This is unreleased documentation for Rasa Documentation Main/Unreleased version.
For the latest released documentation, see the latest version (3.x).

Version: Main/Unreleased

Building a Rasa Assistant in Docker

If you don't have a Rasa project yet, you can build one in Docker without having to install Rasa on your local machine. If you already have a model you're satisfied with, see Deploying a Rasa Assistant to learn how to deploy your model.

Installing Docker

If you're not sure if you have Docker installed, you can check by running:

docker -v
# Docker version 18.09.2, build 6247962

If Docker is installed on your machine, the output should show you your installed versions of Docker. If the command doesn't work, you'll have to install Docker. See Docker Installation for details.

Setting up your Rasa Project

Just like starting a project from scratch, you'll use the rasa init command to create a project. The only difference is that you'll be running Rasa inside a Docker container, using the image rasa/rasa. To initialize your project, run:

docker run -v $(pwd):/app rasa/rasa:3.7.0a1-full init --no-prompt

What does this command mean?

  • -v $(pwd):/app mounts your current working directory to the working directory in the Docker container. This means that files you create on your computer will be visible inside the container, and files created in the container will get synced back to your computer.
  • rasa/rasa is the name of the docker image to run. '3.7.0a1-full' is the name of the tag, which specifies the version and dependencies.
  • the Docker image has the rasa command as its entrypoint, which means you don't have to type rasa init, just init is enough.

Running this command will produce a lot of output. What happens is:

  • a Rasa project is created

  • an initial model is trained using the project's training data.

To check that the command completed correctly, look at the contents of your working directory:

ls -1

The initial project files should all be there, as well as a models directory that contains your trained model.


If you run into permission errors, it may be because the rasa/rasa images run as user 1001 as a best practice, to avoid giving the container root permissions. Hence, all files created by these containers will be owned by user 1001. See the Docker documentation if you want to run the containers as a different user.

Talking to Your Assistant

To talk to your newly-trained assistant, run this command:

docker run -it -v $(pwd):/app rasa/rasa:3.7.0a1-full shell

This will start a shell where you can chat to your assistant. Note that this command includes the flags -it, which means that you are running Docker interactively, and you are able to give input via the command line. For commands which require interactive input, like rasa shell and rasa interactive, you need to pass the -it flags.

Training a Model

If you edit any training data or edit the config.yml file, you'll need to retrain your Rasa model. You can do so by running:

docker run -v $(pwd):/app rasa/rasa:3.7.0a1-full train --domain domain.yml --data data --out models

Here's what's happening in that command:

  • -v $(pwd):/app: Mounts your project directory into the Docker container so that Rasa can train a model on your training data
  • rasa/rasa:3.7.0a1-full: Use the Rasa image with the tag '3.7.0a1-full'
  • train: Execute the rasa train command within the container. For more information see Command Line Interface.

In this case, we've also passed values for the location of the domain file, training data, and the models output directory to show how these can be customized. You can also leave these out, since we are passing the default values.

Customizing your Model

Choosing a Tag

All rasa/rasa image tags start with a version number. The current version is 3.7.0a1. The tags are:

  • {version}

  • {version}-full

  • {version}-spacy-en

  • {version}-spacy-de

  • {version}-spacy-it

  • {version}-mitie-en

The {version}-full tag includes all possible pipeline dependencies, allowing you to change your config.yml as you like without worrying about missing dependencies. The plain {version} tag includes all the dependencies you need to run the default pipeline created by rasa init.

To keep images as small as possible, we also publish different tags of the rasa/rasa image with different dependencies installed. See Additional Dependencies for more dependency information specific to your pipeline. For example, if you are using components with pre-trained word vectors from spaCy or MITIE, you should choose the corresponding tag.

If your model has a dependency that is not included in any of the tags (for example, a different spaCy language model), you can build a docker image that extends the rasa/rasa image.


You can see a list of all the versions and tags of the Rasa Docker image on DockerHub.


The latest tags correspond to the build of the latest stable version.

Adding Custom Components

If you are using a custom NLU component or policy in your config.yml, you have to add the module file to your Docker container. You can do this by either mounting the file or by including it in your own custom image (e.g. if the custom component or policy has extra dependencies). Make sure that your module is in the Python module search path by setting the environment variable PYTHONPATH=$PYTHONPATH:<directory of your module>.

Adding Custom Actions

To create more sophisticated assistants, you will want to use Custom Actions. Continuing the example from above, you might want to add an action which tells the user a joke to cheer them up.

Build a custom action using the Rasa SDK by editing actions/, for example:

import requests
import json
from rasa_sdk import Action
class ActionJoke(Action):
def name(self):
return "action_joke"
def run(self, dispatcher, tracker, domain):
request = requests.get('').json() # make an api call
joke = request['value']['joke'] # extract a joke from returned json response
dispatcher.utter_message(text=joke) # send the message back to the user
return []

In data/stories.yml, replace utter_cheer_up in with the custom action action_joke tell your bot to use this new action.

In domain.yml, add a section for custom actions, including your new action:

- action_joke

After updating your domain and stories, you have to retrain your model:

docker run -v $(pwd):/app rasa/rasa:3.7.0a1-full train

Your actions will run on a separate server from your Rasa server. First create a network to connect the two containers:

docker network create my-project

You can then run the actions with the following command:

docker run -d -v $(pwd)/actions:/app/actions --net my-project --name action-server rasa/rasa-sdk:3.7.0a1

Here's what's happening in that command:

  • -d: Runs the container in detached mode so that you can run the rasa container in the same window.

  • -v $(pwd):/app: Mounts your project directory into the Docker container so that the action server can run the code in the actions folder

  • net my-project: Run the server on a specific network so that the rasa container can find it

  • --name action-server: Gives the server a specific name for the rasa server to reference

  • rasa/rasa-sdk:3.7.0a1 : Uses the Rasa SDK image with the tag 3.7.0a1

Because the action server is running in detached mode, if you want to stop the container, do it with docker stop action-server. You can also run docker ps at any time to see all of your currently running containers.

To instruct the Rasa server to use the action server, you have to tell Rasa its location. Add this endpoint to your endpoints.yml, referencing the --name you gave the server (in this example, action-server):

url: "http://action-server:5055/webhook"

Now you can talk to your bot again via the shell command:

docker run -it -v $(pwd):/app -p 5005:5005 --net my-project rasa/rasa:3.7.0a1-full shell

If you stop and restart the action-server container, you might see an error like this:

docker: Error response from daemon: Conflict. The container name "/action-server" is
already in use by container "f7ffc625e81ad4ad54cf8704e6ad85123c71781ca0a8e4b862f41c5796c33530".
You have to remove (or rename) that container to be able to reuse that name.

If that happens, it means you have a (stopped) container with the name already. You can remove it via:

docker rm action-server

Deploying your Assistant

Work on your bot until you have a minimum viable assistant that can handle your happy paths. After that, you'll want to deploy your model to get feedback from real test users. To do so, you can deploy the model you created via one of our recommended deployment methods.