vLLM can be run on a cloud based GPU machine with Cerebrium, a serverless AI infrastructure platform that makes it easier for companies to build and deploy AI based applications.
To install the Cerebrium client, run:
pip install cerebriumcerebrium login
Next, create your Cerebrium project, run:
cerebrium init vllm-project
Next, to install the required packages, add the following to your cerebrium.toml:
Next, let us add our code to handle inference for the LLM of your choice (mistralai/Mistral-7B-Instruct-v0.1 for this example), add the following code to your main.py:
Then, run the following code to deploy it to the cloud:
cerebrium deploy
If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case /run)
curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \-H 'Content-Type: application/json' \-H 'Authorization: <JWT TOKEN>' \--data '{"prompts": [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is"]}'
You should get a response like:
{ "run_id": "52911756-3066-9ae8-bcc9-d9129d1bd262", "result": { "result": [ { "prompt": "Hello, my name is", "generated_text": " Sarah, and I'm a teacher. I teach elementary school students. One of" }, { "prompt": "The president of the United States is", "generated_text": " elected every four years. This is a democratic system.\n\n5. What" }, { "prompt": "The capital of France is", "generated_text": " Paris.\n" }, { "prompt": "The future of AI is", "generated_text": " bright, but it's important to approach it with a balanced and nuanced perspective." } ] }, "run_time_ms": 152.53663063049316}
You now have an autoscaling endpoint where you only pay for the compute you use!