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Integration:  dvc git github
Arslan fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
3835ed1044
Add base data and track it with DVC. Add associated python requirements
3 years ago
8983e96d17
Feature: Logistic Classifier
3 years ago
8983e96d17
Feature: Logistic Classifier
3 years ago
a8aba98742
Add necessary folders - data,metrics,artifacts
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
8983e96d17
Feature: Logistic Classifier
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
3835ed1044
Add base data and track it with DVC. Add associated python requirements
3 years ago
92fdede2b3
Add basic app config (shared)
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
6ee8331777
Initial commit
3 years ago
4bd0544299
Add Dockerfile, and related build instructions in README
3 years ago
f352145576
Add api base/skeleton
3 years ago
4bd0544299
Add Dockerfile, and related build instructions in README
3 years ago
8983e96d17
Feature: Logistic Classifier
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
fe3e40ac67
Merge from Dev: Update Docker config, use gunicorn and remove Pytorch
3 years ago
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README.md

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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
Tip!

Press p or to see the previous file or, n or to see the next file

About

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

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