# 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**

- **Recommended Deployment Methods**

- **Kubernetes/Openshift**
  - **Docker Compose**
- **Rasa-Only Deployment with Docker Compose**

## 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](/content/docs/rasa/glossary/index.html).

## Recommended Deployment Methods

The recommended way to deploy an assistant is using either the Docker Compose or Kubernetes/Openshift 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.

### Kubernetes/Openshift

> - Default: Read the docs [here](/content/docs/rasa-x/installation-and-setup/openshift-kubernetes/index.html).
> - Custom: Read the docs [here](/content/docs/rasa-x/installation-and-setup/openshift-kubernetes/index.html) and customize the [open source Helm charts](https://github.com/RasaHQ/rasa-x-helm).

### Docker Compose

> - Default: Watching this [video](https://www.youtube.com/watch?v=IUYdwy8HPVc) or read the docs [here](/content/docs/rasa-x/installation-and-setup/docker-compose-script/index.html).
> - Custom: Read the docs [here](/content/docs/rasa-x/installation-and-setup/docker-compose-manual/index.html).

## Rasa-Only Deployment with Docker Compose

It is also possible to deploy a Rasa assistant using Docker Compose without Rasa X.

- **Installing Docker**
- **Building an Assistant with Rasa and Docker**
- **Customizing your Model**

### 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
```

### 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

#### Setup

Just like in the [tutorial](https://legacy-docs-v1.rasa.com/1.8.1/user-guide/rasa-tutorial/#rasa-tutorial), you’ll use the `rasa init` command to create a project.

```
docker run -v $(pwd):/app rasa/rasa init --no-prompt
```

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.

#### Talking to Your Assistant

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

```
docker run -it -v $(pwd):/app rasa/rasa shell
```

### 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](https://legacy-docs-v1.rasa.com/1.8.1/nlu/choosing-a-pipeline/#choosing-a-pipeline) for more information about dependencies.

#### 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
```

### Running the Rasa Server

To run your AI assistant in production, configure your required [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.8.1/user-guide/messaging-and-voice-channels/#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 \
  run
```

### Using Docker Compose to Run Multiple Services

To run Rasa together with other services, such as a server for custom actions, it is recommended to use [Docker Compose](https://docs.docker.com/compose/).

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

```
touch docker-compose.yml
```

Add the following content to the file:

```
version: '3.0'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run
```

### Adding Custom Actions

To create more sophisticated assistants, you will want to use [Custom Actions](https://legacy-docs-v1.rasa.com/1.8.1/core/actions/#custom-actions).

#### 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
```

#### 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'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run
  action_server:
    image: rasa/rasa-sdk:latest
    volumes:
      - ./actions:/app/actions
```

### Adding Custom Dependencies

If your custom action has additional dependencies of systems or Python libraries, you can add these by extending the official 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](https://legacy-docs-v1.rasa.com/1.8.1/api/tracker-stores/#tracker-stores).