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MNIST Pytorch Classifier

MNIST simple CNN classifier implementation in pytorch for DTU Machine Learning Operations (MLOps) course.

๐Ÿ’ป Project setup

Start by cloning or downloading this repository

git clone https://github.com/ndpooja/MLOps_mnist.git

To run the project

for project preparation

cd MLOps_mnist
make data  # runs the make_dataset.py file, try it!
make clean  # clean __pycache__ files
make requirements  # install everything in the requirements.txt file

to train the model

make train

to predict the model

make predict

to visualize intermediate layer

python mnist_classifier/visualizations/visualize.py

Project structure

The directory structure of the project looks like this:

โ”œโ”€โ”€ Makefile             <- Makefile with convenience commands like `make data` or `make train`
โ”œโ”€โ”€ README.md            <- The top-level README for developers using this project.
โ”œโ”€โ”€ data
โ”‚   โ”œโ”€โ”€ processed        <- The final, canonical data sets for modeling.
โ”‚   โ””โ”€โ”€ raw              <- The original, immutable data dump.
โ”‚
โ”œโ”€โ”€ docs                 <- Documentation folder
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ index.md         <- Homepage for your documentation
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ mkdocs.yml       <- Configuration file for mkdocs
โ”‚   โ”‚
โ”‚   โ””โ”€โ”€ source/          <- Source directory for documentation files
โ”‚
โ”œโ”€โ”€ models               <- Trained and serialized models, model predictions, or model summaries
โ”‚
โ”œโ”€โ”€ notebooks            <- Jupyter notebooks.
โ”‚
โ”œโ”€โ”€ pyproject.toml       <- Project configuration file
โ”‚
โ”œโ”€โ”€ reports              <- Generated analysis as HTML, PDF, LaTeX, etc.
โ”‚   โ””โ”€โ”€ figures          <- Generated graphics and figures to be used in reporting
โ”‚
โ”œโ”€โ”€ requirements.txt     <- The requirements file for reproducing the analysis environment
|
โ”œโ”€โ”€ requirements_dev.txt <- The requirements file for reproducing the analysis environment
โ”‚
โ”œโ”€โ”€ tests                <- Test files
โ”‚
โ”œโ”€โ”€ mnist_classifier  <- Source code for use in this project.
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ __init__.py      <- Makes folder a Python module
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ data             <- Scripts to download or generate data
โ”‚   โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”‚   โ””โ”€โ”€ make_dataset.py
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ models           <- model implementations, training script and prediction script
โ”‚   โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”‚   โ”œโ”€โ”€ model.py
โ”‚   โ”‚
โ”‚   โ”œโ”€โ”€ visualization    <- Scripts to create exploratory and results oriented visualizations
โ”‚   โ”‚   โ”œโ”€โ”€ __init__.py
โ”‚   โ”‚   โ””โ”€โ”€ visualize.py
โ”‚   โ”œโ”€โ”€ train_model.py   <- script for training the model
โ”‚   โ””โ”€โ”€ predict_model.py <- script for predicting from a model
โ”‚
โ””โ”€โ”€ LICENSE              <- Open-source license if one is chosen

Created using mlops_template, a cookiecutter template for getting started with Machine Learning Operations (MLOps).

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