NLP - News classification

Train and deploy a news classifier based on Logistic Regression.

• Serverless version: https://newsclassification-acv3odjjfa-ew.a.run.app/

Architecture

Running on cloud/local machine

To run the application, we can use the pre-build docker image available on Docker Hub and simply run the following command

docker run --rm -p 8080:8080 ahmedbendebba/news_classification:latest

The application will be available on http://0.0.0.0:8080. The user can run a customized Gunicorn command to specify number of workers or an HTTPS certificate.

# Get into the container
docker run -it --rm -v ~/nlp:/cert -p 8080:8080 ahmedbendebba/news_classification:latest bash

Serverless deployement - Google Run

Google Run is a new service from GCP that allows serverless deployment of containers with HTTPS endpoints. The app will run on 1 CPU with 2GB memory and have the ability to scale automatically depending on the number of concurrent requests.

• Build image and push it to Container Registry

From a GCP project, we will use Google Shell to build the image and push it to GCR (container registry).

# Get name of project 
# For illustration we will call it PROJECT-ID

gcloud config get-value project

Create the following Dockerfile in your CloudShell session.

FROM ahmedbendebba/news_classification:latest

# Google Run uses env variable PORT 

CMD gunicorn --bind :$PORT wsgi:app

Finally, we can build and submit the image to GCR.

gcloud builds submit --tag gcr.io/PROJECT-ID/news_classifier

• Deploy on Google Run

From Google Run page, we will use the image gcr.io/PROJECT-ID/news_classifier:latest to run the app. Create a new service

Then enter the address of the image, choose other parameters as follows and deploy

After few seconds, you will see a link to the app.

Serverless version may suffer from cold-start if the service does not receive requests for a long time.

Reproduce results

LR and SVM

• Requirements

To reproduce results reported in the blog post, we need to install the requirements in our development environment.

# Open requirement.txt and select torch==1.1.0 instead of the cpu version used for inference only.
# Then install requirements
pip install -r requirements.txt

• Data

Run the notebook to save data in the data folder

• Hyper-parameter search

After completing the installation, we can run parameters search or training of sklearn models as follows

# Params search for SVM
cd sklearn_models
python3 params_search.py --model svc --exp_name svmsearch_all --data dataset_processed

# Params search for LR
python3 params_search.py --model lreg --exp_name logreg_all --data dataset_processed

The parameters space is defined in the file sklearn_models/params_search.py. The outputs will be saved in the logs folder.

• Training

Training a model for a fixed set of parameters can be done using sklearn_models/baseline.py

# Specify the parameters of the model inside baseline.py and run
python3 baseline.py --model svc --exp_name svc_all --data dataset_processed

The logs/metrics on test dataset will be saved in sklearn_models/logs/ and the trained model will be saved in sklearn_models/saved_models/.

Trained model is available for download at: https://github.com/ahmedbendebba1/News-classification-api/releases

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Ahmed Ben Debba