Deploying Your Rasa Assistant

These docs are for version 1.x of Rasa Open Source.

Deploying Your Rasa Assistant

This page explains when and how to deploy an assistant built with Rasa. It will allow you to make your assistant available to users and set you up with a production-ready environment.

When to Deploy Your Assistant

The best time to deploy your assistant and make it available to test users is once it can handle the most important happy paths or is what we call a minimum viable assistant.

The recommended deployment methods described below make it easy to share your assistant with test users via the share your assistant feature in Rasa X. Then, when you’re ready to make your assistant available via one or more Messaging and Voice Channels, you can easily add them to your existing deployment set up.

Recommended Deployment Methods

The recommended way to deploy an assistant is using either the Server Quick-Install or Helm Chart options we support. Both deploy Rasa X and your assistant. They are the easiest ways to deploy your assistant, allow you to use Rasa X to view conversations and turn them into training data, and are production-ready. For more details on deployment methods see the Rasa X Installation Guide.

Server Quick-Install

The Server Quick-Install script is the easiest way to deploy Rasa X and your assistant. It installs a Kubernetes cluster on your machine with sensible defaults, getting you up and running in one command.

Default: Make sure you meet the OS Requirements, then run:

curl -s get-rasa-x.rasa.com | sudo bash

Helm Chart

For assistants that will receive a lot of user traffic, setting up a Kubernetes or Openshift deployment via our Helm charts is the best option. This provides a scalable architecture that is also straightforward to deploy. However, you can also customize the Helm charts if you have specific requirements.

Alternative Deployment Methods

Docker Compose

You can also run Rasa X in a Docker Compose setup, without the cluster environment. We have an install script for doing so, as well as manual instructions for any custom setups.

Rasa Open Source Only Deployment

It is also possible to deploy a Rasa assistant without Rasa X using Docker Compose. To do so, you can build your Rasa Assistant locally or in Docker. Then you can deploy your model in Docker Compose.

Deploying Your Action Server

Building an Action Server Image

If you build an image that includes your action code and store it in a container registry, you can run it as part of your deployment, without having to move code between servers.

Move your actions code to a folder actions in your project directory. Make sure to also add an empty actions/__init__.py file:
```
mkdir actions
mv actions.py actions/actions.py
touch actions/__init__.py
```
If your actions have any extra dependencies, create a list of them in a file, actions/requirements-actions.txt.
Create a file named Dockerfile in your project directory, in which you’ll extend the official SDK image, copy over your code, and add any custom dependencies (if necessary). For example:
```
FROM rasa/rasa-sdk:1.10.3
WORKDIR /app
COPY ./actions /app/actions
USER 1001
```

You can then build the image via the following command:

docker build . -t <account_username>/<repository_name>:<custom_image_tag>

Using your Custom Action Server Image

If you’re building this image to make it available from another server, for example a Rasa X or Rasa Enterprise deployment, you should push the image to a cloud repository.

You can push the image to DockerHub via:

docker login --username <account_username> --password <account_password>
docker push <account_username>/<repository_name>:<custom_image_tag>

To authenticate and push images to a different container registry, please refer to the documentation of your chosen container registry.