Deployment Environments

Architecture#

Rasa Enterprise allows multiple versions of an assistant to be run simultaneously and served to different users. By default, two environments are defined:

• production

• worker

This page provides a walkthrough of the best practices for managing the lifecycle of a Rasa model.

Each environment corresponds to a Rasa server which uses the model with the corresponding tag (e.g. production). The model training happens on the worker instances to ensure the training does not degrade performance.

A model can be given multiple environment tags, but each environment can only have a single model associated with it. If model A is tagged experimental and you promote it to production, the model will now have two tags, and the two environments will use the same model. This ensures that you can promote a model to an environment without affecting any others.

The name and number of environments can be customized when deploying Rasa Enterprise.

Workflow#

1. The first step is to move a trained model into the experimental environment. To do this, first navigate to the models view. Hover over a model, and click the three dots menu which appears on the table row. Now you can select the experimental environment and click ‘Save’.

This model will now be used to serve users at the endpoint /conversations/{sender_id}/messages?environment=experimental.

1. Now that your model is running in the development environment, you can do some testing. Navigate to the ‘Talk to your bot’ view and select ‘experimental’ from the dropdown on the right. If you make any improvements, like adding more training data or updating the response templates, you need to use the ‘train’ button to create a new model and promote that model to experimental.

2. Once you are satisfied with your model’s performance, repeat step 1 but instead selecting the production environment.

Customizing Deployment Environments#

You currently cannot modify your deployment environments via the API or the user interface. In order to customize the environments (e.g. to create a new one), you will have to edit your deployment setup. If you are using docker compose, edit the environments key in your docker-compose.yml file.

Rasa Enterprise lets you run multiple Rasa models in parallel, allowing you to test different environments promoting them to production. In this section we’ll show you how to

1. Launch additional Rasa servers that run your models

2. Make the additional servers accessible in the /conversations tab

3. Assign tags to the Rasa models so they’re run by the right server

Starting Additional Rasa Servers#

By default, Rasa Enterprise runs two Rasa servers: one that should be used in production and one worker that executes certain tasks like training models. Let’s look at an example where we add a fourth Rasa server in addition to the predefined rasa-production and rasa-worker servers. Let’s call it rasa-experimental. Create a file called docker-compose.override.yml in /etc/rasa containing the following:

Right below, make an entry for the new Rasa service rasa-experimental:

It’s important that the RASA_ENVIRONMENT variable is unique, and that RASA_MODEL_SERVER requests a unique tag, in this case experimental.

Updating the Environments Config#

We need to let Rasa Enterprise know about the newly defined Rasa servers, so that you can talk to the models running on it in the /conversations view. Following the example of our rasa-experimental service, add an entry to your environments settings in the environments.yml file:

The hostname part of url has to match the service name defined in docker-compose.override.yml (in this case rasa-experimental).

Tagging a Model#

The final step is to upload a Rasa model and assign the right tag. You can upload models and tag them via the HTTP API. If you have uploaded a model via the API, you can also tag it in the interface.