Deploying and scaling up with SkyPilot

Deploying and scaling up with 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.

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 &
    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

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.

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_tokens: 1

Click to see the full recipe 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_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 &
    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

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({'L4': 1})  READY   us-east4
vllm          2   1        xx.yy.zz.245  18 mins ago  1x GCP({'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:

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 specify additional configs in services:

services:
    replica_policy:
        min_replicas: 0
        max_replicas: 3
    target_qps_per_replica: 2

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

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.

Click to see the full GUI YAML

envs:
    MODEL_NAME: meta-llama/Meta-Llama-3-70B-Instruct
    ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
resources:
    cpus: 2
setup: |
    conda activate vllm
    if [ $? -ne 0 ]; then
        conda create -n vllm python=3.10 -y
        conda activate vllm
    fi
    # Install Gradio for web UI.
    pip install gradio openai
run: |
    conda activate vllm
    export PATH=$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
    python vllm/examples/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