vllm/docs/source/serving/deploying_with_cerebrium.md

(deploying-with-cerebrium)=

# Deploying with Cerebrium

```{raw} html
<p align="center">
    <img src="https://i.ibb.co/hHcScTT/Screenshot-2024-06-13-at-10-14-54.png" alt="vLLM_plus_cerebrium"/>
</p>
```

vLLM can be run on a cloud based GPU machine with [Cerebrium](https://www.cerebrium.ai/), a serverless AI infrastructure platform that makes it easier for companies to build and deploy AI based applications.

To install the Cerebrium client, run:

```console
$ pip install cerebrium
$ cerebrium login
```

Next, create your Cerebrium project, run:

```console
$ cerebrium init vllm-project
```

Next, to install the required packages, add the following to your cerebrium.toml:

```toml
[cerebrium.deployment]
docker_base_image_url = "nvidia/cuda:12.1.1-runtime-ubuntu22.04"

[cerebrium.dependencies.pip]
vllm = "latest"
```

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`:

```python
from vllm import LLM, SamplingParams

llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.1")

def run(prompts: list[str], temperature: float = 0.8, top_p: float = 0.95):

    sampling_params = SamplingParams(temperature=temperature, top_p=top_p)
    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    results = []
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        results.append({"prompt": prompt, "generated_text": generated_text})

    return {"results": results}
```

Then, run the following code to deploy it to the cloud:

```console
$ 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`)

```python
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:

```python
{
    "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!