ]
     },
     {

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

@@ -223,7 +223,11 @@
         "    register(workspace=ws)\n",
         "ray_env_build_details = ray_environment.build(workspace=ws)\n",
         "\n",
-        "ray_env_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n",
+        "    print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n",
+        "    time.sleep(30)\n",
+        "print(f'status={ray_env_build_details.status}')"
       ]
     },
     {

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

@@ -8,8 +8,10 @@ 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=4.7.12 python=3.7 && conda clean -ay && \
+    pip install ray-on-aml==0.1.6 & \
+    pip install --upgrade ray==0.8.3 \
+    ray[rllib,dashboard,tune]==0.8.3 & \
     pip install --no-cache-dir \
     azureml-defaults \
     azureml-dataset-runtime[fuse,pandas] \
@@ -30,5 +32,3 @@ RUN pip install ray-on-aml==0.1.6 & \
     conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
     conda install -c anaconda opencv
 
-RUN pip install --upgrade ray==0.8.3 \
-    ray[rllib,dashboard,tune]==0.8.3

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

@@ -246,9 +246,7 @@
         "ray_environment = Environment. \\\n",
         "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
         "    register(workspace=ws)\n",
-        "ray_cpu_build_details = ray_environment.build(workspace=ws)\n",
-        "\n",
-        "ray_cpu_build_details.wait_for_completion(show_output=True)"
+        "ray_gpu_build_details = ray_environment.build(workspace=ws)"
       ]
     },
     {