Merge pull request #1748 from Azure/release_update/Release-138

update samples from Release-138 as a part of SDK release
2025-12-19 17:17:04 -05:00 · 2022-04-29 10:40:01 -07:00
parent 7ce79a43f1 ffa3a43979
commit 5928ba83bb
8 changed files with 22 additions and 29 deletions
--- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
+++ b/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/datasets/index.html#diabetes-dataset). "
+        "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). "
      ]
    },
    {
@@ -279,7 +279,9 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
@@ -478,7 +480,9 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
--- a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
+++ b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
@@ -105,7 +105,9 @@
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "environment=Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'numpy',\n",
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
@@ -358,6 +358,7 @@
        "# 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",
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb
@@ -242,11 +242,7 @@
        "    register(workspace=ws)\n",
        "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n",
        "\n",
-        "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}')"
+        "ray_cpu_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
@@ -279,11 +275,7 @@
        "    register(workspace=ws)\n",
        "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n",
        "\n",
-        "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}')"
+        "ray_gpu_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
@@ -255,11 +255,7 @@
        "    register(workspace=ws)\n",
        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
        "\n",
-        "# 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}')"
+        "ray_env_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
@@ -223,11 +223,7 @@
        "    register(workspace=ws)\n",
        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
        "\n",
-        "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}')"
+        "ray_env_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {
--- 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
@@ -8,10 +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=4.7.12 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 & \
+RUN conda install -y conda=4.12.0 python=3.7 && conda clean -ay
+RUN pip install ray-on-aml==0.1.6 & \
    pip install --no-cache-dir \
    azureml-defaults \
    azureml-dataset-runtime[fuse,pandas] \
@@ -32,3 +30,5 @@ RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
    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
--- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb
@@ -246,7 +246,9 @@
        "ray_environment = Environment. \\\n",
        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
        "    register(workspace=ws)\n",
-        "ray_gpu_build_details = ray_environment.build(workspace=ws)"
+        "ray_cpu_build_details = ray_environment.build(workspace=ws)\n",
+        "\n",
+        "ray_cpu_build_details.wait_for_completion(show_output=True)"
      ]
    },
    {