Update Azure ML XGBoost instructions to Ubuntu 24.04

source/_includes/check-gpu-pod-works.md

@@ -1,7 +1,7 @@
 Let's create a sample pod that uses some GPU compute to make sure that everything is working as expected.
 
-```console
-$ cat << EOF | kubectl create -f -
+```bash
+cat << EOF | kubectl create -f -
 apiVersion: v1
 kind: Pod
 metadata:

source/cloud/azure/aks.md

@@ -22,8 +22,8 @@ $ az login
 
 Now we can launch a GPU enabled AKS cluster. First launch an AKS cluster.
 
-```console
-$ az aks create -g <resource group> -n rapids \
+```bash
+az aks create -g <resource group> -n rapids \
         --enable-managed-identity \
         --node-count 1 \
         --enable-addons monitoring \
@@ -91,8 +91,8 @@ $ az extension add --name aks-preview
 
 `````
 
-```console
-$ az aks nodepool add \
+```bash
+    az aks nodepool add \
     --resource-group <resource group> \
     --cluster-name rapids \
     --name gpunp \

source/cloud/azure/azureml.md

@@ -32,7 +32,7 @@ The compute instance provides an integrated Jupyter notebook service, JupyterLab
 
 Sign in to [Azure Machine Learning Studio](https://ml.azure.com/) and navigate to your workspace on the left-side menu.
 
-Select **Compute** > **+ New** > choose a [RAPIDS compatible GPU](https://medium.com/dropout-analytics/which-gpus-work-with-rapids-ai-f562ef29c75f) VM size (e.g., `Standard_NC12s_v3`)
+Select **Compute** > **+ New** (Create compute instance) > choose a [RAPIDS compatible GPU](https://medium.com/dropout-analytics/which-gpus-work-with-rapids-ai-f562ef29c75f) VM size (e.g., `Standard_NC12s_v3`)
 
 ![Screenshot of create new notebook with a gpu-instance](../../images/azureml-create-notebook-instance.png)
 

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

@@ -12,7 +12,7 @@
     ]
    },
    "source": [
-    "# Train and Hyperparameter-Tune with RAPIDS"
+    "# Train and Hyperparameter-Tune with RAPIDS on AzureML"
    ]
   },
   {
@@ -97,12 +97,17 @@
     "from azure.ai.ml import MLClient\n",
     "from azure.identity import DefaultAzureCredential\n",
     "\n",
+    "\n",
+    "subscription_id = \"FILL IN WITH YOUR AZURE ML CREDENTIALS\"\n",
+    "resource_group_name = \"FILL IN WITH YOUR AZURE ML CREDENTIALS\"\n",
+    "workspace_name = \"FILL IN WITH YOUR AZURE ML CREDENTIALS\"\n",
+    "\n",
     "# Get a handle to the workspace\n",
     "ml_client = MLClient(\n",
     "    credential=DefaultAzureCredential(),\n",
-    "    subscription_id=\"fc4f4a6b-4041-4b1c-8249-854d68edcf62\",\n",
-    "    resource_group_name=\"rapidsai-deployment\",\n",
-    "    workspace_name=\"rapids-aml-cluster\",\n",
+    "    subscription_id=  subscription_id,\n",
+    "    resource_group_name= resource_group_name,\n",
+    "    workspace_name= workspace_name\n",
     ")\n",
     "\n",
     "print(\n",

source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb

@@ -178,10 +178,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "location = \"West US 2\"\n",
-    "resource_group = \"rapidsai-deployment\"\n",
-    "vnet = \"rapidsai-deployment-vnet\"\n",
-    "security_group = \"rapidsaiclouddeploymenttest-nsg\"\n",
+    "location = \"FILL-THIS-IN\"\n",
+    "resource_group = \"FILL-THIS-IN\"\n",
+    "vnet = \"FILL-THIS-IN\"\n",
+    "security_group = \"FILL-THIS-IN\"\n",
     "vm_size = \"Standard_NC12s_v3\"  # or choose a different GPU enabled VM type\n",
     "\n",
     "docker_image = \"{{rapids_container}}\"\n",

source/guides/azure/infiniband.md

@@ -13,8 +13,8 @@ for demonstration.
 - Select `East US` region.
 - Change `Availability options` to `Availability set` and create a set.
   - If building multiple instances put additional instances in the same set.
-- Use the 2nd Gen Ubuntu 20.04 image.
-  - Search all images for `Ubuntu Server 20.04` and choose the second one down on the list.
+- Use the 2nd Gen Ubuntu 24.04 image.
+  - Search all images for `Ubuntu Server 24.04` and choose the second one down on the list.
 - Change size to `ND40rs_v2`.
 - Set password login with credentials.
   - User `someuser`
@@ -39,8 +39,8 @@ The commands below should work for Ubuntu. See the [CUDA Toolkit documentation](
 ```shell
 sudo apt-get install -y linux-headers-$(uname -r)
 distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
-wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-keyring_1.0-1_all.deb
-sudo dpkg -i cuda-keyring_1.0-1_all.deb
+wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-keyring_1.1-1_all.deb
+sudo dpkg -i cuda-keyring_1.1-1_all.deb
 sudo apt-get update
 sudo apt-get -y install cuda-drivers
 ```
@@ -118,11 +118,11 @@ Mon Nov 14 20:32:39 2022
 
 ### InfiniBand Driver
 
-On Ubuntu 20.04
+On Ubuntu 24.04
 
 ```shell
 sudo apt-get install -y automake dh-make git libcap2 libnuma-dev libtool make pkg-config udev curl librdmacm-dev rdma-core \
-    libgfortran5 bison chrpath flex graphviz gfortran tk dpatch quilt swig tcl ibverbs-utils
+    libgfortran5 bison chrpath flex graphviz gfortran tk  quilt swig tcl ibverbs-utils
 ```
 
 Check install
@@ -247,14 +247,20 @@ wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforg
 bash Mambaforge-Linux-x86_64.sh
 ```
 
-Accept the default and allow conda init to run. Then start a new shell.
+Accept the default and allow conda init to run.
+``shell
+~/mambaforge/bin/conda init
+
+````
+
+Then start a new shell.
 
 Create a conda environment (see [UCX-Py](https://ucx-py.readthedocs.io/en/latest/install.html) docs)
 
 ```shell
 mamba create -n ucxpy {{ rapids_conda_channels }} {{ rapids_conda_packages }} ipython ucx-proc=*=gpu ucx ucx-py dask distributed numpy cupy pytest pynvml -y
 mamba activate ucxpy
-```
+````
 
 Clone UCX-Py repo locally