https://www.kaggle.com/savasy/ttc4900
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.dvc | 1 month ago | |
artifacts | 1 month ago | |
data | 1 month ago | |
metrics | 1 month ago | |
news_cat | 1 month ago | |
scripts | 1 month ago | |
.dockerignore | 1 month ago | |
.dvcignore | 1 month ago | |
.gitignore | 1 month ago | |
Dockerfile | 1 month ago | |
LICENSE | 1 month ago | |
README.md | 1 month ago | |
app.py | 1 month ago | |
docker-build.sh | 1 month ago | |
params.yaml | 1 month ago | |
poetry.lock | 1 month ago | |
pyproject.toml | 1 month ago | |
requirements.txt | 1 month ago |
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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.
dvc pull
in this repo to get the data/artifacts.docker-build.sh
file (you can change the tag name if you want).docker run -d -p (your host machine port):8080 newscat (or the other name)
Examples:
docker run -d -p 8085:8080 newscat
docker run -it -p 8085:8081 --env PORT=8081 newscat