Hi all,
I’m deploying my RASA bot by loading a model from an AWS bucket. Since containers get recreated sometimes, the model gets lost again and needs to be reloaded when recreating the containers. To avoid this I would like to store the model on a volume but I do not know how I need to configure this in my docker-compose file as I do not have an initial models folder that I could define as a persistent volume. This is part of my docker-compose.yml:
rasa-server:
image: rasa-bot:latest
working_dir: /app
build:
context: ./
dockerfile: Dockerfile
volumes:
## - ./models /app/models
command: bash -c "rasa run --model model.tar.gz --remote-storage aws --endpoints endpoints.yml --credentials credentials.yml --enable-api --cors \"*\" --debug --port 5006"
And this is my Dockerfile:
FROM python:3.7.7-stretch AS BASE
RUN apt-get update \
&& apt-get --assume-yes --no-install-recommends install \
build-essential \
curl \
git \
jq \
libgomp1 \
vim
WORKDIR /app
RUN pip install --no-cache-dir --upgrade pip
RUN pip install rasa==3.1
RUN pip3 install boto3
ADD . .
What I’m not sure about is this part in my docker-compose.yml:
volumes:
## - ./models /app/models
Will the model fetched from AWS storage be saved in app/models? And then, since my command is still the same like
–model model.tar.gz --remote-storage aws
it will fetch it again from AWS instead of looking in a local folder, is that correct? One solution to that, could be somehow check in Docker whether a volume exists and then run a second docker-compose.yml that uses the model from the volume. But how to do so?
@stephens you seem to be an expert with that 