Warning: This document is for an old version of Rasa.

Running Rasa with Docker

This is a guide on how to build a Rasa assistant with Docker. If you haven’t used Rasa before, we’d recommend that you start with the Rasa Tutorial.

Installing Docker

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

docker -v && docker-compose -v
# Docker version 18.09.2, build 6247962
# docker-compose version 1.23.2, build 1110ad01

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

Building an Assistant with Rasa and Docker

This section will cover the following:

  • Setting up your Rasa project and training an initial model

  • Talking to your AI assistant via Docker

  • Choosing a Docker image tag

  • Training your Rasa models using Docker

  • Talking to your assistant using Docker

  • Running a Rasa server with Docker


Just like in the tutorial, 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 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.

  • 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.


By default Docker runs containers as root user. Hence, all files created by these containers will be owned by root. See the documentation of docker and docker-compose if you want to run the containers with 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 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.

Customizing your Model

Choosing a Tag

To keep images as small as possible, we publish different tags of the rasa/rasa image with different dependencies installed. See Choosing a Pipeline for more information about depedencies.

All tags start with a version – the latest tag corresponds to the current master build. The tags are:

  • {version}

  • {version}-spacy-en

  • {version}-spacy-de

  • {version}-mitie-en

  • {version}-full

The plain {version} tag includes all the dependencies you need to run the supervised_embeddings pipeline. If you are using components with pre-trained word vectors, you need to choose the corresponding tag. Alternatively, you can use the -full tag, which includes all pipeline dependencies.


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

Training a Custom Rasa Model with Docker

Edit the config.yml file to use the pipeline you want, and place your NLU and Core data into the data/ directory. Now you can train your Rasa model by running:

docker run \
  -v $(pwd):/app \
  rasa/rasa:latest-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:latest-full: Use the Rasa image with the tag latest-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.


If you are using a custom NLU component or policy, 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>.

Running the Rasa Server

To run your AI assistant in production, configure your required Messaging and Voice Channels in credentials.yml. If this file does not exist, create it using:

touch credentials.yml

Then edit it according to your connected channels. After, run the trained model with:

docker run \
  -v $(pwd)/models:/app/models \
  rasa/rasa:latest-full \

Command Description:

  • -v $(pwd)/models:/app/models: Mounts the directory with the trained Rasa model in the container

  • rasa/rasa:latest-full: Use the Rasa image with the tag latest-full

  • run: Executes the rasa run command. For more information see Command Line Interface.

Using Docker Compose to Run Multiple Services

To run Rasa together with other services, such as a server for custom actions, it is recommend to use Docker Compose. Docker Compose provides an easy way to run multiple containers together without having to run multiple commands.

Start by creating a file called docker-compose.yml:

touch docker-compose.yml

Add the following content to the file:

version: '3.0'
    image: rasa/rasa:latest-full
      - 5005:5005
      - ./:/app
      - run

The file starts with the version of the Docker Compose specification that you want to use. Each container is declared as a service within the docker-compose file. The first service is the rasa service.

The command is similar to the docker run command. The ports part defines a port mapping between the container and your host system. In this case it makes 5005 of the rasa service available on port 5005 of your host. This is the port of the REST Channel interface of Rasa.


Since Docker Compose starts a set of Docker containers, it is no longer possible to connect to the command line of a single container after executing the run command.

To run the services configured in your docker-compose.yml execute:

docker-compose up

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.

Creating a Custom Action

Start by creating the custom actions in a directory actions:

mkdir actions
# Rasa SDK expects a python module.
# Therefore, make sure that you have this file in the directory.
touch actions/__init__.py
touch actions/actions.py

Then build a custom action using the Rasa SDK, e.g.:

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('http://api.icndb.com/jokes/random').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 []

Next, add the custom action in your stories and your domain file. Continuing with the example bot from rasa init, replace utter_cheer_up in data/stories.md with the custom action action_joke, and add action_joke to the actions in the domain file.

Adding the Action Server

The custom actions are run by the action server. To spin it up together with the Rasa instance, add a service action_server to the docker-compose.yml:

version: '3.0'
    image: rasa/rasa:latest-full
      - 5005:5005
      - ./:/app
      - run
    image: rasa/rasa-sdk:latest
      - ./actions:/app/actions

This pulls the image for the Rasa SDK which includes the action server, mounts your custom actions into it, and starts the server.

To instruct Rasa to use the action server you have to tell Rasa its location. Add this to your endpoints.yml (if it does not exist, create it):

  url: http://action_server:5055/webhook

Run docker-compose up to start the action server together with Rasa.

Adding Custom Dependencies

If your custom action has additional dependencies of systems or Python libraries, you can add these by extending the official image.

To do so, create a file named Dockerfile in which you extend the official image and add your custom dependencies. For example:

# Extend the official Rasa SDK image
FROM rasa/rasa-sdk:latest

# Add a custom system library (e.g. git)
RUN apt-get update && \
    apt-get install -y git

# Add a custom python library (e.g. jupyter)
RUN pip install --no-cache-dir jupyter

You can then build the image via the following command, and use it in your docker-compose.yml instead of the rasa/rasa-sdk image.

docker build . -t <name of your custom image>:<tag of your custom image>

Adding a Custom Tracker Store

By default, all conversations are saved in memory. This means that all conversations are lost as soon as you restart the Rasa server. If you want to persist your conversations, you can use a different Tracker Store.

Using PostgreSQL as Tracker Store

Start by adding PostgreSQL to your docker-compose file:

  image: postgres:latest

Then add PostgreSQL to the tracker_store section of your endpoint configuration config/endpoints.yml:

  type: sql
  dialect: "postgresql"
  url: postgres
  db: rasa

Using MongoDB as Tracker Store

Start by adding MongoDB to your docker-compose file. The following example adds the MongoDB as well as a UI (you can skip this), which will be available at localhost:8081. Username and password for the MongoDB instance are specified as rasa and example.

  image: mongo
  image: mongo-express
    - 8081:8081

Then add the MongoDB to the tracker_store section of your endpoints configuration endpoints.yml:

  type: mongod
  url: mongodb://mongo:27017
  username: rasa
  password: example

Then start all components with docker-compose up.

Using Redis as Tracker Store

Start by adding Redis to your docker-compose file:

  image: redis:latest

Then add Redis to the tracker_store section of your endpoint configuration endpoints.yml:

  type: redis
  url: redis

Using a Custom Tracker Store Implementation

If you have a custom implementation of a tracker store you have two options to add this store to Rasa:

  • extending the Rasa image

  • mounting it as volume

Then add the required configuration to your endpoint configuration endpoints.yml as it is described in Tracker Stores. If you want the tracker store component (e.g. a certain database) to be part of your Docker Compose file, add a corresponding service and configuration there.