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.
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.
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.
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.
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.
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.
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.
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.
In addition, you can add any additional dependencies of systems or Python libraries
that are part of your action code but not included in the base
To create your image:
Move your actions code to a folder
actionsin your project directory. Make sure to also add an empty
actions/__init__.pyfile:mkdir actions mv actions.py actions/actions.py touch actions/__init__.py # the init file indicates actions.py is a python module
rasa/rasa-sdkimage will automatically look for the actions in
If your actions have any extra dependencies, create a list of them in a file,
Create a file named
Dockerfilein 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:# Extend the official Rasa SDK image FROM rasa/rasa-sdk:2.0.0a1 # Use subdirectory as working directory WORKDIR /app # Copy any additional custom requirements, if necessary (uncomment next line) # COPY actions/requirements-actions.txt ./ # Change back to root user to install dependencies USER root # Install extra requirements for actions code, if necessary (uncomment next line) # RUN pip install -r requirements-actions.txt # Copy actions folder to working directory COPY ./actions /app/actions # By best practices, don't run the code with root user USER 1001
You can then build the image via the following command:
docker build . -t <account_username>/<repository_name>:<custom_image_tag>
<custom_image_tag> should reference how this image will be different from others. For
example, you could version or date your tags, as well as create different tags that have different code for production
and development servers. You should create a new tag any time you update your code and want to re-deploy it.
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.
This documentation assumes you are pushing your images to DockerHub. DockerHub will let you host multiple public repositories and one private repository for free. Be sure to first create an account and create a repository to store your images. You could also push images to a different Docker registry, such as Google Container Registry, Amazon Elastic Container Registry, or Azure Container Registry.
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.
How you reference the custom action image will depend on your deployment. Pick the relevant documentation for your deployment: