Standardize bash vs console usage and fix copy multiline

README.md

@@ -72,6 +72,14 @@ The [Kubernetes documentation style-guide](https://kubernetes.io/docs/contribute
 encourages the use of `UpperCamelCase` (also known as `PascalCase`) when referring to Kubernetes resources.
 Please make sure to follow this guide when writing documentation that involves Kubernetes.
 
+#### Usage of console and bash blocks
+
+- Use `console` for any set of commands that are entered in a command line that contain output from the command.
+  - Start the line with `$`, they'll be omitted upon copying in sphinx rendered docs.
+  - Use `\` to break lines.
+  - Do not add comments like `# comment` in-line when using break lines because this will break the copying.
+- Use `bash` for any bash script `.sh`, or command line entry without output.
+
 ### Notebooks
 
 The `examples` section of these docs are written in Jupyter Notebooks and built with [MyST-NB](https://myst-nb.readthedocs.io/en/latest/).

source/cloud/aws/ec2-multi.md

@@ -20,7 +20,7 @@ Install the AWS CLI tools following the [official instructions](https://docs.aws
 
 Also install `dask-cloudprovider` and ensure you select the `aws` optional extras.
 
-```console
+```bash
 $ pip install "dask-cloudprovider[aws]"
 ```
 

source/cloud/aws/ec2.md

@@ -35,7 +35,7 @@ If you use the AWS Console, please use the default `ubuntu` user to ensure the N
 Depending on where your ssh key is, when connecting via SSH you might need to do
 
 ```bash
-ssh -i <path-to-your-ssh-key-dir>/your-key-file.pem ubuntu@<ip address>
+$ ssh -i <path-to-your-ssh-key-dir>/your-key-file.pem ubuntu@<ip address>
 ```
 
 If you get prompted with a `WARNING: UNPROTECTED PRIVATE KEY FILE!`, and get a

source/cloud/aws/eks.md

@@ -14,7 +14,7 @@ First you'll need to have the [`aws` CLI tool](https://aws.amazon.com/cli/) and
 
 Ensure you are logged into the `aws` CLI.
 
-```console
+```bash
 $ aws configure
 ```
 
@@ -28,16 +28,18 @@ In your aws console under `EC2` in the side panel under Network & Security > Key
 key pair or import (see "Actions" dropdown) one you've created locally.
 
 2. If you are not using your default AWS profile, add `--profile <your-profile>` to the following command.
+
+3. The `--ssh-public-key` argument is the name assigned during creation of your key in AWS console.
 ```
 
-```console
+```bash
 $ eksctl create cluster rapids \
                       --version 1.30 \
                       --nodes 3 \
                       --node-type=g4dn.xlarge \
                       --timeout=40m \
                       --ssh-access \
-                      --ssh-public-key <public key ID> \  # Name assigned during creation of your key in aws console
+                      --ssh-public-key <public key ID> \
                       --region us-east-1 \
                       --zones=us-east-1c,us-east-1b,us-east-1d \
                       --auto-kubeconfig
@@ -48,7 +50,7 @@ With this command, you’ve launched an EKS cluster called `rapids`. You’ve sp
 To access the cluster we need to pull down the credentials.
 Add `--profile <your-profile>` if you are not using the default profile.
 
-```console
+```bash
 $ aws eks --region us-east-1 update-kubeconfig --name rapids
 ```
 

source/cloud/azure/aks.md

@@ -14,7 +14,7 @@ First you'll need to have the [`az` CLI tool](https://learn.microsoft.com/en-us/
 
 Ensure you are logged into the `az` CLI.
 
-```console
+```bash
 $ az login
 ```
 
@@ -23,7 +23,7 @@ $ az login
 Now we can launch a GPU enabled AKS cluster. First launch an AKS cluster.
 
 ```bash
-az aks create -g <resource group> -n rapids \
+$ az aks create -g <resource group> -n rapids \
         --enable-managed-identity \
         --node-count 1 \
         --enable-addons monitoring \
@@ -77,13 +77,13 @@ Microsoft.ContainerService/GPUDedicatedVHDPreview  Registered
 
 When the status shows as registered, refresh the registration of the `Microsoft.ContainerService` resource provider by using the `az provider register` command:
 
-```console
+```bash
 $ az provider register --namespace Microsoft.ContainerService
 ```
 
 Then install the aks-preview CLI extension, use the following Azure CLI commands:
 
-```console
+```bash
 $ az extension add --name aks-preview
 ```
 
@@ -92,7 +92,7 @@ $ az extension add --name aks-preview
 `````
 
 ```bash
-az aks nodepool add \
+$ az aks nodepool add \
     --resource-group <resource group> \
     --cluster-name rapids \
     --name gpunp \
@@ -108,7 +108,7 @@ Here we have added a new pool made up of `Standard_NC48ads_A100_v4` instances wh
 Then we can install the NVIDIA drivers.
 
 ```bash
-helm install --wait --generate-name --repo https://helm.ngc.nvidia.com/nvidia \
+$ helm install --wait --generate-name --repo https://helm.ngc.nvidia.com/nvidia \
     -n gpu-operator --create-namespace \
     gpu-operator \
     --set operator.runtimeClass=nvidia-container-runtime

source/cloud/azure/azure-vm.md

@@ -54,7 +54,7 @@ Prepare the following environment variables.
 | `AZ_SSH_KEY`       | public ssh key       | `~/.ssh/id_rsa.pub`                                            |
 
 ```bash
-az vm create \
+$ az vm create \
         --name ${AZ_VMNAME} \
         --resource-group ${AZ_RESOURCEGROUP} \
         --image ${AZ_IMAGE} \
@@ -110,7 +110,7 @@ Next we need to allow network traffic to the VM so we can access Jupyter and Das
 | `AZ_NSGRULENAME` | Name for NSG rule   | `Allow-Dask-Jupyter-ports` |
 
 ```bash
-az network nsg rule create \
+$ az network nsg rule create \
     -g ${AZ_RESOURCEGROUP} \
     --nsg-name ${AZ_NSGNAME} \
     -n ${AZ_NSGRULENAME} \
@@ -129,7 +129,7 @@ Next, we can SSH into our VM to install RAPIDS. SSH instructions can be found by
 When connecting via SSH by doing
 
 ```bash
-ssh -i <path-to-your-ssh-key-dir>/your-key-file.pem azureuser@<vm-ip-address>
+$ ssh -i <path-to-your-ssh-key-dir>/your-key-file.pem azureuser@<vm-ip-address>
 ```
 
 you might get prompted with a `WARNING: UNPROTECTED PRIVATE KEY FILE!`, and get a

source/cloud/gcp/dataproc.md

@@ -6,14 +6,28 @@ RAPIDS can be deployed on Google Cloud Dataproc using Dask. For more details, se
 
 It is strongly recommended that you copy the initialization scripts into your own Storage bucket to prevent unintended upgrades from upstream in the cluster:
 
-```console
+```bash
 $ REGION=<region>
+```
+
+```bash
 $ GCS_BUCKET=<bucket_name>
+```
+
+```bash
 $ gcloud storage buckets create gs://$GCS_BUCKET
+```
+
+```bash
 $ gsutil cp gs://goog-dataproc-initialization-actions-${REGION}/gpu/install_gpu_driver.sh gs://$GCS_BUCKET
+```
+
+```bash
 $ gsutil cp gs://goog-dataproc-initialization-actions-${REGION}/dask/dask.sh gs://$GCS_BUCKET
-$ gsutil cp gs://goog-dataproc-initialization-actions-${REGION}/rapids/rapids.sh gs://$GCS_BUCKET
+```
 
+```bash
+$ gsutil cp gs://goog-dataproc-initialization-actions-${REGION}/rapids/rapids.sh gs://$GCS_BUCKET
 ```
 
 **1. Create Dataproc cluster with Dask RAPIDS.** Use the gcloud command to create a new cluster. Because of an Anaconda version conflict, script deployment on older images is slow, we recommend using Dask with Dataproc 2.0+.
@@ -24,12 +38,23 @@ At the time of writing [Dataproc only supports RAPIDS version 23.12 and earlier
 Please ensure that your setup complies with this compatibility requirement. Using newer RAPIDS versions may result in unexpected behavior or errors.
 ```
 
-```console
+```bash
 $ CLUSTER_NAME=<CLUSTER_NAME>
+```
+
+```bash
 $ DASK_RUNTIME=yarn
+```
+
+```bash
 $ RAPIDS_VERSION=23.12
+```
+
+```bash
 $ CUDA_VERSION=11.8
+```
 
+```bash
 $ gcloud dataproc clusters create $CLUSTER_NAME\
     --region $REGION\
     --image-version 2.0-ubuntu18\
@@ -42,7 +67,6 @@ $ gcloud dataproc clusters create $CLUSTER_NAME\
     --optional-components=JUPYTER\
     --metadata gpu-driver-provider=NVIDIA,dask-runtime=$DASK_RUNTIME,rapids-runtime=DASK,rapids-version=$RAPIDS_VERSION,cuda-version=$CUDA_VERSION\
     --enable-component-gateway
-
 ```
 
 [GCS_BUCKET] = name of the bucket to use.\

source/cloud/gcp/gke.md

@@ -14,16 +14,16 @@ First you'll need to have the [`gcloud` CLI tool](https://cloud.google.com/sdk/g
 
 Ensure you are logged into the `gcloud` CLI.
 
-```console
+```bash
 $ gcloud init
 ```
 
 ## Create the Kubernetes cluster
 
 Now we can launch a GPU enabled GKE cluster.
 
-```console
-gcloud container clusters create rapids-gpu-kubeflow \
+```bash
+$ gcloud container clusters create rapids-gpu-kubeflow \
   --accelerator type=nvidia-tesla-a100,count=2 --machine-type a2-highgpu-2g \
   --zone us-central1-c --release-channel stable
 ```
@@ -40,14 +40,14 @@ executable. Install gke-gcloud-auth-plugin for use with kubectl by following htt
 you will need to install the `gke-gcloud-auth-plugin` to be able to get the credentials. To do so,
 
 ```bash
-gcloud components install gke-gcloud-auth-plugin
+$ gcloud components install gke-gcloud-auth-plugin
 ```
 ````
 
 ## Get the cluster credentials
 
-```console
-gcloud container clusters get-credentials rapids-gpu-kubeflow \
+```bash
+$ gcloud container clusters get-credentials rapids-gpu-kubeflow \
     --region=us-central1-c
 ```
 

source/cloud/gcp/vertex-ai.md

@@ -35,16 +35,20 @@ You can create a new RAPIDS conda environment and register it with `ipykernel` f
 
 ```bash
 # Create a new environment
-conda create -y -n rapids \
+$ conda create -y -n rapids \
     {{ rapids_conda_channels }} \
     {{ rapids_conda_packages }} \
     ipykernel
+```
 
+```bash
 # Activate the environment
-conda activate rapids
+$ conda activate rapids
+```
 
+```bash
 # Register the environment with Jupyter
-python -m ipykernel install --prefix "${DL_ANACONDA_HOME}/envs/rapids" --name rapids --display-name rapids
+$ 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.

source/cloud/nvidia/brev.md

@@ -15,7 +15,7 @@ There are two options to get you up and running with RAPIDS in a few steps, than
 
 ### Option 1. Setting up your Brev GPU Instance
 
-1. Navigate to the [Brev console](https://brev.nvidia.com/org) and click on "Create your first instance".
+1. Navigate to the [Brev bash](https://brev.nvidia.com/org) and click on "Create your first instance".
 
 ![Screenshot of the "Create your first instance" UI](/_static/images/platforms/brev/brev1.png)
 
@@ -88,15 +88,15 @@ To create and use a Jupyter Notebook, click "Open Notebook" at the top right aft
 If you want to access your launched Brev instance(s) via Visual Studio Code or SSH using terminal, you need to install the [Brev CLI according to these instructions](https://docs.nvidia.com/brev/latest/brev-cli.html) or this code below:
 
 ```bash
-sudo bash -c "$(curl -fsSL https://raw.githubusercontent.com/brevdev/brev-cli/main/bin/install-latest.sh)" && brev login
+$ sudo bash -c "$(curl -fsSL https://raw.githubusercontent.com/brevdev/brev-cli/main/bin/install-latest.sh)" && brev login
 ```
 
 #### 2.1 Brev CLI using Visual Studio Code
 
 To connect to your Brev instance from VS Code open a new VS Code window and run:
 
 ```bash
-brev open <instance-id>
+$ brev open <instance-id>
 ```
 
 It will automatically open a new VS Code window for you to use with RAPIDS.
@@ -106,23 +106,23 @@ It will automatically open a new VS Code window for you to use with RAPIDS.
 To access your Brev instance from the terminal run:
 
 ```bash
-brev shell <instance-id>
+$ brev shell <instance-id>
 ```
 
 ##### Forwarding a Port Locally
 
 Assuming your Jupyter Notebook is running on port `8888` in your Brev environment, you can forward this port to your local machine using the following SSH command:
 
 ```bash
-ssh -L 8888:localhost:8888 <username>@<ip> -p 22
+$ ssh -L 8888:localhost:8888 <username>@<ip> -p 22
 ```
 
 This command forwards port `8888` on your local machine to port `8888` on the remote Brev environment.
 
 Or for port `2222` (default port).
 
 ```bash
-ssh <username>@<ip> -p 2222
+$ ssh <username>@<ip> -p 2222
 ```
 
 Replace `username` with your username and `ip` with the ip listed if it's different.
@@ -166,6 +166,9 @@ print(gdf)
 - Please note: Git is not preinstalled in the RAPIDS container, but can be installed into the container when it is running using
 
 ```bash
-apt update
-apt install git -y
+$ apt update
+```
+
+```bash
+$ apt install git -y
 ```

source/conf.py

@@ -99,6 +99,8 @@
 
 copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: "
 copybutton_prompt_is_regexp = True
+copybutton_line_continuation_character = "\\"
+
 
 suppress_warnings = ["myst.header", "myst.nested_header"]
 

source/guides/caching-docker-images.md

@@ -45,7 +45,7 @@ spec:
 
 You can create this Daemonset with `kubectl`.
 
-```console
+```bash
 $ kubectl apply -f caching-daemonset.yaml
 ```
 

source/guides/colocate-workers.md

@@ -8,15 +8,15 @@ First you'll need to have the [`gcloud` CLI tool](https://cloud.google.com/sdk/g
 
 Ensure you are logged into the `gcloud` CLI.
 
-```console
+```bash
 $ gcloud init
 ```
 
 ## Create the Kubernetes cluster
 
 Now we can launch a GPU enabled GKE cluster.
 
-```console
+```bash
 $ gcloud container clusters create rapids-gpu \
   --accelerator type=nvidia-tesla-a100,count=2 --machine-type a2-highgpu-2g \
   --zone us-central1-c --release-channel stable
@@ -163,7 +163,7 @@ spec:
 
 You can create this cluster with `kubectl`.
 
-```console
+```bash
 $ kubectl apply -f rapids-dask-cluster.yaml
 ```