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docs: Avoid bash script syntax highlighting for dark mode (#1918)

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See
https://github.com/meta-llama/llama-stack/pull/1913#issuecomment-2790153778

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
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16
docs/source/distributions/kubernetes_deployment.md
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@ -7,13 +7,13 @@ In this guide, we'll use a local [Kind](https://kind.sigs.k8s.io/) cluster and a
First, create a local Kubernetes cluster via Kind: First, create a local Kubernetes cluster via Kind:
```bash ```
kind create cluster --image kindest/node:v1.32.0 --name llama-stack-test kind create cluster --image kindest/node:v1.32.0 --name llama-stack-test
``` ```
First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model: First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
```bash ```
cat <<EOF |kubectl apply -f - cat <<EOF |kubectl apply -f -
apiVersion: v1 apiVersion: v1
kind: PersistentVolumeClaim kind: PersistentVolumeClaim
@ -39,7 +39,7 @@ data:
Next, start the vLLM server as a Kubernetes Deployment and Service: Next, start the vLLM server as a Kubernetes Deployment and Service:
```bash ```
cat <<EOF |kubectl apply -f - cat <<EOF |kubectl apply -f -
apiVersion: apps/v1 apiVersion: apps/v1
kind: Deployment kind: Deployment
@ -95,7 +95,7 @@ EOF
We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model): We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
```bash ```
$ kubectl logs -l app.kubernetes.io/name=vllm $ kubectl logs -l app.kubernetes.io/name=vllm
... ...
INFO: Started server process [1] INFO: Started server process [1]
@ -119,7 +119,7 @@ providers:
Once we have defined the run configuration for Llama Stack, we can build an image with that configuration and the server source code: Once we have defined the run configuration for Llama Stack, we can build an image with that configuration and the server source code:
```bash ```
cat >/tmp/test-vllm-llama-stack/Containerfile.llama-stack-run-k8s <<EOF cat >/tmp/test-vllm-llama-stack/Containerfile.llama-stack-run-k8s <<EOF
FROM distribution-myenv:dev FROM distribution-myenv:dev
@ -135,7 +135,7 @@ podman build -f /tmp/test-vllm-llama-stack/Containerfile.llama-stack-run-k8s -t
We can then start the Llama Stack server by deploying a Kubernetes Pod and Service: We can then start the Llama Stack server by deploying a Kubernetes Pod and Service:
```bash ```
cat <<EOF |kubectl apply -f - cat <<EOF |kubectl apply -f -
apiVersion: v1 apiVersion: v1
kind: PersistentVolumeClaim kind: PersistentVolumeClaim
@ -195,7 +195,7 @@ EOF
### Verifying the Deployment ### Verifying the Deployment
We can check that the LlamaStack server has started: We can check that the LlamaStack server has started:
```bash ```
$ kubectl logs -l app.kubernetes.io/name=llama-stack $ kubectl logs -l app.kubernetes.io/name=llama-stack
... ...
INFO: Started server process [1] INFO: Started server process [1]
@ -207,7 +207,7 @@ INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit
Finally, we forward the Kubernetes service to a local port and test some inference requests against it via the Llama Stack Client: Finally, we forward the Kubernetes service to a local port and test some inference requests against it via the Llama Stack Client:
```bash ```
kubectl port-forward service/llama-stack-service 5000:5000 kubectl port-forward service/llama-stack-service 5000:5000
llama-stack-client --endpoint http://localhost:5000 inference chat-completion --message "hello, what model are you?" llama-stack-client --endpoint http://localhost:5000 inference chat-completion --message "hello, what model are you?"
``` ```