diff --git a/dockerhub-readme/generated-readmes/ngc-rapidsai-core-dev.md b/dockerhub-readme/generated-readmes/ngc-rapidsai-core-dev.md
index f2dbddca..095e8b07 100644
--- a/dockerhub-readme/generated-readmes/ngc-rapidsai-core-dev.md
+++ b/dockerhub-readme/generated-readmes/ngc-rapidsai-core-dev.md
@@ -39,7 +39,7 @@ This repo (rapidsai/rapidsai-core-dev), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-devel-ubuntu18.04-py3.10
+23.02-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -52,8 +52,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -63,16 +63,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -111,7 +111,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -134,7 +134,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -162,14 +162,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/ngc-rapidsai-core.md b/dockerhub-readme/generated-readmes/ngc-rapidsai-core.md
index c6ee281a..8c863790 100644
--- a/dockerhub-readme/generated-readmes/ngc-rapidsai-core.md
+++ b/dockerhub-readme/generated-readmes/ngc-rapidsai-core.md
@@ -39,7 +39,7 @@ The [rapidsai/rapidsai-core-dev](https://catalog.ngc.nvidia.com/orgs/nvidia/team
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+23.02-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -50,16 +50,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04
+cuda11.8-runtime-ubuntu22.04
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -69,16 +69,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -117,7 +117,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -140,7 +140,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -168,14 +168,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/ngc-rapidsai-dev.md b/dockerhub-readme/generated-readmes/ngc-rapidsai-dev.md
index bdad0006..4a296eb6 100644
--- a/dockerhub-readme/generated-readmes/ngc-rapidsai-dev.md
+++ b/dockerhub-readme/generated-readmes/ngc-rapidsai-dev.md
@@ -39,7 +39,7 @@ This repo (rapidsai/rapidsai-dev), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-devel-ubuntu18.04-py3.10
+23.02-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -52,8 +52,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -63,16 +63,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -111,7 +111,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -134,7 +134,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -162,14 +162,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/ngc-rapidsai.md b/dockerhub-readme/generated-readmes/ngc-rapidsai.md
index 461c97a2..035d6908 100644
--- a/dockerhub-readme/generated-readmes/ngc-rapidsai.md
+++ b/dockerhub-readme/generated-readmes/ngc-rapidsai.md
@@ -39,7 +39,7 @@ The [rapidsai/rapidsai-dev](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/rap
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+23.02-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -50,16 +50,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04
+cuda11.8-runtime-ubuntu22.04
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -69,16 +69,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -117,7 +117,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -140,7 +140,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -168,14 +168,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         nvcr.io/nvidia/rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-core-dev-nightly.md b/dockerhub-readme/generated-readmes/rapidsai-core-dev-nightly.md
index 38938dee..a9f30142 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-core-dev-nightly.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-core-dev-nightly.md
@@ -46,7 +46,7 @@ This repo (rapidsai/rapidsai-core-dev-nightly), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.04-cuda11.8-devel-ubuntu18.04-py3.10
+23.04-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -59,8 +59,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -70,16 +70,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -118,7 +118,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -141,7 +141,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -169,14 +169,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-core-dev.md b/dockerhub-readme/generated-readmes/rapidsai-core-dev.md
index 20a70122..4dd1b8fb 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-core-dev.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-core-dev.md
@@ -39,7 +39,7 @@ This repo (rapidsai/rapidsai-core-dev), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-devel-ubuntu18.04-py3.10
+23.02-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -52,8 +52,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -63,16 +63,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -111,7 +111,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -134,7 +134,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -162,14 +162,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-core-nightly.md b/dockerhub-readme/generated-readmes/rapidsai-core-nightly.md
index e3bb943d..073b1643 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-core-nightly.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-core-nightly.md
@@ -46,7 +46,7 @@ The [rapidsai/rapidsai-core-dev-nightly](https://hub.docker.com/r/rapidsai/rapid
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+23.04-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -57,16 +57,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04-py3.10
+cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -76,16 +76,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -124,7 +124,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -147,7 +147,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -175,14 +175,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-core.md b/dockerhub-readme/generated-readmes/rapidsai-core.md
index d29be923..6342462d 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-core.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-core.md
@@ -39,7 +39,7 @@ The [rapidsai/rapidsai-core-dev](https://hub.docker.com/r/rapidsai/rapidsai-core
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+23.02-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -50,16 +50,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04-py3.10
+cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -69,16 +69,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -117,7 +117,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -140,7 +140,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -168,14 +168,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-dev-nightly.md b/dockerhub-readme/generated-readmes/rapidsai-dev-nightly.md
index 54831089..8928ef30 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-dev-nightly.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-dev-nightly.md
@@ -46,7 +46,7 @@ This repo (rapidsai/rapidsai-dev-nightly), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.04-cuda11.8-devel-ubuntu18.04-py3.10
+23.04-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -59,8 +59,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -70,16 +70,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -118,7 +118,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -141,7 +141,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -169,14 +169,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev-nightly:23.04-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-dev.md b/dockerhub-readme/generated-readmes/rapidsai-dev.md
index 9ca34051..582ce1b0 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-dev.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-dev.md
@@ -39,7 +39,7 @@ This repo (rapidsai/rapidsai-dev), contains the following:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-devel-ubuntu18.04-py3.10
+23.02-cuda11.8-devel-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -52,8 +52,8 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -63,16 +63,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -111,7 +111,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -134,7 +134,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+    rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -162,14 +162,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu18.04-py3.10
+         rapidsai/rapidsai-dev:23.02-cuda11.8-devel-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai-nightly.md b/dockerhub-readme/generated-readmes/rapidsai-nightly.md
index d2b8aefd..12a2ad6a 100644
--- a/dockerhub-readme/generated-readmes/rapidsai-nightly.md
+++ b/dockerhub-readme/generated-readmes/rapidsai-nightly.md
@@ -46,7 +46,7 @@ The [rapidsai/rapidsai-dev-nightly](https://hub.docker.com/r/rapidsai/rapidsai-d
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+23.04-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -57,16 +57,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04-py3.10
+cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -76,16 +76,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -124,7 +124,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -147,7 +147,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -175,14 +175,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai-nightly:23.04-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/generated-readmes/rapidsai.md b/dockerhub-readme/generated-readmes/rapidsai.md
index b2ea5836..97c5ad90 100644
--- a/dockerhub-readme/generated-readmes/rapidsai.md
+++ b/dockerhub-readme/generated-readmes/rapidsai.md
@@ -39,7 +39,7 @@ The [rapidsai/rapidsai-dev](https://hub.docker.com/r/rapidsai/rapidsai-dev/tags)
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+23.02-cuda11.8-runtime-ubuntu22.04-py3.10
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -50,16 +50,16 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA 11.8, Python 3.10, and Ubuntu 18.04, use the following tag:
 ```
-cuda11.8-runtime-ubuntu18.04-py3.10
+cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu18.04-py3.10`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda11.8-runtime-ubuntu22.04-py3.10`.
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -69,16 +69,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+$ docker pull rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Container Ports
@@ -117,7 +117,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Bind Mounts
@@ -140,7 +140,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+    rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -168,14 +168,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu18.04-py3.10
+         rapidsai/rapidsai:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/dockerhub-readme/templates/base.md.j2 b/dockerhub-readme/templates/base.md.j2
index 2e7f749c..7676ede6 100644
--- a/dockerhub-readme/templates/base.md.j2
+++ b/dockerhub-readme/templates/base.md.j2
@@ -65,7 +65,7 @@ The [rapidsai/{{ repo_name | br2devel }}]({% if not is_ngc %}https://hub.docker.
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{DEFAULT_PYTHON_VERSION }}
+{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{DEFAULT_PYTHON_VERSION }}
  ^       ^    ^        ^         ^
  |       |    type     |         python version
  |       |             |
@@ -77,17 +77,17 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
 {% if is_br %}
 To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the `runtime` image with support for CUDA {{ DEFAULT_CUDA_VERSION }}, Python {{ DEFAULT_PYTHON_VERSION }}, and Ubuntu 18.04, use the following tag:
 ```
-cuda{{ DEFAULT_CUDA_VERSION }}-runtime-ubuntu18.04{{ "-py"+DEFAULT_PYTHON_VERSION if not is_ngc }}
+cuda{{ DEFAULT_CUDA_VERSION }}-runtime-{{ DEFAULT_LINUX_VERSION }}{{ "-py"+DEFAULT_PYTHON_VERSION if not is_ngc }}
 ```
 
-Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda{{ DEFAULT_CUDA_VERSION }}-runtime-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}`.
+Many users do not need a specific platform combination but would like to ensure they're getting the latest version of RAPIDS, so as an additional convenience, a tag named simply `latest` is also provided which is equivalent to `cuda{{ DEFAULT_CUDA_VERSION }}-runtime-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}`.
 {% endif %}
 
 ## Prerequisites
 
 - NVIDIA Pascal™ GPU architecture or better
-- CUDA [11.2/11.4/11.5](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
-- Ubuntu 18.04/20.04 or CentOS 7 or Rocky Linux 8
+- CUDA [11.2/11.4/11.5/11.8](https://developer.nvidia.com/cuda-downloads) with a compatible NVIDIA driver
+- Ubuntu 20.04/22.04 or CentOS 7 or Rocky Linux 8
 - Docker CE v18+
 - [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
 
@@ -97,16 +97,16 @@ Many users do not need a specific platform combination but would like to ensure
 
 #### Preferred - Docker CE v19+ and `nvidia-container-toolkit`
 ```bash
-$ docker pull {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+$ docker pull {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
-$ docker pull {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+$ docker pull {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 
 ### Container Ports
@@ -150,7 +150,7 @@ $ docker run \
     -p 8888:8888 \
     -p 8787:8787 \
     -p 8786:8786 \
-    {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+    {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 
 ### Bind Mounts
@@ -173,7 +173,7 @@ $ docker run \
     -it \
     --gpus all \
     -v $(pwd)/environment.yml:/opt/rapids/environment.yml \
-    {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+    {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 
 ### Use JupyterLab to Explore the Notebooks
@@ -201,14 +201,14 @@ You are free to modify the above steps. For example, you can launch an interacti
 ```bash
 $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 
 #### Legacy - Docker CE v18 and `nvidia-docker2`
 ```bash
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
          -v /path/to/host/data:/rapids/my_data \
-         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-ubuntu18.04-py{{ DEFAULT_PYTHON_VERSION }}
+         {{ ngc_prefix if is_ngc }}rapidsai/{{ repo_name }}:{{ repo_rapids_version }}-cuda{{ DEFAULT_CUDA_VERSION }}-{{ repo_default_img_type }}-{{ DEFAULT_LINUX_VERSION }}-py{{ DEFAULT_PYTHON_VERSION }}
 ```
 This will map data from your host operating system to the container OS in the `/rapids/my_data` directory. You may need to modify the provided notebooks for the new data paths.
 
diff --git a/settings.yaml b/settings.yaml
index 44d53b1b..1ebc8fb5 100644
--- a/settings.yaml
+++ b/settings.yaml
@@ -2,6 +2,7 @@
 # Default Docker build arguments
 DEFAULT_PYTHON_VERSION: "3.10"
 DEFAULT_CUDA_VERSION: "11.8"
+DEFAULT_LINUX_VERSION: "ubuntu22.04"
 DEFAULT_STABLE_RAPIDS_VERSION: "23.02"
 DEFAULT_NIGHTLY_RAPIDS_VERSION: "23.04"
 DEFAULT_NEXT_RAPIDS_VERSION: "23.06"