/HuggingFace-Model-Serving

Quick and easy tutorial to serve HuggingFace sentiment analysis model using torchserve

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

HuggingFace-Model-Serving

Quick and easy tutorial to serve HuggingFace sentiment analysis model using torchserve

Full explanation of all possible configurations to serve any type of model can be found at Torchserve Github

However, This tutorial can help you to get started quickly on serving your models to production. we will be using a pretrained huggingface model distilbert-base-uncased-finetuned-sst-2-english for serving.

This model is finetuned for Text Classification (sentiment analysis) task. It is available on HuggingFace

Dependencies Installation

We will require following components available for serving. It is a good idea to create and activate a python virtual environment with name of your choice before installing python dependencies. We may want to call it "torchserve" as an environment.

  • JDK 11 You may need to sign up to oracle to download archived version of JDK to be able to download and install Torchserve uses JDK for HTTP server support.

  • pytorch Install torchserve and related components using below command

pip install torchserve torch-model-archiver torch-workflow-archiver

-Transformers As we will be serving Transformer model, we will require to install Transformers using following command

pip install transformers

Setup

We will first download the transformer model locally, then archive it to model archive file (.mar) and serve it using Torch Serve

  • Step 1 - Lets create and change directory to a local folder named "sentiment_deployment".

  • Step 2 - Clone or download and extract serve repo to your machine from Torch Serve repo. we will require a couple of files from this repo. this will give you "serve-master" directory with all the artifacts.

If you do not want to download all the files/repo from torch serve, you can download my repo that has only required files. In that case you can skip the steps 3,4 & 5 and directly continue with step 6

  • Step 3 - copy following files from serve-master folder of serve repo to sentiment_deployment folder.
/serve-master/examples/Huggingface_Transformers/setup_config.json
/serve-master/examples/Huggingface_Transformers/Download_Transformer_models.py
/serve-master/examples/Huggingface_Transformers/Transformer_handler_generalized.py
/serve-master/examples/Huggingface_Transformers/Seq_classification_artifacts/index_to_name.json
  • Step 4 - Edit setup_config.json to have following content.
{
 "model_name":"distilbert-base-uncased-finetuned-sst-2-english",
 "mode":"sequence_classification",
 "do_lower_case":true,
 "num_labels":"2",
 "save_mode":"pretrained",
 "max_length":"128",
 "captum_explanation":false,
 "embedding_name": "distilbert",
 "FasterTransformer":false,
 "model_parallel":false
}
  • Step 5 - Edit index_to_name.json to have following content.
{
 "0":"Negative",
 "1":"Positive"
}
  • Step 6 - Let's now download Transformer model using following command
python Download_Transformer_models.py

This will create a new folder Transformer_model under current directory & download transformer model mentioned in setup_config.json and and all required artifacts

  • Step 6 - Let's create Model Archive (.mar) using following command. Please ensure that you have all the files at correct places. If you have followed the steps correctly then these files should be in correct places.
torch-model-archiver --model-name distilBERTSeqClassification --version 1.0 --serialized-file Transformer_model/pytorch_model.bin --handler ./Transformer_handler_generalized.py --extra-files "Transformer_model/config.json,./setup_config.json,./index_to_name.json"
  • Step 7 - Create a directory named model_store under current directory and move your new archived model file to this folder
mkdir model_store
mv distilBERTSeqClassification.mar model_store/
  • Step 8 - This is the final step in serving the model. We will run torchserve as below
torchserve --start --model-store model_store --models sentiments=distilBERTSeqClassification.mar --ncs

If everything goes well, you should see a message like below in the terminal log

Transformer model from path c:.... loaded successfully

This confirms that you are now serving pretrained Huggingface sentiment analysis model as a REST API

Running inference

You can test the model inference that we just hosted as REST API

Quick way to do this is run below Curl command. Before you run below command you need to create a simple text file named sentiment.txt with one sentence that will be given for inference.

curl -X POST http://127.0.0.1:8080/predictions/sentiments -T sentiment.txt

You may as well write a simple python REST client to check model inference. I have added a simple REST API client code in this repo. In a new terminal with "torchserve" virtual environment activated, Please run following client code to get model inference

python /client/Sentiment_analysis_client.py

This should return the sentiment inference as Positive or Negative.

Since this server is running locally and we are only running one sentence not the batch inference, the response time is extremely fast.

Citations

This tutorial is curated list of steps from Torch Serve Github documentation.