pipecat/examples/deployment/modal-example

Deploying Pipecat to Modal.com

Deployment example for modal.com. This example demonstrates how to deploy a FastAPI webapp to Modal with an RTVI compatible /connect endpoint that launches a Pipecat pipeline in a separate Modal container and returns a room/token for the client to join. This example also supports providing a parameter to the /connect endpoint for specifying which Pipecat pipeline to launch; openai, gemini, or vllm. The vllm pipeline points to a self-hosted OpenAI compatible LLM, using a llama model (neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16), deployed to Modal.

Running this Example

Prerequisites

Setup a Modal account and install it on your machine if you have not already, following their easy 3-steps in their Getting Started Guide

Deploy a self-serve LLM

1. Follow the Modal Guide and example for Deploying an OpenAI-compatible LLM service with vLLM.

   The TLDR, though, is to simply do the following from within this directory:

   git clone https://github.com/modal-labs/modal-examples
   cd modal-examples
   modal deploy 06_gpu_and_ml/llm-serving/vllm_inference.py
   

2. Jot down the endpoint from the previous step to use in the bot_vllm file mentioned below. It will look something like: https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run

Note: This Modal example is their initial getting started example with a Llama-3.1 model. By default, it will tear down the container after 15 minutes of inactivity and can take 5-10 minutes to re-start, during which time it is unusable. So for the purposes of just getting started and this example, we recommend visiting the /docs endpoint (https://<Modal workspace>--example-vllm-openai-compatible-serve.modal.run/docs) for your deployed llm in a browser to trigger the cold start. Then wait for the page to load, indicating its ready before trying to connect your client.

Deploy FastAPI App and Pipecat pipeline to Modal

1. Setup environment variables

cd server
cp env.example .env
# Modify .env to provide your service API Keys

Alternatively, you can configure your Modal app to use secrets

1. Update the modal_url in server/src/bot_vllm.py to point to the url produced from the self-serve llm deploy, mentioned above.

2. From within the server directory, test the app locally:

modal serve app.py

4. Deploy to production

modal deploy app.py

5. Jot down the endpoint from the previous step to use in the client's app.js file mentioned its README. It will look something like: https://<Modal workspace>--pipecat-modal-fastapi-app.modal.run

Launch and Talk to your Bots running on Modal

Option 1: Direct Link

Simply click on the url displayed after running the server or deploy step to launch an agent and be redirected to a Daily room to talk with the launched bot. This will use the OpenAI pipeline.

Option 2: Connect via an RTVI Client

Follow the instructions provided in the client folder's README for building and running a custom client that connects to your Modal endpoint. The provided client provides a dropdown for choosing which bot pipeline to run.

Navigating your llm, server, and Pipecat logs

In your Modal dashboard, you should have two Apps listed under Live Apps:

1. example-vllm-openai-compatible: This App contains the containers and logs used to run your self-hosted LLM. There will be just one App Function listed: serve. Click on this function to view logs for your LLM.
2. pipecat-modal: This App contains the containers and logs used to run your connect endpoints and Pipecat pipelines. It will list two App Functions:
   1. fastapi_app: This function is running the endpoints that your client will interact with and initiate starting a new pipeline (/, /connect, /status). Click on this function to see logs for each endpoint hit.
   2. bot_runner: This function handles launching and running a bot pipeline. Click on this function to get a list of all pipeline runs and access each run's logs.

Diagram of Deployment

Modal + Pipecat Tips

• In most other Pipecat examples, we use Popen to launch the pipeline process from the /connect endpoint. In this example, we instead use a Modal function with its own Modal image defined. This change ensures that each run of the Pipeline happens in a isolated, customizable container.
• For the FastAPI and most common Pipecat Pipeline containers, a default debian_slim CPU-only should be all that's required to run. GPU containers are needed for self-hosted services.
• To minimize cold starts of the pipeline and reduce latency for users, set min_containers=1 on the Modal Function that launches the pipeline to ensure at least one warm instance of your function is always available.
• For next steps on running a self-hosted llm and reducing latency, check out all of Modal's LLM examples.