# SkyPilot
vLLM can be **run and scaled to multiple service replicas on clouds and Kubernetes** with [SkyPilot](https://github.com/skypilot-org/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](https://skypilot.readthedocs.io/en/latest/gallery/index.html).
## Prerequisites
- Go to the [HuggingFace model page](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and request access to the model `meta-llama/Meta-Llama-3-8B-Instruct`.
- Check that you have installed SkyPilot ([docs](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html)).
- Check that `sky check` shows clouds or Kubernetes are enabled.
```bash
pip install skypilot-nightly
sky check
```
## Run on a single instance
See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm/serve.yaml).
??? code "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:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_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/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, ...):
```bash
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.
```console
(task, pid=7431) Running on public URL: https://.gradio.live
```
**Optional**: Serve the 70B model instead of the default 8B and use more GPU:
```bash
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.
??? code "Yaml"
```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
```
??? code "Yaml"
```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:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_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 $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
Start the serving the Llama-3 8B model on multiple replicas:
```bash
HF_TOKEN="your-huggingface-token" \
sky serve up -n vllm serving.yaml \
--env HF_TOKEN
```
Wait until the service is ready:
```bash
watch -n10 sky serve status vllm
```
Example outputs:
```console
Services
NAME VERSION UPTIME STATUS REPLICAS ENDPOINT
vllm 1 35s READY 2/2 xx.yy.zz.100:30001
Service Replicas
SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION
vllm 1 1 xx.yy.zz.121 18 mins ago 1x GCP([Spot]{'L4': 1}) READY us-east4
vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP([Spot]{'L4': 1}) READY us-east4
```
After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
??? console "Commands"
```bash
ENDPOINT=$(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`:
```yaml
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.
??? code "Yaml"
```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:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
HF_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 $SKYPILOT_NUM_GPUS_PER_NODE \
2>&1 | tee api_server.log
```
To update the service with the new config:
```bash
HF_TOKEN="your-huggingface-token" sky serve update vllm serving.yaml --env HF_TOKEN
```
To stop the service:
```bash
sky serve down vllm
```
### **Optional**: Connect a GUI to the endpoint
It is also possible to access the Llama-3 service with a separate GUI frontend, so the user requests send to the GUI will be load-balanced across replicas.
??? code "Yaml"
```yaml
envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
resources:
cpus: 2
setup: |
conda create -n vllm python=3.10 -y
conda activate vllm
# Install Gradio for web UI.
pip install gradio openai
run: |
conda activate vllm
export PATH=$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
```
1. Start the chat web UI:
```bash
sky launch \
-c gui ./gui.yaml \
--env ENDPOINT=$(sky serve status --endpoint vllm)
```
2. Then, we can access the GUI at the returned gradio link:
```console
| INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live
```