Merge branch 'main' of github.com:rapidsai/deployment into migrate-az…

…ure-mnmg

.github/workflows/build-and-deploy.yml

@@ -21,7 +21,7 @@ jobs:
     name: Build (and deploy)
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3.1.0
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -38,7 +38,7 @@ jobs:
           DEPLOYMENT_DOCS_BUILD_STABLE: ${{ startsWith(github.event.ref, 'refs/tags/') && 'true' || 'false' }}
         run: make dirhtml SPHINXOPTS="-W --keep-going -n"
 
-      - uses: aws-actions/configure-aws-credentials@v1-node16
+      - uses: aws-actions/configure-aws-credentials@v4
         if: ${{ github.repository == 'rapidsai/deployment' && github.event_name == 'push' }}
         with:
           role-to-assume: ${{ vars.AWS_ROLE_ARN }}
@@ -49,4 +49,4 @@ jobs:
         if: ${{ github.repository == 'rapidsai/deployment' && github.event_name == 'push' }}
         env:
           DESTINATION_DIR: ${{ startsWith(github.event.ref, 'refs/tags/') && 'stable' || 'nightly' }}
-        run: aws s3 sync --no-progress --delete build/dirhtml "s3://rapidsai-docs/deployment/${DESTINATION_DIR}/html"
+        run: aws s3 sync --no-progress --delete build/dirhtml "s3://rapidsai-docs/deployment/html/${DESTINATION_DIR}"

source/cloud/aws/ec2-multi.md

@@ -39,7 +39,7 @@ cluster = EC2Cluster(
     worker_class="dask_cuda.CUDAWorker",
     worker_options={"rmm-managed-memory": True},
     security_groups=["<SECURITY GROUP ID>"],
-    docker_args="--shm-size=256m -e DISABLE_JUPYTER=true",
+    docker_args="--shm-size=256m",
     n_workers=3,
     security=False,
     availability_zone="us-east-1a",

source/cloud/azure/azure-vm-multi.md

@@ -39,7 +39,7 @@ cluster = AzureVMCluster(
     n_workers=2,
     worker_class="dask_cuda.CUDAWorker",
     docker_image="{{rapids_container}}",
-    docker_args="-e DISABLE_JUPYTER=true -p 8787:8787 -p 8786:8786",
+    docker_args="-p 8787:8787 -p 8786:8786",
 )
 ```
 

source/cloud/gcp/vertex-ai.md

@@ -25,14 +25,21 @@ $ docker push gcr.io/<project>/<folder>/{{ rapids_container.replace('rapidsai/',
 ## Create a New Notebook
 
 1. From the Google Cloud UI, navigate to [**Vertex AI**](https://console.cloud.google.com/vertex-ai) -> **Dashboard** and select **+ CREATE NOTEBOOK INSTANCE**.
-2. Under the **Environment** section, specify **Custom container**, and in the section below, select the `gcr.io` path to your pushed RAPIDS Docker image.
-3. Under **Machine Configuration** select an NVIDIA GPU.
-4. Check the **Install NVIDIA GPU Driver** option.
-5. After customizing any other aspects of the machine you wish, click **CREATE**.
+2. In the **Details** section, under the **Workbench type** heading select **Managed Notebook** from the drop down menu.
+3. Under the **Environment** section, select **Provide custom docker images**, and in the input field below, select the `gcr.io` path to your pushed RAPIDS Docker image.
+4. Under the **Machine type** section select an NVIDIA GPU.
+5. Check the **Install NVIDIA GPU Driver** option.
+6. After customizing any other aspects of the machine you wish, click **CREATE**.
 
-## TEST RAPIDS
+## Test RAPIDS
 
-Once the managed notebook is fully configured, you can click **OPEN JUPYTERLAB** to navigate to another tab running JupyterLab to use the latest version of RAPIDS with Vertex AI.
+Once the managed notebook is fully configured, you can click **OPEN JUPYTERLAB** to navigate to another tab running JupyterLab.
+
+```{warning}
+You should see a popup letting you know it is loading the RAPIDS kernel, this can take a long time so please be patient.
+```
+
+Once the kernel is loaded you can launch a notebook with the `rapids` kernel to use the latest version of RAPIDS with Vertex AI.
 
 For example we could import and use RAPIDS libraries like `cudf`.
 

source/conf.py

@@ -20,18 +20,22 @@
 copyright = f"{datetime.date.today().year}, NVIDIA"
 author = "NVIDIA"
 
+# Single modifiable version for all of the docs - easier for future updates
+stable_version = "23.08"
+nightly_version = "23.10"
+
 versions = {
     "stable": {
-        "rapids_version": "23.06",
-        "rapids_container": "nvcr.io/nvidia/rapidsai/rapidsai-core:23.06-cuda11.8-runtime-ubuntu22.04-py3.10",
+        "rapids_version": stable_version,
+        "rapids_container": f"nvcr.io/nvidia/rapidsai/base:{stable_version}-cuda11.8-py3.10",
         "rapids_conda_channels": "-c rapidsai -c conda-forge -c nvidia",
-        "rapids_conda_packages": "rapids=23.06 python=3.10 cudatoolkit=11.8",
+        "rapids_conda_packages": f"rapids={stable_version} python=3.10 cudatoolkit=11.8",
     },
     "nightly": {
-        "rapids_version": "23.08-nightly",
-        "rapids_container": "rapidsai/rapidsai-core-nightly:23.08-cuda11.8-runtime-ubuntu22.04-py3.10",
+        "rapids_version": f"{nightly_version}-nightly",
+        "rapids_container": f"rapidsai/base:{nightly_version + 'a'}-cuda11.8-py3.10",
         "rapids_conda_channels": "-c rapidsai-nightly -c conda-forge -c nvidia",
-        "rapids_conda_packages": "rapids=23.08 python=3.10 cudatoolkit=11.8",
+        "rapids_conda_packages": f"rapids={nightly_version} python=3.10 cudatoolkit=11.8",
     },
 }
 rapids_version = (

source/examples/rapids-autoscaling-multi-tenant-kubernetes/image-prepuller.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       initContainers:
         - name: prepull-rapids
-          image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10
+          image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:23.08-cuda12.0-py3.10
           command: ["sh", "-c", "'true'"]
       containers:
         - name: pause