Serverless BERT with HuggingFace and AWS Lambda

Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework, and AWS Lambda.

Philipp Schmid

Published in

Towards Data Science

7 min readJun 30, 2020

Originally published at https://www.philschmid.de on June 30, 2020.

Introduction

“Serverless” and “BERT” are two topics that strongly influenced the world of computing. Serverless architecture allows us to provide dynamically scale-in and -out the software without managing and provisioning computing power. It allows us, developers, to focus on our applications.

BERT is probably the most known NLP model out there. You can say it changed the way we work with textual data and what we can learn from it. “BERT will help [Google] Search [achieve a] better understand[ing] one in 10 searches”. BERT and its fellow friends RoBERTa, GPT-2, ALBERT, and T5 will drive business and business ideas in the next few years and will change/disrupt business areas like the internet once did.

Imagine the business value you achieve combining these two together. But BERT is not the easiest machine learning model to be deployed in a serverless architecture. BERT is quite big and needs quite some computing power. Most tutorials you find online demonstrate how to deploy BERT in “easy” environments like a VM with 16GB of memory and 4 CPUs.

I will show you how to leverage the benefits of serverless architectures and deploy a BERT Question-Answering API in a serverless environment. We are going to use the Transformers library by HuggingFace, the Serverless Framework, and AWS Lambda.

Transformer Library by Huggingface

The Transformers library provides state-of-the-art machine learning architectures like BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5 for Natural Language Understanding (NLU) and Natural Language Generation (NLG). It also provides thousands of pre-trained models in 100+ different languages and is deeply interoperability between PyTorch & TensorFlow 2.0. It enables developers to fine-tune machine learning models for different NLP-tasks like text classification, sentiment analysis, question-answering, or text generation.

AWS Lambda

AWS Lambda is a serverless computing service that lets you run code without managing servers. It executes your code only when required and scales automatically, from a few requests per day to thousands per second. You only pay for the compute time you consume — there is no charge when your code is not running.

Serverless Framework

The Serverless Framework helps us develop and deploy AWS Lambda functions. It’s a CLI that offers structure, automation, and best practices right out of the box. It also allows us to focus on building sophisticated, event-driven, serverless architectures, comprised of functions and events.

If you aren’t familiar or haven’t set up the Serverless Framework, take a look at this quick-start with the Serverless Framework.

Tutorial

Before we get started, make sure you have the Serverless Framework configured and set up. You also need a working Docker environment. A Docker environment is used to build our own python runtime, which we deploy to AWS Lambda. Furthermore, you need access to an AWS Account to create an S3 Bucket and the AWS Lambda function.

In the tutorial, we are going to build a Question-Answering API with a pre-trained BERT model. The idea is we send a context (small paragraph) and a question to the lambda function, which will respond with the answer to the question.

As this guide is not about building a model, we will use a pre-built version, that I created using distilbert. You can check the colab notebook here.

Before we start, I want to say that we’re not gonna go into detail this time. If you want to understand more about how to use Deep Learning in AWS Lambda I suggest you check out my other articles:

The architecture we are building will look like this.

What are we going to do:

create a Python Lambda function with the Serverless Framework
create an S3 Bucket and upload our model
Configure the serverless.yaml, add transformers as a dependency and set up an API Gateway for inference
add the BERT model from the colab notebook to our function
deploy & test the function

You can find everything we are doing in this GitHub repository and the colab notebook.

Create a Python Lambda function

First, we create our AWS Lambda function by using the Serverless CLI with the aws-python3 template.

This CLI command will create a new directory containing a handler.py, .gitignore and serverless.yaml file. The handler.py contains some basic boilerplate code.

Add `transformers` as a dependency

The Serverless Framework created almost anything we need, except for the requirements.txt. We create the requirements.txt by hand and add the following dependencies.

Create an S3 Bucket and upload the model

AWS S3 and Pytorch provide a unique way of working with machine learning models which are bigger than 250MB. Why 250 MB? The size of the Lambda function is limited to 250MB unzipped.

But S3 allows files to be loaded directly from S3 into memory. In our function, we are going to load our model squad-distilbert from S3 into memory and reading it from memory as a buffer in Pytorch.

If you run the colab notebook it will create a file called squad-distilbert.tar.gz, which includes our model.

For creating an S3 Bucket you can either create one using the management console or with this command.

After we created the bucket we can upload our model. You can do it either manually or using the provided python script.

Configuring the `serverless.yaml`

This time I provided the complete serverless.yamlfor us. If you want to know what each section is used for, I suggest you check out Scaling Machine Learning from ZERO to HERO. In this article, I went through each configuration and explain the usage of them.

Add the `BERT` model from the colab notebook to our function

A typical transformers model consists of a pytorch_model.bin, config.json, special_tokens_map.json, tokenizer_config.json, and vocab.txt. The pytorch_model.bin has already been extracted and uploaded to S3.

We are going to add config.json, special_tokens_map.json, tokenizer_config.json, and vocab.txt directly into our Lambda function because they are only a few KB in size. Therefore we create a model directory in our lambda function.

If this sounds complicated, check out the GitHub repository.

The next step is to create a model.py in the model/ directory that holds our model class ServerlessModel.

In the handler.py we create an instance of our ServerlessModel and can use the predict function to get our answer.

Deploy & Test the function

In order to deploy the function you only have to run serverless deploy.

After this process is done we should see something like this.

Test and Outcome

To test our Lambda function we can use Insomnia, Postman, or any other REST client. Just add a JSON with a context and a question to the body of your request. Let´s try it with our example from the colab notebook.

Our ServerlessModel answered our question correctly with 83.1. Also, you can see the complete request took 319ms with a lambda execution time of around 530ms. To be honest, this is pretty fast.

The best thing is, our BERT model automatically scales up if there are several incoming requests! It scales up to thousands of parallel requests without any worries.

If you rebuild this, you have to be careful that the first request could take a while. First off, the Lambda is unzipping and installing our dependencies and then downloading the model from S3.

Thanks for reading. You can find the GitHub repository with the complete code here and the colab notebook here.

Thanks for reading. If you have any questions, feel free to contact me or comment on this article. You can also connect with me on Twitter or LinkedIn.