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Deploying huggingface‘s BERT to production with pytorch/serve

TorchServe architecture. Image first found in an AWS blogpost on TorchServe.

TL;DR: pytorch/serve is a new awesome framework to serve torch models in production. This story teaches you how to use it for huggingface/transformers models like BERT.

Traditionally, serving pytorch models in production was challenging, as no standard framework used to be available for this task. This gap allowed its main competitor tensorflow to retain a strong grasp on many production systems, as it provided solid tooling for such deployments in its tensorflow/serving framework.

However, nowadays most new models and approaches tend to first be developed and made available in pytorch as researchers enjoy its flexibility for prototyping. This creates a gap between the state-of-the-art developed in research labs and the models typically deployed to production in most companies. In fast-moving fields such as natural language processing (NLP) this gap can be quite pronounced in spite of the efforts of frameworks like huggingface/transformers to provide model compatibility for both frameworks. In practice, development and adoption of new approaches tends to happen in pytorch first and by the time frameworks and productive systems have caught up and integrated a tensorflow version, new and more improved models have already deprecated it.

Most recently, the pytorch developers have released their new serving framework pytorch/serve to address these issues in a straightforward manner.

Introduction to TorchServe

TorchServe is a flexible and easy to use tool for serving PyTorch models.

TorchServe (repository: pytorch/serve) is a recently (4 days ago at the time of writing) released framework developed by the pytorch developers to allow easy and efficient productionalization of trained pytorch models.

I recommend reading this AWS blog post for a thorough overview over TorchServe.

Serving Transformer models

huggingface/transformers can be considered a state-of-the-art framework for deep learning on text and has shown itself nimble enough to follow the rapid developments in this fast-moving space.

As this is a very popular framework with many active users (>25k stars on Github) from various different domains, it comes as no surprise that there is already interest (e.g. here, here and here) in serving BERT and other transformer models using TorchServe.

This story will explain how to serve your trained transformer model with TorchServe.


To avoid unnecessarily bloating this post, I will make an assumption: you already have a trained BERT (or other transformers sentence classifier model) checkpoint.

If you don’t, worry not: I will provide references to guides you can follow to get one of your own in no time.

Installing TorchServe

TorchServe provides an easy guide for its installation with pip, conda or docker. Currently, the installation is roughly comprised of two steps:

  • Install Java JDK 11
  • Install torchserve with its python dependencies

Please go through the installation guide linked above to ensure TorchServe is installed on your machine.

Training a huggingface BERT sentence classifier

Many tutorials on this exist and as I seriously doubt my ability to add to the existing corpus of knowledge on this topic, I simply give a few references I recommend:

A simple way to get a trained BERT checkpoint is to use the huggingface GLUE example for sentence classification:

At the end of training, please ensure that you place trained model checkpoint (pytorch.bin), model configuration file (config.json) and tokenizer vocabulary file (vocab.txt) in the same directory. In what follows below, I will use a trained “bert-base-uncased” checkpoint and store it with its tokenizer vocabulary in a folder “./bert_model”.

For reference, mine looks like this:

Model checkpoint folder, a few files are optional

Defining a TorchServe handler for our BERT model

This is the salt: TorchServe uses the concept of handlers to define how requests are processed by a served model. A nice feature is that these handlers can be injected by client code when packaging models, allowing for a great deal of customization and flexibility.

Here is my template for a very basic TorchServe handler for BERT/transformer classifiers:

A few things that my handler does not do, but yours might want to do:

  • Custom pre-processing of the text (here we are just tokenizing)
  • Any post-processing of the BERT predictions (these can be added in the postprocess function).
  • Load an ensemble of models. One easy way to achieve this would be to load additional checkpoints in the initialize function and provide ensemble prediction logic in the inference function.

Converting the trained checkpoint to TorchServe MAR file

TorchServe uses a format called MAR (Model Archive) to package models and version them inside its model store. To make it accessible from TorchServe, we need to convert our trained BERT checkpoint to this format and attach our handler above.

The following command does the trick:

torch-model-archiver --model-name "bert" --version 1.0 --serialized-file ./bert_model/pytorch_model.bin --extra-files "./bert_model/config.json,./bert_model/vocab.txt" --handler "./"

This command attaches the serialized checkpoint of your BERT model (./bert_model/pytorch_model.bin) to our new custom handler described above and adds in extra files for the configuration and tokenizer vocabulary. It produces a file named bert.mar that can be understood by TorchServe.

Next, we can start a TorchServe server (by default it uses ports 8080 and 8081) for our BERT model with a model store that contains our freshly created MAR file:

mkdir model_store && mv bert.mar model_store && torchserve --start --model-store model_store --models bert=bert.mar

That’s it! We can now query the model using the inference API:

curl -X POST -T unhappy_sentiment.txt

In my case, unhappy_sentiment.txt is a file containing example sentences with a negative sentiment. My model correctly predicted a negative sentiment for this text (class 0).

Note that there are many additional interesting facilities available out of the box in the management API. For example we can easily get a list of all registered models, register a new model or new model version and switch served model versions for each model dynamically.

Happy coding and serving!



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Data Scientist & ML Engineer in the Applied Technology and Innovation Team at Novartis (All views expressed are solely my own)