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This is a static snapshot from the time of the Kubeflow 1.3 release.
For up-to-date information, see the latest version.

AWS-Optimized Kubeflow Notebooks

Work in AWS-optimized Notebooks based on AWS Deep Learning Containers

Installing Kubeflow on AWS includes AWS-optimized container images as default options for a Kubeflow Jupyter Notebook server. For more information on gettings started with Kubeflow Notebooks, see the Quickstart Guide.

AWS-optimized container images

The following container images are available from the Amazon Elastic Container Registry (Amazon ECR).

public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.0-gpu-py38-cu112
public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.0-cpu-py38
public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.9.0-gpu-py38-cu111
public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.9.0-cpu-py38

These images are based on AWS Deep Learning Containers. AWS Deep Learning Containers provide optimized environments with popular machine learning frameworks such as TensorFlow and PyTorch, and are available in the Amazon ECR. For more information on AWS Deep Learning Container options, see Available Deep Learning Containers Images.

Along with specific machine learning frameworks, these container images have additional pre-installed packages:

  • kfp
  • kfserving
  • h5py
  • pandas
  • awscli
  • boto3

Last modified 07.03.2022: Added AWS 1.3 documentation to Kubeflow site (v1.3 branch) (#3174) (1d8970db)