https://www.kaggle.com/savasy/ttc4900

Arslan fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3835ed1044
Add base data and track it with DVC. Add associated python requirements
6 months ago
8983e96d17
Feature: Logistic Classifier
6 months ago
8983e96d17
Feature: Logistic Classifier
6 months ago
a8aba98742
Add necessary folders - data,metrics,artifacts
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
8983e96d17
Feature: Logistic Classifier
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
3835ed1044
Add base data and track it with DVC. Add associated python requirements
6 months ago
92fdede2b3
Add basic app config (shared)
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
6ee8331777
Initial commit
6 months ago
4bd0544299
Add Dockerfile, and related build instructions in README
6 months ago
f352145576
Add api base/skeleton
6 months ago
4bd0544299
Add Dockerfile, and related build instructions in README
6 months ago
8983e96d17
Feature: Logistic Classifier
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
6 months ago
Data Pipeline
Legend
DVC Managed File
Git Managed File
Metric
Stage File
External File

README.md

Turkish Text Categorization

This is meant to serve as an example of how to structure and work on your ML projects, and deploy the resultant models.

The dataset I have chosen can be found on Kaggle.

  • The python packages can be installed via the requirements.txt file (in a venv), or using Poetry (preferred way).
  • To get the models and data files, you'll also need DVC. Just run dvc pull in this repo to get the data/artifacts.

Docker

  • The docker images can be built easily using the docker-build.sh file (you can change the tag name if you want).
  • Then run the image simply using docker run -d -p (your host machine port):8080 newscat (or the other name)
  • The port on docker can be configured using the env variable port.

Examples:

docker run -d -p 8085:8080 newscat

docker run -it -p 8085:8081 --env PORT=8081 newscat