These docs are for version 1.x of Rasa Open Source.

## 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](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/#when-to-deploy-your-assistant)
- [Recommended Deployment Methods](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/#recommended-deployment-methods)
  - [Kubernetes/Openshift](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/#kubernetes-openshift)
  - [Docker Compose](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/#docker-compose)
- [Rasa-Only Deployment with Docker Compose](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/how-to-deploy/#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). The recommended deployment methods allow you to share your assistant with test users via the [share your assistant feature in Rasa X](/content/docs/rasa-x/user-guide/enable-workflows#conversations-with-test-users/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.

### Kubernetes/Openshift

Kubernetes/Openshift is the best option if you need a scalable architecture. It’s straightforward to deploy if you use the helm charts we provide. However, you can also customize the Helm charts if you have specific requirements.

> - 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

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

```bash
docker -v && docker-compose -v
```

### 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/1.8.2/user-guide/rasa-tutorial/#rasa-tutorial), you’ll use the `rasa init` command to create a project.

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

#### Talking to Your Assistant

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

```bash
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.

### 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:

```bash
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/1.8.2/user-guide/messaging-and-voice-channels/#messaging-and-voice-channels) in `credentials.yml`.

### Using Docker Compose to Run Multiple Services

To run Rasa together with other services, it is recommend to use [Docker Compose](https://docs.docker.com/compose/).

### Adding Custom Actions

To create more sophisticated assistants, you will want to use [Custom Actions](https://legacy-docs-v1.rasa.com/1.8.2/core/actions/#custom-actions).

#### Creating a Custom Action

Start by creating the custom actions in a directory `actions`:

```bash
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 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/1.8.2/api/tracker-stores/#tracker-stores).

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