Warning: This document is for an old version of Rasa NLU. The latest version is 0.15.1.

Storing Models in the Cloud

Rasa NLU supports using S3 and GCS to save your models.

  • Amazon S3 Storage

    S3 is supported using the boto3 module which you can install with pip install boto3.

    Start the Rasa NLU server with storage option set to aws. Get your S3 credentials and set the following environment variables:


    If there is no bucket with the name BUCKET_NAME Rasa will create it.

  • Google Cloud Storage

    GCS is supported using the google-cloud-storage package which you can install with pip install google-cloud-storage

    Start the Rasa NLU server with storage option set to gcs.

    When running on google app engine and compute engine, the auth credentials are already set up. For running locally or elsewhere, checkout their client repo for details on setting up authentication. It involves creating a service account key file from google cloud console, and setting the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of that key file.

  • Azure Storage

    Azure is supported using the azure-storage-blob package which you can install with pip install azure-storage-blob

    Start the Rasa NLU server with storage option set to azure.

    The following environment variables must be set:


    If there is no container with the name AZURE_CONTAINER Rasa will create it.

Models are gzipped before they are saved in the cloud. The gzipped file naming convention is {PROJECT}___{MODEL_NAME}.tar.gz and it is stored in the root folder of the storage service. Currently, you are not able to manually specify the path on the cloud storage.

If storing trained models, Rasa NLU will gzip the new model and upload it to the container. If retrieving/loading models from the cloud storage, Rasa NLU will download the gzipped model locally and extract the contents to the location specified by the –path flag.

Have questions or feedback?

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa NLU GitHub repository.