Update notebook images to include kserve instead of kfserving. Also u…

…pdate repo name. (awslabs#405) **Description of your changes:** Update notebook images to include kserve instead of kfserving. Also update repo name. Tests have passed **Testing:** - [ ] Unit tests pass - [ ] e2e tests pass - Details about new tests (If this PR adds a new feature) - Details about any manual tests performed By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
awshans · Sep 21, 2022 · b40def0 · b40def0
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diff --git a/awsconfigs/apps/jupyter-web-app/configs/spawner_ui_config.yaml b/awsconfigs/apps/jupyter-web-app/configs/spawner_ui_config.yaml
@@ -17,14 +17,14 @@
 spawnerFormDefaults:
   image:
     # The container Image for the user's Jupyter Notebook
-    value: public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2
+    value: public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20
     # The list of available standard container Images
     options:
     - kubeflownotebookswg/jupyter-scipy:v1.6.0
-    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2
-    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2
-    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2
-    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2
+    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2-2022-09-20
+    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20
+    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2-2022-09-20
+    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20
   imageGroupOne:
     # The container Image for the user's Group One Server
     # The annotation `notebooks.kubeflow.org/http-rewrite-uri: /`

diff --git a/...ter-web-app/templates/ConfigMap/jupyter-web-app-config-tgcbb2gdkd-kubeflow-ConfigMap.yaml b/...ter-web-app/templates/ConfigMap/jupyter-web-app-config-tgcbb2gdkd-kubeflow-ConfigMap.yaml
@@ -11,12 +11,12 @@ data:
     \ be available for users to edit.\n#\n# Note that some values can be templated.\
     \ Such values are the names of the\n# Volumes as well as their StorageClass\n\
     spawnerFormDefaults:\n  image:\n    # The container Image for the user's Jupyter\
-    \ Notebook\n    value: public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2\n\
+    \ Notebook\n    value: public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20\n\
     \    # The list of available standard container Images\n    options:\n    - kubeflownotebookswg/jupyter-scipy:v1.6.0\n\
-    \    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2\n\
-    \    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2\n\
-    \    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2\n\
-    \    - public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2\n\
+    \    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2-2022-09-20\n\
+    \    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20\n\
+    \    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2-2022-09-20\n\
+    \    - public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20\n\
     \  imageGroupOne:\n    # The container Image for the user's Group One Server\n\
     \    # The annotation `notebooks.kubeflow.org/http-rewrite-uri: /`\n    # is applied\
     \ to notebook in this group, configuring\n    # the Istio rewrite for containers\

diff --git a/components/notebook-dockerfiles/README.md b/components/notebook-dockerfiles/README.md
@@ -5,12 +5,12 @@ The AWS Distribution of Kubeflow comes with four ready-to-use container images p
 ## The AWS Images
 This directory contains the source code for these jupyter images which is based on the Kubeflow guidelines on building custom images [here](https://v1-4-branch.kubeflow.org/docs/components/notebooks/custom-notebook/) as well as the existing sample Dockerfiles [here](https://github.com/kubeflow/kubeflow/tree/v1.5.0/components/example-notebook-servers). 
 
-The following AWS Optimized container images are available from the [Amazon Elastic Container Registry](https://gallery.ecr.aws/c9e4w0g3/) (Amazon ECR).
+The following AWS Optimized container images are available from the [Amazon Elastic Container Registry](https://gallery.ecr.aws/kubeflow-on-aws/) (Amazon ECR).
 ```
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.3-gpu-py38-cu112-ubuntu20.04-v1.8
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.3-cpu-py38-ubuntu20.04-v1.8
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.11.0-gpu-py38-cu115-ubuntu20.04-e3-v1.1
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.11.0-cpu-py38-ubuntu20.04-e3-v1.1
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20
 ```
 These images are based on AWS Deep Learning Containers which 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 [Deep Learning Container Docs](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/what-is-dlc.html).
 

diff --git a/components/notebook-dockerfiles/pytorch/requirements.txt b/components/notebook-dockerfiles/pytorch/requirements.txt
@@ -14,7 +14,7 @@ ipywidgets==7.7.1
 # Kubeflow Related Packages
 kfp==1.8.13
 kfp-server-api==1.8.3
-kfserving==0.6.1
+kserve==0.9.0
 kubeflow-training==1.4.0
 kubeflow-katib==0.14.0
 
@@ -26,8 +26,8 @@ xgboost==1.6.1
 ipympl==0.9.1
 
 # AWS related packages
-awscli==1.25.44
-boto3==1.24.44
+awscli==1.22.101
+boto3==1.21.0
 
 # Pytorch packages
 # a version mismatch for fastai can cause a different version of torch to get installed, be careful.

diff --git a/components/notebook-dockerfiles/tensorflow/requirements.txt b/components/notebook-dockerfiles/tensorflow/requirements.txt
@@ -14,7 +14,7 @@ ipywidgets==7.7.1
 # Kubeflow Related Packages
 kfp==1.8.13
 kfp-server-api==1.8.3
-kfserving==0.6.1
+kserve==0.9.0
 kubeflow-training==1.4.0
 kubeflow-katib==0.14.0
 
@@ -26,8 +26,8 @@ xgboost==1.6.1
 ipympl==0.9.1
 
 # AWS related packages
-awscli==1.25.44
-boto3==1.24.44
+awscli==1.22.101
+boto3==1.21.0
 
 # TF Packages
 keras==2.9.0

diff --git a/deployments/add-ons/storage/training-sample/Dockerfile b/deployments/add-ons/storage/training-sample/Dockerfile
@@ -1,4 +1,4 @@
-FROM public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.0-cpu-py38
+FROM public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.6.0-cpu-py38
 
 COPY training.py /
 ENTRYPOINT ["python", "/training.py"]
diff --git a/tests/e2e/tests/test_notebook_images.py b/tests/e2e/tests/test_notebook_images.py
@@ -23,11 +23,11 @@
 CUSTOM_RESOURCE_TEMPLATES_FOLDER = "./resources/custom-resource-templates"
 
 NOTEBOOK_IMAGES = [
-    "public.ecr.aws/j1r0q0g6/notebooks/notebook-servers/jupyter-scipy:v1.5.0",
-    "public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2",
-    "public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2",
-    "public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2",
-    "public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2",
+    "kubeflownotebookswg/jupyter-scipy:v1.6.0",
+    "public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2-2022-09-20",
+    "public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20",
+    "public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2-2022-09-20",
+    "public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20",
 ]
 
 testdata = [

diff --git a/website/content/en/docs/add-ons/storage/efs/guide.md b/website/content/en/docs/add-ons/storage/efs/guide.md
@@ -248,7 +248,7 @@ For the automated setup, you can specify the `uid` and `gid` as arguments to the
 
 ### 3.4 Use existing EFS volume as workspace or data volume for a Notebook
 
-Spin up a new Kubeflow notebook server and specify the name of the PVC to be used as the workspace volume or the data volume and specify your desired mount point. We'll assume you created a PVC with the name `efs-claim` via Kubeflow Volumes UI or via the manual setup step [Static Provisioning](#4-option-2-static-provisioning). For our example here, we are using the AWS Optimized Tensorflow 2.6 CPU image provided in the Notebook configuration options (`public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow`). Additionally, use the existing `efs-claim` volume as the workspace volume at the default mount point `/home/jovyan`. The server might take a few minutes to come up. 
+Spin up a new Kubeflow notebook server and specify the name of the PVC to be used as the workspace volume or the data volume and specify your desired mount point. We'll assume you created a PVC with the name `efs-claim` via Kubeflow Volumes UI or via the manual setup step [Static Provisioning](#4-option-2-static-provisioning). For our example here, we are using the AWS Optimized Tensorflow 2.6 CPU image provided in the Notebook configuration options (`public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow`). Additionally, use the existing `efs-claim` volume as the workspace volume at the default mount point `/home/jovyan`. The server might take a few minutes to come up. 
 
 In case the server does not start up in the expected time, do make sure to check:
 1. The Notebook Controller Logs

diff --git a/website/content/en/docs/add-ons/storage/fsx-for-lustre/guide.md b/website/content/en/docs/add-ons/storage/fsx-for-lustre/guide.md
@@ -196,7 +196,7 @@ kubectl apply -f $GITHUB_STORAGE_DIR/notebook-sample/set-permission-job.yaml
 ```
 
 ### 3.2 Using FSx volume as workspace or data volume for a notebook server 
-Spin up a new Kubeflow notebook server and specify the name of the PVC to be used as the workspace volume or the data volume and specify your desired mount point. For our example here, we are using the AWS-optimized Tensorflow 2.6 CPU image provided in the Notebook configuration options (`public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow`). Additionally, use the existing PVC as the workspace volume at the default mount point `/home/jovyan`. The server might take a few minutes to come up. 
+Spin up a new Kubeflow notebook server and specify the name of the PVC to be used as the workspace volume or the data volume and specify your desired mount point. For our example here, we are using the AWS-optimized Tensorflow 2.6 CPU image provided in the Notebook configuration options (`public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow`). Additionally, use the existing PVC as the workspace volume at the default mount point `/home/jovyan`. The server might take a few minutes to come up. 
 
 In case the server does not start up in the expected time, do make sure to check:
 1. The Notebook Controller Logs

diff --git a/website/content/en/docs/component-guides/notebooks.md b/website/content/en/docs/component-guides/notebooks.md
@@ -17,13 +17,13 @@ You can use Notebooks with Kubeflow on AWS to:
 
 Use AWS-optimized Kubeflow Notebook server images to quickly get started with a range of framework, library, and hardware options. These images are built on top of the [AWS Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/what-is-dlc.html) along with other Kubeflow specific packages. 
 
-These container images are available on the [Amazon Elastic Container Registry (Amazon ECR)](https://gallery.ecr.aws/c9e4w0g3/). The following images are available as part of this release, however you can always find the latest updated images in the linked ECR repository. 
+These container images are available on the [Amazon Elastic Container Registry (Amazon ECR)](https://gallery.ecr.aws/kubeflow-on-aws/). The following images are available as part of this release, however you can always find the latest updated images in the linked ECR repository. 
 
 ```
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.3-gpu-py38-cu112-ubuntu20.04-v1.8
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-tensorflow:2.6.3-cpu-py38-ubuntu20.04-v1.8
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.11.0-gpu-py38-cu115-ubuntu20.04-e3-v1.1
-public.ecr.aws/c9e4w0g3/notebook-servers/jupyter-pytorch:1.11.0-cpu-py38-ubuntu20.04-e3-v1.1
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-gpu-py39-cu112-ubuntu20.04-e3-v1.2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-tensorflow:2.9.1-cpu-py39-ubuntu20.04-e3-v1.2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-gpu-py38-cu116-ubuntu20.04-ec2-2022-09-20
+public.ecr.aws/kubeflow-on-aws/notebook-servers/jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20
 ```
 
 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](https://github.com/aws/deep-learning-containers/blob/master/available_images.md).