---
id: building-in-docker
sidebar_label: Building a Rasa Assistant in Docker
title: Building a Rasa Assistant in Docker
description: Learn how to build a Rasa assistant in Docker.
---
import useBaseUrl from '@docusaurus/useBaseUrl';

<!-- this file is version specific, do not use `@site/...` syntax -->
import variables from '../variables.json';

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](../deploy/introduction.mdx) to learn how to deploy your model.


## Installing Docker

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

```bash
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](https://docs.docker.com/install/) 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:

<pre><code parentName="pre" className="language-bash">
{`docker run -v $(pwd):/app rasa/rasa:${variables.release}-full init --no-prompt`}</code></pre>

What does this command mean?

<ul>
  <li><inlineCode>-v $(pwd):/app</inlineCode> 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.</li>
  <li><inlineCode>rasa/rasa</inlineCode> is the name of the docker image to run. '{variables.release}-full' is the name of the tag,
    which specifies the version and dependencies.</li>

  <li>the Docker image has the <inlineCode>rasa</inlineCode> command as its entrypoint, which means you don't
    have to type <inlineCode>rasa init</inlineCode>, just <inlineCode>init</inlineCode> is enough.</li>
</ul>

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:

```bash
ls -1
```

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

:::note
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](https://docs.docker.com/reference/cli/docker/container/run/)
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:

<pre><code parentName="pre" className="language-bash">
{`docker run -it -v $(pwd):/app rasa/rasa:${variables.release}-full shell`}</code></pre>

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:

<pre><code parentName="pre" className="language-bash">
{`docker run -v $(pwd):/app rasa/rasa:${variables.release}-full train --domain domain.yml --data data --out models`}</code></pre>

Here's what's happening in that command:

<ul>
  <li><inlineCode>-v $(pwd):/app</inlineCode>: Mounts your project directory into the Docker
  container so that Rasa can train a model on your training data</li>

  <li>rasa/rasa:{variables.release}-full: Use the Rasa image with the tag '{variables.release}-full'</li>

  <li><inlineCode>train</inlineCode>: Execute the <inlineCode>rasa train</inlineCode> command within the container. For more
  information see <a href={useBaseUrl("/command-line-interface")}>Command Line Interface</a>.</li>
</ul>

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

<p>
  All <inlineCode>rasa/rasa</inlineCode> image tags start with a version number. The current version is  {variables.release}. The tags are:
</p>

* `{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](../installation/installing-rasa-open-source.mdx#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.

:::note
You can see a list of all the versions and tags of the Rasa
Docker image on [DockerHub](https://hub.docker.com/r/rasa/rasa/).

:::

:::caution
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](../actions.mdx#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/actions.py`, for example:

```python
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 []
```

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:

```yaml-rasa
actions:
  - action_joke
```

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

<pre><code parentName="pre" className="language-bash">
{`docker run -v $(pwd):/app rasa/rasa:${variables.release}-full train`}</code></pre>

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

```bash
docker network create my-project
```

You can then run the actions with the following command:

<pre><code parentName="pre" className="language-bash">
{`docker run -d -v $(pwd)/actions:/app/actions --net my-project --name action-server rasa/rasa-sdk:${variables.rasa_sdk_version}`}</code></pre>


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

* <code>rasa/rasa-sdk:{variables.rasa_sdk_version}</code> : Uses the Rasa SDK image with the tag {`${variables.rasa_sdk_version}`}

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`):

```yaml-rasa
action_endpoint:
  url: "http://action-server:5055/webhook"
```

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

<pre><code parentName="pre" className="language-bash">
{`docker run -it -v $(pwd):/app -p 5005:5005 --net my-project rasa/rasa:${variables.release}-full shell`}</code></pre>

:::note
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:

```bash
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](../deploy/introduction.mdx#recommended-deployment-method).
