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

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docs/source/serving/run_on_sky.rst
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@ -5,9 +5,9 @@ Deploying and scaling up with SkyPilot
.. raw:: html .. raw:: html
<p align="center"> <p align="center">
<img src="https://imgur.com/yxtzPEu.png" alt="vLLM"/> <img src="https://imgur.com/yxtzPEu.png" alt="vLLM"/>
</p> </p>
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>`__. 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>`__.
@ -21,8 +21,8 @@ Prerequisites
.. code-block:: console .. code-block:: console
pip install skypilot-nightly pip install skypilot-nightly
sky check sky check
Run on a single instance Run on a single instance
@ -32,64 +32,64 @@ See the vLLM SkyPilot YAML for serving, `serving.yaml <https://github.com/skypil
.. code-block:: yaml .. code-block:: yaml
resources: resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model. accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True use_spot: True
disk_size: 512 # Ensure model checkpoints can fit. disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best disk_tier: best
ports: 8081 # Expose to internet traffic. ports: 8081 # Expose to internet traffic.
envs: envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct 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. HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: | setup: |
conda create -n vllm python=3.10 -y conda create -n vllm python=3.10 -y
conda activate vllm conda activate vllm
pip install vllm==0.4.0.post1 pip install vllm==0.4.0.post1
# Install Gradio for web UI. # Install Gradio for web UI.
pip install gradio openai pip install gradio openai
pip install flash-attn==2.5.7 pip install flash-attn==2.5.7
run: | run: |
conda activate vllm conda activate vllm
echo 'Starting vllm api server...' echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \ python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \ --port 8081 \
--model $MODEL_NAME \ --model $MODEL_NAME \
--trust-remote-code \ --trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \ --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...' echo 'Waiting for vllm api server to start...'
while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
echo 'Starting gradio server...' echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true git clone https://github.com/vllm-project/vllm.git || true
python vllm/examples/gradio_openai_chatbot_webserver.py \ python vllm/examples/gradio_openai_chatbot_webserver.py \
-m $MODEL_NAME \ -m $MODEL_NAME \
--port 8811 \ --port 8811 \
--model-url http://localhost:8081/v1 \ --model-url http://localhost:8081/v1 \
--stop-token-ids 128009,128001 --stop-token-ids 128009,128001
Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...): Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):
.. code-block:: console .. code-block:: console
HF_TOKEN="your-huggingface-token" sky launch serving.yaml --env HF_TOKEN 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. 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.
.. code-block:: console .. code-block:: console
(task, pid=7431) Running on public URL: https://<gradio-hash>.gradio.live (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: **Optional**: Serve the 70B model instead of the default 8B and use more GPU:
.. code-block:: console .. code-block:: console
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 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 Scale up to multiple replicas
@ -99,151 +99,212 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
.. code-block:: yaml .. code-block:: yaml
service: service:
replicas: 2 replicas: 2
# An actual request for readiness probe. # An actual request for readiness probe.
readiness_probe: readiness_probe:
path: /v1/chat/completions path: /v1/chat/completions
post_data: post_data:
model: $MODEL_NAME model: $MODEL_NAME
messages: messages:
- role: user - role: user
content: Hello! What is your name? content: Hello! What is your name?
max_tokens: 1 max_tokens: 1
.. raw:: html .. raw:: html
<details> <details>
<summary>Click to see the full recipe YAML</summary> <summary>Click to see the full recipe YAML</summary>
.. code-block:: yaml .. code-block:: yaml
service: service:
replicas: 2 replicas: 2
# An actual request for readiness probe. # An actual request for readiness probe.
readiness_probe: readiness_probe:
path: /v1/chat/completions path: /v1/chat/completions
post_data: post_data:
model: $MODEL_NAME model: $MODEL_NAME
messages: messages:
- role: user - role: user
content: Hello! What is your name? content: Hello! What is your name?
max_tokens: 1 max_tokens: 1
resources: resources:
accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model. accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
use_spot: True use_spot: True
disk_size: 512 # Ensure model checkpoints can fit. disk_size: 512 # Ensure model checkpoints can fit.
disk_tier: best disk_tier: best
ports: 8081 # Expose to internet traffic. ports: 8081 # Expose to internet traffic.
envs: envs:
MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct 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. HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
setup: | setup: |
conda create -n vllm python=3.10 -y conda create -n vllm python=3.10 -y
conda activate vllm conda activate vllm
pip install vllm==0.4.0.post1 pip install vllm==0.4.0.post1
# Install Gradio for web UI. # Install Gradio for web UI.
pip install gradio openai pip install gradio openai
pip install flash-attn==2.5.7 pip install flash-attn==2.5.7
run: | run: |
conda activate vllm conda activate vllm
echo 'Starting vllm api server...' echo 'Starting vllm api server...'
python -u -m vllm.entrypoints.openai.api_server \ python -u -m vllm.entrypoints.openai.api_server \
--port 8081 \ --port 8081 \
--model $MODEL_NAME \ --model $MODEL_NAME \
--trust-remote-code \ --trust-remote-code \
--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \ --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 $MODEL_NAME \
--port 8811 \
--model-url http://localhost:8081/v1 \
--stop-token-ids 128009,128001
.. raw:: html .. raw:: html
</details> </details>
Start the serving the Llama-3 8B model on multiple replicas: Start the serving the Llama-3 8B model on multiple replicas:
.. code-block:: console .. code-block:: console
HF_TOKEN="your-huggingface-token" sky serve up -n vllm serving.yaml --env HF_TOKEN HF_TOKEN="your-huggingface-token" sky serve up -n vllm serving.yaml --env HF_TOKEN
Wait until the service is ready: Wait until the service is ready:
.. code-block:: console .. code-block:: console
watch -n10 sky serve status vllm watch -n10 sky serve status vllm
.. raw:: html .. raw:: html
<details> <details>
<summary>Example outputs:</summary> <summary>Example outputs:</summary>
.. code-block:: console .. code-block:: console
Services Services
NAME VERSION UPTIME STATUS REPLICAS ENDPOINT NAME VERSION UPTIME STATUS REPLICAS ENDPOINT
vllm 1 35s READY 2/2 xx.yy.zz.100:30001 vllm 1 35s READY 2/2 xx.yy.zz.100:30001
Service Replicas Service Replicas
SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION
vllm 1 1 xx.yy.zz.121 18 mins ago 1x GCP({'L4': 1}) READY us-east4 vllm 1 1 xx.yy.zz.121 18 mins ago 1x GCP([Spot]{'L4': 1}) READY us-east4
vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP({'L4': 1}) READY us-east4 vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP([Spot]{'L4': 1}) READY us-east4
.. raw:: html .. raw:: html
</details> </details>
After the service is READY, you can find a single endpoint for the service and access the service with the endpoint: After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
.. code-block:: console .. code-block:: console
ENDPOINT=$(sky serve status --endpoint 8081 vllm) ENDPOINT=$(sky serve status --endpoint 8081 vllm)
curl -L http://$ENDPOINT/v1/chat/completions \ curl -L http://$ENDPOINT/v1/chat/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "meta-llama/Meta-Llama-3-8B-Instruct", "model": "meta-llama/Meta-Llama-3-8B-Instruct",
"messages": [ "messages": [
{ {
"role": "system", "role": "system",
"content": "You are a helpful assistant." "content": "You are a helpful assistant."
}, },
{ {
"role": "user", "role": "user",
"content": "Who are you?" "content": "Who are you?"
} }
], ],
"stop_token_ids": [128009, 128001] "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 .. code-block:: yaml
services: service:
replica_policy: replica_policy:
min_replicas: 0 min_replicas: 2
max_replicas: 3 max_replicas: 4
target_qps_per_replica: 2 target_qps_per_replica: 2
This will scale the service up to when the QPS exceeds 2 for each replica. 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 **Optional**: Connect a GUI to the endpoint
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@ -253,58 +314,53 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
.. raw:: html .. raw:: html
<details> <details>
<summary>Click to see the full GUI YAML</summary> <summary>Click to see the full GUI YAML</summary>
.. code-block:: yaml .. code-block:: yaml
envs: 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. ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
resources: resources:
cpus: 2 cpus: 2
setup: | setup: |
conda activate vllm conda create -n vllm python=3.10 -y
if [ $? -ne 0 ]; then conda activate vllm
conda create -n vllm python=3.10 -y
conda activate vllm
fi
# Install Gradio for web UI. # Install Gradio for web UI.
pip install gradio openai pip install gradio openai
run: | run: |
conda activate vllm conda activate vllm
export PATH=$PATH:/sbin export PATH=$PATH:/sbin
WORKER_IP=$(hostname -I | cut -d' ' -f1)
CONTROLLER_PORT=21001 echo 'Starting gradio server...'
WORKER_PORT=21002 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
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
.. raw:: html .. raw:: html
</details> </details>
1. Start the chat web UI: 1. Start the chat web UI:
.. code-block:: console .. code-block:: console
sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm) sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm)
2. Then, we can access the GUI at the returned gradio link: 2. Then, we can access the GUI at the returned gradio link:
.. code-block:: console .. code-block:: console
| INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live | INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live