[Doc] Added cerebrium as Integration option (vllm-project#5553)

docs/source/serving/deploying_with_cerebrium.rst

@@ -0,0 +1,109 @@
+.. _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:
+
+.. code-block:: console
+
+    $ pip install cerebrium
+    $ cerebrium login
+
+Next, create your Cerebrium project, run:
+
+.. code-block:: console
+
+    $ cerebrium init vllm-project
+
+Next, to install the required packages, add the following to your cerebrium.toml:
+
+.. code-block:: toml
+
+    [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`:
+
+.. code-block:: 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
+
+.. code-block:: 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)
+
+.. code-block:: 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:
+
+.. code-block:: 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!
+

docs/source/serving/integrations.rst

@@ -8,6 +8,7 @@ Integrations
    deploying_with_kserve
    deploying_with_triton
    deploying_with_bentoml
+   deploying_with_cerebrium
    deploying_with_lws
    deploying_with_dstack
    serving_with_langchain