llearnell/ubuntu-ml repository overview

⁠mldeploy

An attempt to deploy machine learning models via docker. In this docker image most common build tools and common machine learning frameworks have been included

⁠Docker image details

Includes following high level packages for CPU only

1. miniconda with python 3.8.8
2. Pytorch
3. Tensorflow
4. keras
5. sklearn
6. spaCy
7. spaCy en_core_web_trf package for english
8. nltk
9. Huggingface transformers
10. sentencepiece
11. sentence_transformers from SBERT
12. charting packages (plotly, matplotlib, bokeh, seaborn)

⁠How to use

docker run -it -v persitent_location_on_disk:/workspace/app llearnell/ubuntu-ml

Once the container is running, you will be provided with a /bin/bash prompt within /workspace/app directory All python environment already set.

⁠Where are the downloaded transformer models stored

They are stored in /workspace/app/data/ folder. This is one level inside the persistent_location_on_disk you provided while running the docker container.

⁠Why is the image size so large ?

I wanted a system where i have to do minimal steps and not wait for dependency resolution by conda everytime i replicate the environment.

Docker container can be found here: https://hub.docker.com/repository/docker/llearnell/ubuntu-ml⁠