SkyPilot¶

vLLM can be run and scaled to multiple service replicas on clouds and Kubernetes with SkyPilot, an open-source framework for running LLMs on any cloud. More examples for various open models, such as Llama-3, Mixtral, etc, can be found in SkyPilot AI gallery.

Prerequisites¶

Go to the HuggingFace model page and request access to the model meta-llama/Meta-Llama-3-8B-Instruct.
Check that you have installed SkyPilot (docs).
Check that sky check shows clouds or Kubernetes are enabled.

pip install skypilot-nightly
sky check

Run on a single instance¶

See the vLLM SkyPilot YAML for serving, serving.yaml.

Yaml

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:
  PYTHONUNBUFFERED: 1
  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...'
  vllm serve $MODEL_NAME \
    --port 8081 \
    --trust-remote-code \
    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
    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/online_serving/gradio_openai_chatbot_webserver.py \
    -m $MODEL_NAME \
    --port 8811 \
    --model-url http://localhost:8081/v1 \
    --stop-token-ids 128009,128001

Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):

HF_TOKEN="your-huggingface-token" sky launch serving.yaml --env HF_TOKEN

Check the output of the command. There will be a shareable gradio link (like the last line of the following). Open it in your browser to use the LLaMA model to do the text completion.

(task, pid=7431) Running on public URL: https://<gradio-hash>.gradio.live

Optional: Serve the 70B model instead of the default 8B and use more GPU:

HF_TOKEN="your-huggingface-token" \
  sky launch serving.yaml \
  --gpus A100:8 \
  --env HF_TOKEN \
  --env MODEL_NAME=meta-llama/Meta-Llama-3-70B-Instruct

Scale up to multiple replicas¶

SkyPilot can scale up the service to multiple service replicas with built-in autoscaling, load-balancing and fault-tolerance. You can do it by adding a services section to the YAML file.

Yaml

service:
  replicas: 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_completion_tokens: 1

Yaml

service:
  replicas: 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_completion_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:
  PYTHONUNBUFFERED: 1
  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...'
  vllm serve $MODEL_NAME \
    --port 8081 \
    --trust-remote-code \
    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
    2>&1 | tee api_server.log

Start the serving the Llama-3 8B model on multiple replicas:

HF_TOKEN="your-huggingface-token" \
  sky serve up -n vllm serving.yaml \
  --env HF_TOKEN

Wait until the service is ready:

watch -n10 sky serve status vllm

Example outputs:

Services
NAME  VERSION  UPTIME  STATUS  REPLICAS  ENDPOINT
vllm  1        35s     READY   2/2       xx.yy.zz.100:30001

Service Replicas
SERVICE_NAME  ID  VERSION  IP            LAUNCHED     RESOURCES                STATUS  REGION
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([Spot]{'L4': 1})  READY   us-east4

After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:

Commands

ENDPOINT=$(sky serve status --endpoint 8081 vllm)
curl -L http://$ENDPOINT/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
    "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Who are you?"
    }
    ],
    "stop_token_ids": [128009,  128001]
  }'

To enable autoscaling, you could replace the replicas with the following configs in service:

service:
  replica_policy:
    min_replicas: 2
    max_replicas: 4
    target_qps_per_replica: 2

This will scale the service up to when the QPS exceeds 2 for each replica.

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_completion_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:
  PYTHONUNBUFFERED: 1
  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...'
  vllm serve $MODEL_NAME \
    --port 8081 \
    --trust-remote-code \
    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
    2>&1 | tee api_server.log

To update the service with the new config:

HF_TOKEN="your-huggingface-token" sky serve update vllm serving.yaml --env HF_TOKEN

To stop the service:

sky serve down vllm

Optional: Connect a GUI to the endpoint¶

It is also possible to access the Llama-3 service with a separate GUI frontend, so the user requests send to the GUI will be load-balanced across replicas.

Yaml

envs:
  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 create -n vllm python=3.10 -y
  conda activate vllm

  # Install Gradio for web UI.
  pip install gradio openai

run: |
  conda activate vllm
  export PATH=$PATH:/sbin

  echo 'Starting gradio server...'
  git clone https://github.com/vllm-project/vllm.git || true
  python vllm/examples/online_serving/gradio_openai_chatbot_webserver.py \
    -m $MODEL_NAME \
    --port 8811 \
    --model-url http://$ENDPOINT/v1 \
    --stop-token-ids 128009,128001 | tee ~/gradio.log

Start the chat web UI:

sky launch \
  -c gui ./gui.yaml \
  --env ENDPOINT=$(sky serve status --endpoint vllm)

Then, we can access the GUI at the returned gradio link:

| INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live