Merge remote-tracking branch 'origin/main'

Author: VenkateshJaya

_includes/selector.html

@@ -657,7 +657,10 @@
             },
             getCondaNotes() {
                 var notes = [];
-                notes = [...notes, "RAPIDS currently doesn't support <code>channel_priority: strict</code>; use <code>channel_priority: flexible</code> instead"];
+                if (this.active_conda_cuda_ver.startsWith("11")) {
+                    notes = [...notes, "RAPIDS on CUDA 11 doesn't support <code>channel_priority: strict</code>; use <code>channel_priority: flexible</code> instead"];
+                }
+
                 if (this.active_packages.length === 1 && this.active_packages[0] === "Standard") {
                     var pkgs_html = this.rapids_meta_pkgs.map(pkg => "<code>" + pkg + "</code>").join(", ");
                     notes = [...notes, "The <code>Standard</code> selection contains the following packages:<br>" + pkgs_html];

install/index.md

@@ -215,7 +215,13 @@ bash Miniforge3-$(uname)-$(uname -m).sh
 
 **3. Start Conda.** Open a new terminal window, which should now show Conda initialized.
 
-**4. Check Conda Configuration.** Installing RAPIDS requires you to use `channel_priority: flexible`. You can check this and change it, if required, by doing:
+**4. Check Conda Configuration.** Installing RAPIDS may require you to use `channel_priority: flexible`.
+
+If you are installing RAPIDS with CUDA 12 or greater, then you can use either `strict` or `flexible` channel priority.
+
+If you are install RAPIDS with CUDA 11, then you must set `channel_priority: flexible`.
+
+You can check this and change it, if required, by doing:
 ```sh
 conda config --show channel_priority
 conda config --set channel_priority flexible

resources/github-actions.md

@@ -123,7 +123,7 @@ As an example, `rapids-print-env` is used to print common environment informatio
 
 ## Downloading CI Artifacts
 
-Artifacts from both pull-requests and branch builds can be accessed on the Github UI for the specific workflow run, found in the Actions tab of the repository.
+Artifacts from both pull-requests and branch builds can be accessed on the GitHub UI for the specific workflow run, found in the Actions tab of the repository.
 
 ![](/assets/images/workflow-ui.png)
 
@@ -133,7 +133,7 @@ There is also a link provided in the `Run actions/upload-artifact@v4` step of ev
 
 This link downloads the required artifact as a zip file.
 
-This can also be done using `ghcli` using the following command to download the artifact. This command also unzips the artifact to the destination location:
+This can also be done using the following `gh` CLI command to download the artifact. This command also unzips the artifact to the destination location:
 
 ```sh
 gh run download <workflow-run-id> --repo <repo-name> --name <artifact-name> --dir <destination-directory>
@@ -147,9 +147,9 @@ gh run download 14437867406 --repo rapidsai/rmm --name rmm_conda_python_cuda11_p
 
 ## Using Conda CI Artifacts Locally
 
-The artifacts that result from running `conda build` are conda channels. RAPIDS' CI system uploads these conda channels to GitHub Artifacts as zipfiles.
+The artifacts that result from running `conda build` are conda channels. RAPIDS' CI system uploads these conda channels to GitHub Artifacts as zip files.
 
-The packages in the conda channel can be used by downloading the artifact to your local filesystem using `ghcli` and using the resulting path in your conda commands.
+The packages in the conda channel can be used by downloading the artifact to your local filesystem using `gh` CLI and using the resulting path in your conda commands.
 
 For example, the following snippet will download an artifact for `librmm` from workflow run ID `14437867406` and install it into the active conda environment:
 
@@ -163,9 +163,9 @@ Note: Make sure you have the GitHub CLI (`gh`) installed and authenticated on yo
 
 ## Using Wheel CI Artifacts Locally
 
-RAPIDS' CI system compresses the wheels that it builds into zipfiles and uploads them to GitHub Artifacts.
+RAPIDS' CI system compresses the wheels that it builds into zip files and uploads them to GitHub Artifacts.
 
-The wheels can be used byby downloading the artifact to your local filesystem using `ghcli` and using the resulting path in your pip commands.
+The wheels can be used by downloading the artifact to your local filesystem using `gh` CLI and using the resulting path in your pip commands.
 
 For example, the following snippet will download an artifact for `librmm` and install it into the active conda environment: