vLLM¶
This example shows how to deploy Llama 3.1 8B with dstack using vLLM .
Prerequisites
Once dstack is installed, go ahead clone the repo, and run dstack init.
$ git clone https://github.com/dstackai/dstack
$ cd dstack
$ dstack init
Deployment¶
Here's an example of a service that deploys Llama 3.1 8B using vLLM.
type: service
name: llama31
python: "3.11"
env:
- HF_TOKEN
- MODEL_ID=meta-llama/Meta-Llama-3.1-8B-Instruct
- MAX_MODEL_LEN=4096
commands:
- pip install vllm
- vllm serve $MODEL_ID
--max-model-len $MAX_MODEL_LEN
--tensor-parallel-size $DSTACK_GPUS_NUM
port: 8000
# Register the model
model: meta-llama/Meta-Llama-3.1-8B-Instruct
# Uncomment to leverage spot instances
#spot_policy: auto
# Uncomment to cache downloaded models
#volumes:
# - /root/.cache/huggingface/hub:/root/.cache/huggingface/hub
resources:
gpu: 24GB
# Uncomment if using multiple GPUs
#shm_size: 24GB
Running a configuration¶
To run a configuration, use the dstack apply command.
$ dstack apply -f examples/deployment/vllm/.dstack.yml
# BACKEND REGION RESOURCES SPOT PRICE
1 runpod CA-MTL-1 18xCPU, 100GB, A5000:24GB yes $0.12
2 runpod EU-SE-1 18xCPU, 100GB, A5000:24GB yes $0.12
3 gcp us-west4 27xCPU, 150GB, A5000:24GB:2 yes $0.23
Submit a new run? [y/n]: y
Provisioning...
---> 100%
If no gateway is created, the model will be available via the OpenAI-compatible endpoint
at <dstack server URL>/proxy/models/<project name>/.
$ curl http://127.0.0.1:3000/proxy/models/main/chat/completions \
-X POST \
-H 'Authorization: Bearer <dstack token>' \
-H 'Content-Type: application/json' \
-d '{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What is Deep Learning?"
}
],
"max_tokens": 128
}'
When a gateway is configured, the OpenAI-compatible endpoint
is available at https://gateway.<gateway domain>/.
Source code¶
The source-code of this example can be found in
examples/deployment/vllm .