.. _section_persistence: 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.