My Journey to a serverless transformers pipeline on Google Cloud

A guest blog post by community member Maxence Dominici

This article will discuss my journey to deploy the transformers sentiment-analysis pipeline on Google Cloud. We will start with a quick introduction to transformers and then move to the technical part of the implementation. Finally, we'll summarize this implementation and review what we have achieved.

The Goal

I wanted to create a micro-service that automatically detects whether a customer review left in Discord is positive or negative. This would allow me to treat the comment accordingly and improve the customer experience. For instance, if the review was negative, I could create a feature which would contact the customer, apologize for the poor quality of service, and inform him/her that our support team will contact him/her as soon as possible to assist him and hopefully fix the problem. Since I don't plan to get more than 2,000 requests per month, I didn't impose any performance constraints regarding the time and the scalability.

The Transformers library

I was a bit confused at the beginning when I downloaded the .h5 file. I thought it would be compatible with tensorflow.keras.models.load_model, but this wasn't the case. After a few minutes of research I was able to figure out that the file was a weights checkpoint rather than a Keras model. After that, I tried out the API that Hugging Face offers and read a bit more about the pipeline feature they offer. Since the results of the API & the pipeline were great, I decided that I could serve the model through the pipeline on my own server.

Below is the official example from the Transformers GitHub page.

from transformers import pipeline

# Allocate a pipeline for sentiment-analysis
classifier = pipeline('sentiment-analysis')
classifier('We are very happy to include pipeline into the transformers repository.')
[{'label': 'POSITIVE', 'score': 0.9978193640708923}]