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

Model Persistence

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:

  • AWS_SECRET_ACCESS_KEY
  • AWS_ACCESS_KEY_ID
  • AWS_DEFAULT_REGION
  • BUCKET_NAME
  • AWS_ENDPOINT_URL

  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:

  • AZURE_CONTAINER
  • AZURE_ACCOUNT_NAME
  • AZURE_ACCOUNT_KEY

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

Models are gzipped before saving to cloud.