Cloud Storage

Rasa supports using S3, GCS, and Azure Storage 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 server with remote-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 server with remote-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 server with remote-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 they are saved in the cloud. The gzipped file naming convention is {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 will gzip the new model and upload it to the container. If retrieving/loading models from the cloud storage, Rasa will download the gzipped model locally and extract the contents to a temporary directory.

👋 I can help you get started with Rasa and answer your technical questions.