readme.md

Custom BERT model torchserve quickstart

Overview

We are going to create a torchserve archive (.mar) in a two-step approach and then use it for setting up a Torchserve instance. Feel free to skip a step if you've already got the required files.

This guide references the BiobBERT model, but you can use it with any BERT implementation.

Requirements

• Python >= 3.7 with packages from requirements.txt
  • torch-model-archiver is optional
• Docker

Model preparation

Step 0. Downloading the model

We need a BERT model in a torch-like format. They can be downloaded from the official page.

Files required after this step:

biobert_large/
├── bert_config.json
├── pytorch_model.bin
└── vocab.txt

Step 1. Tracing

Torchserve operates on statically traced models. The most irritating part is that if something goes wrong in this step, the torchserve will not able of detecting it - the only hint will be the fact that the produced output will be mostly random.

Command:

python3 trace_model.py

Files required after this step:

models
├── biobert_batch.pt
└── vocab.txt

Step 2. Archiving

Now, we can pack all the files we need into a .mar archive for the torchserve. We can do it either via a docker image or a python package - the choice is yours.

Command:

docker run -v $(pwd):/app wwydmanski/torch-model-archiver --model-name biobert_batch \--version 1.0 \--serialized-file ./models/biobert_batch.pt \--extra-files ./models/vocab.txt,./utils.py,./bert_helper.py,./handler.py,./bert_tokenizer.py --handler my_handler.py  --export-path ./model-store -f

OR, without docker:

torch-model-archiver --model-name biobert_batch \--version 1.0 \--serialized-file ./models/biobert_batch.pt \--extra-files ./models/vocab.txt,./utils.py,./bert_helper.py,./handler.py,./bert_tokenizer.py --handler my_handler.py  --export-path ./model-store -f

Files required after this step:

model-store
└── biobert_batch.mar

Running the model

Command:

docker run --rm -it -p 8080:8080 -v $(pwd)/model-store:/home/model-server/model-store pytorch/torchserve torchserve --start --model-store model-store --models biobert=biobert_batch.mar

Embedding sentences

curl --location --request GET 'localhost:8080/predictions/biobert' \
--form 'data="test"'

Checking if everything's working:

~ $ curl localhost:8080/ping

Response:

{
  "status": "Healthy"
}