Merge branch 'main' into sagemaker-sdk

source/cloud/azure/azure-vm.md

@@ -15,11 +15,13 @@ NVIDIA maintains a [Virtual Machine Image (VMI) that pre-installs NVIDIA drivers
 ````{tab-item} via Azure Portal
 :sync: portal
 
-
-1. Select the latest **NVIDIA GPU-Optimized VMI** version from the drop down list, then select **Get It Now**.
-2. If already logged in on Azure, select continue clicking **Create**.
-3. In **Create a virtual machine** interface, fill in required information for the vm.
-   Select a GPU enabled VM size.
+1. Select a resource group or create one if needed.
+2. Select the latest **NVIDIA GPU-Optimized VMI** version from the drop down list, then select **Get It Now** (if there are multiple `Gen` versions, select the latest).
+3. If already logged in on Azure, select continue clicking **Create**.
+4. In **Create a virtual machine** interface, fill in required information for the vm.
+   - Select a GPU enabled VM size (see [recommended VM types](https://docs.rapids.ai/deployment/stable/cloud/azure/)).
+   - In "Configure security features" select Standard.
+   - Make sure you create ssh keys and download them.
 
 ```{dropdown} Note that not all regions support availability zones with GPU VMs.
 :color: info
@@ -88,8 +90,15 @@ Next we need to allow network traffic to the VM so we can access Jupyter and Das
 1. After creating VM, select **Go to resource** to access VM.
 2. Select **Networking** -> **Networking Settings** in the left panel.
 3. Select **+Create port rule** -> **Add inbound port rule**.
-4. Set **Destination port ranges** to `8888,8787`. Keep rest unchanged. Select **Add**.
+4. Set **Destination port ranges** to `8888,8787`.
+5. Modify the "Name" to avoid the `,` or any other symbols.
 
+```{dropdown} See example of port setting.
+:color: info
+:icon: info
+![set-ports-inbound-sec](../../_static/azure-set-ports-inbound-sec.png)
+```
+5. Keep rest unchanged. Select **Add**.
 ````
 
 ````{tab-item} via Azure CLI
@@ -116,6 +125,20 @@ az network nsg rule create \
 
 Next, we can SSH into our VM to install RAPIDS. SSH instructions can be found by selecting **Connect** in the left panel.
 
+````{tip}
+When connecting via SSH by doing
+
+```bash
+ssh -i <path-to-your-ssh-key-dir>/your-key-file.pem <username>@<vm-ip-address>
+```
+
+you might get prompted with a `WARNING: UNPROTECTED PRIVATE KEY FILE!`, and get a
+**"Permission denied"** as a result of this.
+
+Change the permissions of your key file to be less permissive by
+doing `chmod 600 your_key_file.pem`, and you should be good to go.
+````
+
 ```{include} ../../_includes/install-rapids-with-docker.md
 
 ```

source/cloud/gcp/vertex-ai.md

@@ -6,14 +6,13 @@ review_priority: "p0"
 
 RAPIDS can be deployed on [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench).
 
-## Create a new user-managed Notebook
+## Create a new Notebook Instance
 
 1. From the Google Cloud UI, navigate to [**Vertex AI**](https://console.cloud.google.com/vertex-ai/workbench/user-managed) -> Notebook -> **Workbench**
-2. Make sure you select **User-Managed Notebooks** (**Managed Notebooks** are currently not supported) and select **+ CREATE NEW**.
+2. Select **Instances** and select **+ CREATE NEW**.
 3. In the **Details** section give the instance a name.
-4. Under the **Environment** section choose "Python 3 with CUDA 11.8".
-5. Check the "Attach 1 NVIDIA T4 GPU" option.
-6. After customizing any other aspects of the machine you wish, click **CREATE**.
+4. Check the "Attach 1 NVIDIA T4 GPU" option.
+5. After customizing any other aspects of the machine you wish, click **CREATE**.
 
 ```{tip}
 If you want to select a different GPU or select other hardware options you can select "Advanced Options" at the bottom and then make changes in the "Machine type" section.
@@ -23,11 +22,37 @@ If you want to select a different GPU or select other hardware options you can s
 
 Once the instance has started select **OPEN JUPYTER LAB** and at the top of a notebook install the RAPIDS libraries you wish to use.
 
+```{warning}
+Installing RAPIDS via `pip` in the default environment is [not currently possible](https://github.com/rapidsai/deployment/issues/517), for now you must create a new `conda` environment.
+
+Vertex AI currently ships with CUDA Toolkit 11 system packages as of the [Jan 2025 Vertex AI release](https://cloud.google.com/vertex-ai/docs/release-notes#January_31_2025).
+The default Python environment also contains the `cupy-cuda12x` package. This means it's not possible to install RAPIDS package like `cudf` via `pip` as `cudf-cu12` will conflict with the CUDA Toolkit version but `cudf-cu11` will conflict with the `cupy` version.
+
+You can find out your current system CUDA Toolkit version by running `ls -ld /usr/local/cuda*`.
+```
+
+You can create a new RAPIDS conda environment and register it with `ipykernel` for use in Jupyter Lab. Open a new terminal in Jupyter and run the following commands.
+
 ```bash
-pip install \
-    --extra-index-url={{rapids_pip_index}} \
-    "cudf-cu12>={{rapids_pip_version}}" "cuml-cu12>={{rapids_pip_version}}" \
-    "dask-cuda>={{rapids_pip_version}}"
+# Create a new environment
+conda create -y -n rapids \
+    {{ rapids_conda_channels }} \
+    {{ rapids_conda_packages }} \
+    ipykernel
+
+# Activate the environment
+conda activate rapids
+
+# Register the environment with Jupyter
+python -m ipykernel install --prefix "${DL_ANACONDA_HOME}/envs/rapids" --name rapids --display-name rapids
+```
+
+Then refresh the Jupyter Lab page and open the launcher. You will see a new "rapids" kernel available.
+
+![Screenshot of the Jupyter Lab launcher showing the RAPIDS kernel](../../images/vertex-ai-launcher.png)
+
+```{tip}
+If you don't see the new kernel wait a minute and refresh the page again, it can take a little while to show up.
 ```
 
 ## Test RAPIDS

source/cloud/nvidia/brev.md

@@ -1,3 +1,7 @@
+---
+review_priority: "p0"
+---
+
 # NVIDIA Brev
 
 The [NVIDIA Brev](https://brev.dev/) platform provides you with GPU instances on cloud providers including [Amazon Web Services](https://aws.amazon.com/) and [Google Cloud](https://cloud.google.com) with CUDA, Python, Jupyter Lab, all set up.

source/conf.py

@@ -21,8 +21,8 @@
 author = "NVIDIA"
 
 # Single modifiable version for all of the docs - easier for future updates
-stable_version = "24.10"
-nightly_version = "24.12"
+stable_version = "24.12"
+nightly_version = "25.02"
 
 versions = {
     "stable": {

source/examples/index.md

@@ -20,4 +20,5 @@ xgboost-rf-gpu-cpu-benchmark/notebook
 xgboost-dask-databricks/notebook
 xgboost-azure-mnmg-daskcloudprovider/notebook
 rapids-1brc-single-node/notebook
+rapids-snowflake-cudf/notebook
 ```

source/examples/rapids-1brc-single-node/notebook.ipynb

@@ -4,19 +4,23 @@
    "cell_type": "markdown",
    "id": "5a19dc1a",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
      "tools/dask-cuda",
-     "aws/ec2",
-     "aws/sagemaker",
-     "azure/azure-vm",
-     "azure/ml",
-     "gcp/compute-engine",
-     "gcp/vertex-ai",
      "data-format/csv",
      "library/cudf",
      "library/cupy",
      "library/dask",
-     "library/pandas"
+     "library/pandas",
+     "cloud/aws/ec2",
+     "cloud/aws/sagemaker",
+     "cloud/azure/azure-vm",
+     "cloud/azure/ml",
+     "cloud/gcp/compute-engine",
+     "cloud/gcp/vertex-ai"
     ]
    },
    "source": [
@@ -499,9 +503,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rapids",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "rapids"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

source/examples/rapids-azureml-hpo/notebook.ipynb

@@ -3,12 +3,16 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
-     "workflows/hpo",
      "cloud/azure/ml",
      "library/cudf",
      "library/cuml",
-     "library/randomforest"
+     "library/randomforest",
+     "workflow/hpo"
     ]
    },
    "source": [
@@ -484,9 +488,9 @@
    "name": "rapids"
   },
   "kernelspec": {
-   "display_name": "rapids",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "rapids"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {