Running GPT2 Inference through a Streamlit Interface on GCP
Inpsired by this article.
Bringing a Machine Learning model outside of a notebook environment and turning it into a beautiful data product used to be a lot of work. Luckily, there’s a lot of tooling being developed in this area to make prototyping easier. A while ago, I came across Streamlit, an open source Python library for building custom web apps. It’s quick and easy to get started and took me less than 30 minutes to build an app with a pre-trained model. Since then, I have been using Streamlit for prototyping models and demonstrating their capabilities. The nice user interface is a refreshing change from using my console for predictions. I’m going to be sharing the process in this article, using GPT-2 as an example.
Make it run locally
Clone the sample git
Just clone the sample git via entering the following in your terminal.
git clone https://github.com/seduerr91/gpt2-streamlit.git
Set up a virtual environment
Set up a virtual environment, start it and install all the necessary requirements with the following code.
mkdir env python3 -m venv env/st source env/st/bin/activate pip3 install -r requirements.txt
Load GPT-2 from Transformers
Wait for all the libraries to be installed.
Run it locally
Run via ‘python3 app.py’
Run it online
Inspired by this article Write: heroku login Next: heroku create
git add . git commit -m “Enter your message here” git push heroku master
No for web running:
heroku ps:scale web=1