Update resource-curated-environments.md

saachigopal · web-flow · commit be2419837b24 · 2022-01-21T11:26:21.000-05:00
diff --git a/articles/machine-learning/resource-curated-environments.md b/articles/machine-learning/resource-curated-environments.md
@@ -19,185 +19,62 @@ This article lists the curated environments with latest framework versions in Az
 > [!NOTE]
 > Use the [Python SDK](how-to-use-environments.md), [CLI](/cli/azure/ml/environment#az_ml_environment_list), or Azure Machine Learning [studio](how-to-manage-environments-in-studio.md) to get the full list of environments and their dependencies. For more information, see the [environments article](how-to-use-environments.md#use-a-curated-environment). 
 
+## Why should I use curated environments?
+
+* Reduces training and deployment latency.
+* Improves training and deployment success rate.
+* Avoid unnecessary image builds.
+* Only have required dependencies and access right in the image/container. 
+
+>[!IMPORTANT] 
+> To see view more information about curated environment packages and versions, visit the Environments tab in the Azure Machine Learning studio. 
+
 ## Training curated environments
 
 ### PyTorch
 
-**Name**: AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu
+**Name**: AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu  
 **Description**: An environment for deep learning with PyTorch containing the AzureML Python SDK and other python packages.  
-
-The following Dockerfile can be customized for your personal workflows.
-
-```dockerfile
-FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu18.04:20211221.v1
-
-ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/pytorch-1.10
-
-# Create conda environment
-RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
-    python=3.8 \
-    pip=20.2.4 \
-    pytorch=1.10.0 \
-    torchvision=0.11.1 \
-    torchaudio=0.10.0 \
-    cudatoolkit=11.1.1 \
-    nvidia-apex=0.1.0 \
-    gxx_linux-64 \
-    -c anaconda -c pytorch -c conda-forge
-
-# Prepend path to AzureML conda environment
-ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
-
-# Install pip dependencies
-RUN pip install 'matplotlib>=3.3,<3.4' \
-                'psutil>=5.8,<5.9' \
-                'tqdm>=4.59,<4.63' \
-                'pandas>=1.3,<1.4' \
-                'scipy>=1.5,<1.8' \
-                'numpy>=1.10,<1.22' \
-                'ipykernel~=6.0' \
-                'azureml-core==1.37.0.post1' \
-                'azureml-defaults==1.37.0' \
-                'azureml-mlflow==1.37.0' \
-                'azureml-telemetry==1.37.0' \
-                'tensorboard==2.6.0' \
-                'tensorflow-gpu==2.6.0' \
-                'onnxruntime-gpu>=1.7,<1.10' \
-                'horovod==0.23' \
-                'future==0.18.2' \
-                'torch-tb-profiler==0.3.1'
-
-
-# This is needed for mpi to locate libpython
-ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
-```
+* GPU: Cuda11
+* OS: Ubuntu18.04
+* PyTorch: 1.10
 
 Other available PyTorch environments:
 * AzureML-pytorch-1.9-ubuntu18.04-py37-cuda11-gpu  
 * AzureML-pytorch-1.8-ubuntu18.04-py37-cuda11-gpu
 * AzureML-pytorch-1.7-ubuntu18.04-py37-cuda11-gpu
 
+
 ### LightGBM
 
 **Name**: AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu  
 **Description**: An environment for machine learning with Scikit-learn, LightGBM, XGBoost, Dask containing the AzureML Python SDK and other packages.  
+* OS: Ubuntu18.04
+* Dask: 2021.6
+* LightGBM: 3.2
+* Scikit-learn: 0.24
+* XGBoost: 1.4
 
-The following Dockerfile can be customized for your personal workflows.
-
-```dockerfile
-FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20211221.v1
-
-ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/lightgbm
-
-# Create conda environment
-RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
-    python=3.7 pip=20.2.4
-
-# Prepend path to AzureML conda environment
-ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
-
-# Install pip dependencies
-RUN HOROVOD_WITH_TENSORFLOW=1 \
-    pip install 'matplotlib>=3.3,<3.4' \
-                'psutil>=5.8,<5.9' \
-                'tqdm>=4.59,<4.60' \
-                'pandas>=1.1,<1.2' \
-                'numpy>=1.10,<1.20' \
-                'scipy~=1.5.0' \
-                'scikit-learn~=0.24.1' \
-                'xgboost~=1.4.0' \
-                'lightgbm~=3.2.0' \
-                'dask~=2021.6.0' \
-                'distributed~=2021.6.0' \
-                'dask-ml~=1.9.0' \
-                'adlfs~=0.7.0' \
-                'ipykernel~=6.0' \
-                'azureml-core==1.37.0.post1' \
-                'azureml-defaults==1.37.0' \
-                'azureml-mlflow==1.37.0' \
-                'azureml-telemetry==1.37.0'
-
-# This is needed for mpi to locate libpython
-ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
-```
 
 ### Sklearn
-**Name**: AzureML-sklearn-0.24-ubuntu18.04-py37-cuda11-gpu  
+**Name**: AzureML-sklearn-1.0-ubuntu20.04-py38-cpu
 **Description**: An environment for tasks such as regression, clustering, and classification with Scikit-learn. Contains the AzureML Python SDK and other python packages.  
+* OS: Ubuntu20.04
+* Scikit-learn: 1.0
 
-The following Dockerfile can be customized for your personal workflows.
-
-```dockerfile
-FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20211221.v1
-
-ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/sklearn-0.24.1
-
-# Create conda environment
-RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
-    python=3.7 pip=20.2.4
-
-# Prepend path to AzureML conda environment
-ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
-
-# Install pip dependencies
-RUN pip install 'matplotlib>=3.3,<3.4' \
-                'psutil>=5.8,<5.9' \
-                'tqdm>=4.59,<4.60' \
-                'pandas>=1.1,<1.2' \
-                'scipy>=1.5,<1.6' \
-                'numpy>=1.10,<1.20' \
-                'ipykernel~=6.0' \
-                'azureml-core==1.37.0.post1' \
-                'azureml-defaults==1.37.0' \
-                'azureml-mlflow==1.37.0' \
-                'azureml-telemetry==1.37.0' \
-                'scikit-learn==0.24.1'
+Other available Sklearn environments:
+* AzureML-sklearn-0.24-ubuntu18.04-py37-cpu
 
-# This is needed for mpi to locate libpython
-ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
-```
 
 ### TensorFlow
 
 **Name**: AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu  
 **Description**: An environment for deep learning with TensorFlow containing the AzureML Python SDK and other python packages.  
+* GPU: Cuda11
+* Horovod: 2.4.1
+* OS: Ubuntu18.04
+* TensorFlow: 2.4
 
-The following Dockerfile can be customized for your personal workflows.
-
-```dockerfile
-FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04:20211221.v1
-
-ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/tensorflow-2.4
-
-# Create conda environment
-RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
-    python=3.7 pip=20.2.4
-
-# Prepend path to AzureML conda environment
-ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
-
-# Install pip dependencies
-RUN HOROVOD_WITH_TENSORFLOW=1 \
-    pip install 'matplotlib>=3.3,<3.4' \
-                'psutil>=5.8,<5.9' \
-                'tqdm>=4.59,<4.60' \
-                'pandas>=1.1,<1.2' \
-                'scipy>=1.5,<1.6' \
-                'numpy>=1.10,<1.20' \
-                'ipykernel~=6.0' \
-                'azureml-core==1.37.0.post1' \
-                'azureml-defaults==1.37.0' \
-                'azureml-mlflow==1.37.0' \
-                'azureml-telemetry==1.37.0' \
-                'tensorboard==2.4.0' \
-                'tensorflow-gpu==2.4.1' \
-                'tensorflow-datasets==4.3.0' \
-                'onnxruntime-gpu>=1.7,<1.8' \
-                'horovod[tensorflow-gpu]==0.21.3'
-
-# This is needed for mpi to locate libpython
-ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
-```
 
 ## Automated ML (AutoML)
 
@@ -216,6 +93,6 @@ For more information on AutoML and Azure ML pipelines, see [use automated ML in
 
 [!INCLUDE [list-of-inference-prebuilt-docker-images](../../includes/aml-inference-list-prebuilt-docker-images.md)]
 
-## Security
+## Support
 Version updates for supported environments are released every two weeks to address vulnerabilities no older than 30 days. 
 
