Merge branch 'xgboost-azure' of github.com:rapidsai/deployment into x…

…gboost-azure

source/cloud/azure/azure-vm.md

@@ -128,7 +128,7 @@ Next, we can SSH into our VM to install RAPIDS. SSH instructions can be found by
 
 ### Useful Links
 
-- [Using NGC with Azure](https://docs.nvidia.com/ngc/ngc-azure-setup-guide/index.html)
+- [Using NGC with Azure](https://docs.nvidia.com/ngc/ngc-deploy-public-cloud/ngc-azure/index.html)
 
 ```{relatedexamples}
 

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

@@ -72,7 +72,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Initialize`MLClient`[class](https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python) to handle the workspace you created in the prerequisites step. \n",
+    "Initialize `MLClient` [class](https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python) to handle the workspace you created in the prerequisites step. \n",
     "\n",
     "You can manually provide the workspace details or call `MLClient.from_config(credential, path)`\n",
     "to create a workspace object from the details stored in `config.json`"
@@ -307,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll be using a custom RAPIDS docker image to [setup the environment]((https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=python#create-an-environment-from-a-docker-image). This is available in `rapidsai/rapidsai` repo on [DockerHub](https://hub.docker.com/r/rapidsai/rapidsai/).\n",
+    "We'll be using a custom RAPIDS docker image to [setup the environment](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=python#create-an-environment-from-a-docker-image). This is available in `rapidsai/rapidsai` repo on [DockerHub](https://hub.docker.com/r/rapidsai/rapidsai/).\n",
     "\n",
     "Make sure you have the correct path to the docker build context as `os.getcwd()`,"
    ]

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

@@ -277,7 +277,7 @@
     " \n",
     "Optuna uses [studies](https://optuna.readthedocs.io/en/stable/reference/study.html) and [trials](https://optuna.readthedocs.io/en/stable/reference/trial.html) to keep track of the HPO experiments. Put simply, a trial is a single call of the objective function while a set of trials make up a study. We will pick the best observed trial from a study to get the best parameters that were used in that run.\n",
     "\n",
-    "Here, `DaskStorage` class is used to set up a storage shared by all workers in the cluster. Learn more about what storages can be used [here](https://optuna.readthedocs.io/en/stable/tutorial/distributed.html)\n",
+    "Here, `DaskStorage` class is used to set up a storage shared by all workers in the cluster. Learn more about what storages can be used [here](https://optuna.readthedocs.io/en/stable/reference/storages.html)\n",
     "\n",
     "`optuna.create_study` is used to set up the study. As you can see, it specifies the study name, sampler to be used, the direction of the study, and the storage.\n",
     "With just a few lines of code, we have set up a distributed HPO experiment."
@@ -347,7 +347,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Conluding Remarks\n",
+    "## Concluding Remarks\n",
     " \n",
     "This notebook shows how RAPIDS and Optuna can be used along with dask to run multi-GPU HPO jobs, and can be used as a starting point for anyone wanting to get started with the framework. We have seen how by just adding a few lines of code we were able to integrate the libraries for a muli-GPU HPO runs. This can also be scaled to multiple nodes.\n",
     " \n",

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

@@ -43,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src='../../_static/images/examples/rapids-sagemaker-hpo/hpo.png'>"
+    "![](../../_static/images/examples/rapids-sagemaker-hpo/hpo.png)"
    ]
   },
   {
@@ -595,7 +595,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src='../../_static/images/examples/rapids-sagemaker-hpo/ml_workflow.png' width='800'> "
+    "![](../../_static/images/examples/rapids-sagemaker-hpo/ml_workflow.png) "
    ]
   },
   {
@@ -707,7 +707,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src='../../_static/images/examples/rapids-sagemaker-hpo/estimator.png' width='800'>"
+    "![](../../_static/images/examples/rapids-sagemaker-hpo/estimator.png)"
    ]
   },
   {
@@ -1477,7 +1477,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src='../../_static/images/examples/rapids-sagemaker-hpo/run_hpo.png'>"
+    "![](../../_static/images/examples/rapids-sagemaker-hpo/run_hpo.png)"
    ]
   },
   {
@@ -2186,7 +2186,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src='../../_static/images/examples/rapids-sagemaker-hpo/results.png' width='70%'>"
+    "![](../../_static/images/examples/rapids-sagemaker-hpo/results.png)"
    ]
   },
   {

source/platforms/coiled.md

@@ -82,7 +82,7 @@ We can also connect a Dask client to see that information for the workers too.
 ```python
 from dask.distributed import Client
 
-client = Client(cluster)
+client = Client()
 client
 ```
 

source/platforms/kubeflow.md

@@ -83,7 +83,7 @@ To use Dask, we need to create a scheduler and some workers that will perform ou
 
 ### Installing the Dask Kubernetes operator
 
-To install the operator we need to create any custom resources and the operator itself, please [refer to the documentation](https://kubernetes.dask.org/en/latest/operator_installation.html) to find up-to-date installation instructions. From the terminal run the following command.
+To install the operator we need to create any custom resources and the operator itself, please [refer to the documentation](https://kubernetes.dask.org/en/latest/installing.html) to find up-to-date installation instructions. From the terminal run the following command.
 
 ```console
 $ helm install --repo https://helm.dask.org --create-namespace -n dask-operator --generate-name dask-kubernetes-operator

source/tools/kubernetes/dask-operator.md

@@ -1,7 +1,7 @@
 # Dask Operator
 
 Many libraries in RAPIDS can leverage Dask to scale out computation onto multiple GPUs and multiple nodes.
-[Dask has an operator for Kubernetes](https://kubernetes.dask.org/en/latest/operator.html) which allows you to launch Dask clusters as native Kubernetes resources.
+[Dask has an operator for Kubernetes](https://kubernetes.dask.org/en/latest/) which allows you to launch Dask clusters as native Kubernetes resources.
 
 With the operator and associated Custom Resource Definitions (CRDs)
 you can create `DaskCluster`, `DaskWorkerGroup` and `DaskJob` resources that describe your Dask components and the operator will
@@ -45,7 +45,7 @@ graph TD
 
 Your Kubernetes cluster must have GPU nodes and have [up to date NVIDIA drivers installed](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/getting-started.html).
 
-To install the Dask operator follow the [instructions in the Dask documentation](https://kubernetes.dask.org/en/latest/operator_installation.html).
+To install the Dask operator follow the [instructions in the Dask documentation](https://kubernetes.dask.org/en/latest/installing.html).
 
 ## Configuring a RAPIDS `DaskCluster`
 
@@ -226,7 +226,7 @@ spec:
 ```
 
 For the scheduler pod we are also setting the `rapidsai/base` container image, mainly to ensure our Dask versions match between
-the scheduler and workers. We also disable Jupyter and ensure that the `dask-scheduler` command is configured.
+the scheduler and workers. We ensure that the `dask-scheduler` command is configured.
 
 Then we configure both the Dask communication port on `8786` and the Dask dashboard on `8787` and add some probes so that Kubernetes can monitor
 the health of the scheduler.