Merge pull request #536 from raydouglass/update-dockerhub

Update dockerhub-readme for Ubuntu 22.04
rapidsai · Mar 15, 2023 · df87703 · df87703
2 parents 6c81e8b + 4d70971
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diff --git 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
@@ -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
@@ -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
@@ -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.