Add CUDA 12.2 in nightlies

_includes/selector.html

@@ -370,8 +370,8 @@
 
             // all possible values
             python_vers: ["3.9", "3.10"],
-            cuda_vers: ["11.2", "11.8", "12.0"],
-            pip_cuda_vers: ["11.2 - 11.8", "12.0"],
+            cuda_vers: ["11.2", "11.8", "12.0", "12.2"],
+            pip_cuda_vers: ["11.2 - 11.8", "12"],
             methods: ["Conda", "pip", "Docker"],
             releases: ["Stable", "Nightly"],
             img_loc: ["NGC", "Dockerhub"],
@@ -627,9 +627,9 @@
             },
             getpipNotes() {
                 var notes = [];
-                var install_location_notes = "<code>cuDF</code>, <code>dask-cuDF</code>, <code>cuML</code>, <code>cuGraph</code>, <code>cuSpatial</code>, <code>cuProj</code>, <code>cuxfilter</code>, <code>cuCIM</code>, and <code>RAFT</code> pip packages are hosted by NVIDIA<br>"
+                var install_location_notes = "RAPIDS pip packages are hosted by NVIDIA<br>"
                 notes = [...notes, install_location_notes,
-                    'pip installation supports Python <code>3.9</code> and <code>3.10</code><br>'];
+                    'pip installation supports only Python <code>3.9</code> and <code>3.10</code><br>'];
 
                 return notes.map(note => this.note_prefix + " " + note);
             },
@@ -668,6 +668,7 @@
                 var isDisabled = false;
                 if (this.active_additional_packages.includes("TensorFlow") && (cuda_version !== "12.0")) isDisabled = true;
                 if (this.active_method === "Docker" && cuda_version < "11.8") isDisabled = true;
+                if (this.active_release != "Nightly" && cuda_version === "12.2") isDisabled = true;
                 return isDisabled;
             },
             disableUnsupportedPython(python_version) {
@@ -696,6 +697,8 @@
             },
             releaseClickHandler(e, release) {
                 if (this.isDisabled(e.target)) return;
+                // Remove below after we support 12.2 in stable
+                if (this.active_cuda_ver === "12.2" && release === "Stable") this.active_cuda_ver = "12.0";
                 this.active_release = release;
             },
             imgTypeClickHandler(e, type) {

install/install.md

@@ -137,14 +137,15 @@ All provisioned systems need to be RAPIDS capable. Here's what is required:
 - <i class="fas fa-check-circle"></i> [CUDA 11.5](https://developer.nvidia.com/cuda-11-5-0-download-archive){: target="_blank"} with Driver 495.29.05 or newer
 - <i class="fas fa-check-circle"></i> [CUDA 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive){: target="_blank"} with Driver 520.61.05 or newer
 - <i class="fas fa-check-circle"></i> [CUDA 12.0](https://developer.nvidia.com/cuda-12-0-1-download-archive){: target="_blank"} with Driver 525.60.13 or newer **see CUDA 12 section below for notes on usage**
+- <i class="fas fa-check-circle"></i> [CUDA 12.2](https://developer.nvidia.com/cuda-12-2-2-download-archive){: target="_blank"} with Driver 535.86.10 or newer **see CUDA 12 section below for notes on usage**
 
  **Note**: RAPIDS is tested with and officially supports the versions listed above. Newer CUDA and driver versions may also work with RAPIDS. See [CUDA compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html) for details. 
 
 ## **CUDA 12 Support**
 
 ### **Docker and Conda**
 
-- <i class="fas fa-info-circle"></i> CUDA 12 conda packages and Docker images currently support CUDA 12.0
+- <i class="fas fa-info-circle"></i> Stable CUDA 12 conda packages and Docker images currently support CUDA 12.0. Additionally, nightly versions now support CUDA 12.2
 - <i class="fas fa-info-circle"></i> CUDA 11 conda packages and Docker images can be used on a system with a CUDA 12 driver because they include their own CUDA toolkit
 
 ### **pip**
@@ -334,8 +335,9 @@ Windows users can now tap into GPU accelerated data science on their local machi
 1. Install WSL2 and the Ubuntu 22.04 package [using Microsoft's instructions](https://docs.microsoft.com/en-us/windows/wsl/install){: target="_blank"}.
 2. Install the [latest NVIDIA Drivers](https://www.nvidia.com/download/index.aspx){: target="_blank"} on the Windows host.
 3. Log in to the WSL2 Linux instance.
-4. Follow [this helpful developer guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl2){: target="_blank"} and then install the WSL-specific [CUDA 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} or [CUDA 12.0](https://developer.nvidia.com/cuda-12-0-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} Toolkit without drivers into the WSL2 instance.
+4. Follow [this helpful developer guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl2){: target="_blank"} and then install the WSL-specific [CUDA 11](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} or [CUDA 12](https://developer.nvidia.com/cuda-12-0-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} Toolkit without drivers into the WSL2 instance.
 	- The installed CUDA Toolkit version must match the pip wheel version (`-cu11` or `-cu12`)
+	- Any CUDA 12 CTK will work with RAPIDS `-cu12` pip packages
 5. Install RAPIDS pip packages on the WSL2 Linux Instance using the [release selector](#selector) commands.
 6. Run this code to check that the RAPIDS installation is working:
 	```