This tutorial takes you through the creation of a secure and automated streamlit deployment with infrastructure set up to run batch jobs with custom docker images and view results.
These are loosely based on things I’ve seen or set up in company settings, but done in a more generic way that should be generally useful!
Final code structure overview The code for this second part looks mostly similar to the second part with a new workflow, a new countdown subpackage, and a new batch.py util file. Sample code repo here. sample-streamlit-deploy .github/workflows/ custom-docker-push.yml docker-push.yml update-streamlit.yml .gitignore code_package/ countdown/ countdown.py streamlit/ pages/* util/ google_oauth.py Home.py requirements.txt start.sh util/ batch.py cloud.py Dockerfile Makefile docker-compose.yml environment.yml Overview One of the limitations of streamlit is that it doesn’t play well with longer running computations....
Final code structure overview The code for this second part looks mostly similar to the first part with some added files in the streamlit directory. Sample code repo here. sample-streamlit-deploy .github/workflows/ docker-push.yml update-streamlit.yml .gitignore code_package/ streamlit/ pages/* util/ google_oauth.py Home.py requirements.txt start.sh util/ cloud.py Dockerfile Makefile docker-compose.yml environment.yml Overview In a company setting, we may want to build streamlit apps which deal with sensitive information. In that case, we have to think about the security of our app....
Final code structure overview In this case, I wanted the streamlit deployment to be part of a larger repo with multiple tools/packages that use a common environment. As a result, the repo is structured with a top level package code_package under which we’ll make our streamlit directory. In the same repo as the code_package directory, we have our .github directory for workflows and a variety of other files such as the Dockerfile or Makefile....