fasadvu.blogg.se

1. #Docker windows base image dockerfile example install#
2. #Docker windows base image dockerfile example license#

Src = ScriptRunConfig( source_directory = '.', core import ScriptRunConfig ws = Workspace. core import Workspace, Experiment from azureml. To see which base image is used by a specific curated environment, you can run the following:įrom azureml.

If you are using an Azure ML curated environment, those are already configured with one of the Azure ML base images.

In some cases, the Azure ML base images will be used by default:īy default, if no base image is explicitly set by the user for a training run, Azure ML will use the image corresponding to _CPU_IMAGE.

#Docker windows base image dockerfile example license#

For NVIDIA CUDA and cuDNN take a look at the ThirdPartyNotices.txt file inside the docker container for more information about NVIDIA’s license terms Take a look at LICENSE.txt file inside the docker container for more information. GPU images pulled from MCR can only be used with Azure Services. If you observe the naming convention, image tag maps to the folder name that contains the corresponding Dockerfile. GPU image example: docker pull /azureml/openmpi3.1.2-cuda10.1-cudnn7-ubuntu18.04.CPU image example: docker pull /azureml/openmpi3.1.2-ubuntu18.04.You can pull these images from MCR using the following command: nvidia/cuda:11.1.1-cudnn8-devel-ubuntu20.04Īll images in this repository are published to Microsoft Container Registry (MCR).The GPU images for cuda11 are built from: The GPU images for cuda10 are built from: The GPU images for cuda9 are built from nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04. The CPU images are built from ubuntu18.04 and ubuntu20.04. For more detailed information, please view the dockerfiles. The major dependencies installed in the base images are Miniconda, OpenMPI, CUDA, cuDNN, NCCL, and git. We recommend using images supporting Ubuntu 18.04 for the timebeing as we transition towards providing 20.04 images.Ĭurrently Azure ML supports cuda9, cuda10 and cuda11 base images. Please note that images supporting Ubuntu 16.04 are now deprecated.

#Docker windows base image dockerfile example install#

If you require the Azure ML SDK package for your job, make sure you also install the appropriate package. Note that these base images do not come with Python packages, notably the Azure ML Python SDK, installed. If you specify any conda dependencies in your Environment, the extra dependencies are installed on top of the dependencies in the Docker image. You can also use these Docker images as base images for your custom Azure ML Environments. While submitting a training job on AmlCompute or any other target with Docker enabled, Azure ML runs your job in a conda environment within a Docker container. These Docker images serve as base images for training and inference in Azure ML.

This repository contains Dockerfiles for the base images used in Azure Machine Learning.