diff --git 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 index 10380475..e2062c19 100644 --- 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", diff --git 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 index 25002c44..49b39085 100644 --- 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", diff --git 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 index 121e3fe6..aa492308 100644 --- 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", diff --git 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 index a6ee8eeb..b8d8af9e 100644 --- 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)" ] }, { diff --git 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 index ab567f56..8eea14f6 100644 --- 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)" ] }, { diff --git 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 index d274986c..cf7a11b8 100644 --- 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)" ] }, { diff --git 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 index b582de4d..b63a3aad 100644 --- 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 \ No newline at end of file diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb index 1be9aecf..5e99a9b5 100644 --- 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)" ] }, {