[Doc] Update SkyPilot doc for wrong indents and instructions for update service (#4283)

docs/source/serving/run_on_sky.rst

@@ -159,18 +159,7 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
       --model $MODEL_NAME \
       --trust-remote-code \
       --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
-            2>&1 | tee api_server.log &
+      2>&1 | tee api_server.log
-        
-        echo 'Waiting for vllm api server to start...'
-        while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
-
-        echo 'Starting gradio server...'
-        git clone https://github.com/vllm-project/vllm.git || true
-        python vllm/examples/gradio_openai_chatbot_webserver.py \
-            -m $MODEL_NAME \
-            --port 8811 \
-            --model-url http://localhost:8081/v1 \
-            --stop-token-ids 128009,128001
 
 .. raw:: html
 
@@ -203,8 +192,8 @@ Wait until the service is ready:
 
   Service Replicas
   SERVICE_NAME  ID  VERSION  IP            LAUNCHED     RESOURCES                STATUS  REGION
-    vllm          1   1        xx.yy.zz.121  18 mins ago  1x GCP({'L4': 1})  READY   us-east4
+  vllm          1   1        xx.yy.zz.121  18 mins ago  1x GCP([Spot]{'L4': 1})  READY   us-east4
-    vllm          2   1        xx.yy.zz.245  18 mins ago  1x GCP({'L4': 1})  READY   us-east4
+  vllm          2   1        xx.yy.zz.245  18 mins ago  1x GCP([Spot]{'L4': 1})  READY   us-east4
 
 .. raw:: html
   
@@ -232,19 +221,91 @@ After the service is READY, you can find a single endpoint for the service and a
       "stop_token_ids": [128009,  128001]
     }'
 
-To enable autoscaling, you could specify additional configs in `services`:
+To enable autoscaling, you could replace the `replicas` with the following configs in `service`:
 
 .. code-block:: yaml
 
-    services:
+  service:
     replica_policy:
-            min_replicas: 0
+      min_replicas: 2
-            max_replicas: 3
+      max_replicas: 4
       target_qps_per_replica: 2
 
 This will scale the service up to when the QPS exceeds 2 for each replica.
 
     
+.. raw:: html
+
+  <details>
+  <summary>Click to see the full recipe YAML</summary>
+
+
+.. code-block:: yaml
+
+  service:
+    replica_policy:
+      min_replicas: 2
+      max_replicas: 4
+      target_qps_per_replica: 2
+    # An actual request for readiness probe.
+    readiness_probe:
+      path: /v1/chat/completions
+      post_data:
+        model: $MODEL_NAME
+        messages:
+          - role: user
+            content: Hello! What is your name?
+        max_tokens: 1
+
+  resources:
+    accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
+    use_spot: True
+    disk_size: 512  # Ensure model checkpoints can fit.
+    disk_tier: best
+    ports: 8081  # Expose to internet traffic.
+
+  envs:
+    MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
+    HF_TOKEN: <your-huggingface-token>  # Change to your own huggingface token, or use --env to pass.
+
+  setup: |
+    conda create -n vllm python=3.10 -y
+    conda activate vllm
+
+    pip install vllm==0.4.0.post1
+    # Install Gradio for web UI.
+    pip install gradio openai
+    pip install flash-attn==2.5.7
+
+  run: |
+    conda activate vllm
+    echo 'Starting vllm api server...'
+    python -u -m vllm.entrypoints.openai.api_server \
+      --port 8081 \
+      --model $MODEL_NAME \
+      --trust-remote-code \
+      --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+      2>&1 | tee api_server.log
+
+
+.. raw:: html
+  
+  </details>
+
+To update the service with the new config:
+
+.. code-block:: console
+
+  HF_TOKEN="your-huggingface-token" sky serve update vllm serving.yaml --env HF_TOKEN
+
+
+To stop the service:
+
+.. code-block:: console
+
+  sky serve down vllm
+
+
 **Optional**: Connect a GUI to the endpoint
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -259,18 +320,15 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
 .. code-block:: yaml
 
   envs:
-        MODEL_NAME: meta-llama/Meta-Llama-3-70B-Instruct
+    MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
     ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm. 
 
   resources:
     cpus: 2
 
   setup: |
-        conda activate vllm
-        if [ $? -ne 0 ]; then
     conda create -n vllm python=3.10 -y
     conda activate vllm
-        fi
 
     # Install Gradio for web UI.
     pip install gradio openai
@@ -278,9 +336,6 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
   run: |
     conda activate vllm
     export PATH=$PATH:/sbin
-        WORKER_IP=$(hostname -I | cut -d' ' -f1)
-        CONTROLLER_PORT=21001
-        WORKER_PORT=21002
 
     echo 'Starting gradio server...'
     git clone https://github.com/vllm-project/vllm.git || true
@@ -290,6 +345,7 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
       --model-url http://$ENDPOINT/v1 \
       --stop-token-ids 128009,128001 | tee ~/gradio.log
 
+
 .. raw:: html
   
   </details>