mirror of https://github.com/vllm-project/vllm
[Doc] Update the SkyPilot doc with serving and Llama-3 (#4276)
This commit is contained in:
Zhanghao Wu
GitHub
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ceaf4ed003
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@ -1,7 +1,7 @@
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.. _on_cloud:
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Running on clouds with SkyPilot
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===============================
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Deploying and scaling up with SkyPilot
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================================================
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.. raw:: html
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@ -9,51 +9,75 @@ Running on clouds with SkyPilot
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<img src="https://imgur.com/yxtzPEu.png" alt="vLLM"/>
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</p>
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vLLM can be run on the cloud to scale to multiple GPUs with `SkyPilot <https://github.com/skypilot-org/skypilot>`__, an open-source framework for running LLMs on any cloud.
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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>`__.
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To install SkyPilot and setup your cloud credentials, run:
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Prerequisites
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-------------
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- Go to the `HuggingFace model page <https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct>`__ and request access to the model :code:`meta-llama/Meta-Llama-3-8B-Instruct`.
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- Check that you have installed SkyPilot (`docs <https://skypilot.readthedocs.io/en/latest/getting-started/installation.html>`__).
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- Check that :code:`sky check` shows clouds or Kubernetes are enabled.
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.. code-block:: console
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$ pip install skypilot
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$ sky check
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pip install skypilot-nightly
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sky check
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Run on a single instance
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------------------------
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See the vLLM SkyPilot YAML for serving, `serving.yaml <https://github.com/skypilot-org/skypilot/blob/master/llm/vllm/serve.yaml>`__.
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.. code-block:: yaml
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resources:
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accelerators: A100
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accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
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use_spot: True
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disk_size: 512 # Ensure model checkpoints can fit.
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disk_tier: best
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ports: 8081 # Expose to internet traffic.
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envs:
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MODEL_NAME: decapoda-research/llama-13b-hf
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TOKENIZER: hf-internal-testing/llama-tokenizer
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MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
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HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
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setup: |
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conda create -n vllm python=3.9 -y
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conda create -n vllm python=3.10 -y
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conda activate vllm
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git clone https://github.com/vllm-project/vllm.git
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cd vllm
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pip install .
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pip install gradio
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pip install vllm==0.4.0.post1
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# Install Gradio for web UI.
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pip install gradio openai
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pip install flash-attn==2.5.7
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run: |
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conda activate vllm
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echo 'Starting vllm api server...'
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python -u -m vllm.entrypoints.api_server \
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--model $MODEL_NAME \
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--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
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--tokenizer $TOKENIZER 2>&1 | tee api_server.log &
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python -u -m vllm.entrypoints.openai.api_server \
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--port 8081 \
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--model $MODEL_NAME \
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--trust-remote-code \
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--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
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2>&1 | tee api_server.log &
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echo 'Waiting for vllm api server to start...'
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while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
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echo 'Starting gradio server...'
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python vllm/examples/gradio_webserver.py
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Start the serving the LLaMA-13B model on an A100 GPU:
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echo 'Starting gradio server...'
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git clone https://github.com/vllm-project/vllm.git || true
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python vllm/examples/gradio_openai_chatbot_webserver.py \
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-m $MODEL_NAME \
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--port 8811 \
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--model-url http://localhost:8081/v1 \
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--stop-token-ids 128009,128001
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Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):
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.. code-block:: console
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$ sky launch serving.yaml
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HF_TOKEN="your-huggingface-token" sky launch serving.yaml --env HF_TOKEN
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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.
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@ -61,9 +85,226 @@ Check the output of the command. There will be a shareable gradio link (like the
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(task, pid=7431) Running on public URL: https://<gradio-hash>.gradio.live
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**Optional**: Serve the 65B model instead of the default 13B and use more GPU:
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**Optional**: Serve the 70B model instead of the default 8B and use more GPU:
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.. code-block:: console
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sky launch -c vllm-serve-new -s serve.yaml --gpus A100:8 --env MODEL_NAME=decapoda-research/llama-65b-hf
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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
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Scale up to multiple replicas
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-----------------------------
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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.
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.. code-block:: yaml
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service:
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replicas: 2
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# An actual request for readiness probe.
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readiness_probe:
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path: /v1/chat/completions
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post_data:
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model: $MODEL_NAME
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messages:
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- role: user
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content: Hello! What is your name?
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max_tokens: 1
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.. raw:: html
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<details>
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<summary>Click to see the full recipe YAML</summary>
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.. code-block:: yaml
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service:
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replicas: 2
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# An actual request for readiness probe.
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readiness_probe:
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path: /v1/chat/completions
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post_data:
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model: $MODEL_NAME
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messages:
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- role: user
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content: Hello! What is your name?
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max_tokens: 1
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resources:
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accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
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use_spot: True
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disk_size: 512 # Ensure model checkpoints can fit.
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disk_tier: best
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ports: 8081 # Expose to internet traffic.
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envs:
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MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
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HF_TOKEN: <your-huggingface-token> # Change to your own huggingface token, or use --env to pass.
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setup: |
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conda create -n vllm python=3.10 -y
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conda activate vllm
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pip install vllm==0.4.0.post1
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# Install Gradio for web UI.
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pip install gradio openai
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pip install flash-attn==2.5.7
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run: |
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conda activate vllm
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echo 'Starting vllm api server...'
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python -u -m vllm.entrypoints.openai.api_server \
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--port 8081 \
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--model $MODEL_NAME \
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--trust-remote-code \
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--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
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2>&1 | tee api_server.log &
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echo 'Waiting for vllm api server to start...'
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while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
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echo 'Starting gradio server...'
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git clone https://github.com/vllm-project/vllm.git || true
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python vllm/examples/gradio_openai_chatbot_webserver.py \
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-m $MODEL_NAME \
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--port 8811 \
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--model-url http://localhost:8081/v1 \
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--stop-token-ids 128009,128001
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.. raw:: html
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</details>
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Start the serving the Llama-3 8B model on multiple replicas:
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.. code-block:: console
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HF_TOKEN="your-huggingface-token" sky serve up -n vllm serving.yaml --env HF_TOKEN
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Wait until the service is ready:
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.. code-block:: console
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watch -n10 sky serve status vllm
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.. raw:: html
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<details>
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<summary>Example outputs:</summary>
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.. code-block:: console
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Services
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NAME VERSION UPTIME STATUS REPLICAS ENDPOINT
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vllm 1 35s READY 2/2 xx.yy.zz.100:30001
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Service Replicas
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SERVICE_NAME ID VERSION IP LAUNCHED RESOURCES STATUS REGION
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vllm 1 1 xx.yy.zz.121 18 mins ago 1x GCP({'L4': 1}) READY us-east4
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vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP({'L4': 1}) READY us-east4
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.. raw:: html
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</details>
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After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
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.. code-block:: console
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ENDPOINT=$(sky serve status --endpoint 8081 vllm)
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curl -L http://$ENDPOINT/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "meta-llama/Meta-Llama-3-8B-Instruct",
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant."
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},
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{
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"role": "user",
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"content": "Who are you?"
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}
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],
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"stop_token_ids": [128009, 128001]
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}'
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To enable autoscaling, you could specify additional configs in `services`:
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.. code-block:: yaml
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services:
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replica_policy:
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min_replicas: 0
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max_replicas: 3
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target_qps_per_replica: 2
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This will scale the service up to when the QPS exceeds 2 for each replica.
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**Optional**: Connect a GUI to the endpoint
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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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.
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.. raw:: html
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<details>
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<summary>Click to see the full GUI YAML</summary>
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.. code-block:: yaml
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envs:
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MODEL_NAME: meta-llama/Meta-Llama-3-70B-Instruct
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ENDPOINT: x.x.x.x:3031 # Address of the API server running vllm.
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resources:
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cpus: 2
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setup: |
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conda activate vllm
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if [ $? -ne 0 ]; then
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conda create -n vllm python=3.10 -y
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conda activate vllm
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fi
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# Install Gradio for web UI.
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pip install gradio openai
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run: |
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conda activate vllm
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export PATH=$PATH:/sbin
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WORKER_IP=$(hostname -I | cut -d' ' -f1)
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CONTROLLER_PORT=21001
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WORKER_PORT=21002
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echo 'Starting gradio server...'
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git clone https://github.com/vllm-project/vllm.git || true
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python vllm/examples/gradio_openai_chatbot_webserver.py \
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-m $MODEL_NAME \
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--port 8811 \
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--model-url http://$ENDPOINT/v1 \
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--stop-token-ids 128009,128001 | tee ~/gradio.log
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.. raw:: html
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</details>
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1. Start the chat web UI:
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.. code-block:: console
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sky launch -c gui ./gui.yaml --env ENDPOINT=$(sky serve status --endpoint vllm)
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2. Then, we can access the GUI at the returned gradio link:
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.. code-block:: console
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| INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live
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