Note: The purpose of the project to explore the libraries and learn how to use them. Not to build a SOTA model.

Requirements:

This project uses Python 3.8

Create a virtual env with the following command:

conda create --name project-setup python=3.8
conda activate project-setup

Install the requirements:

pip install -r requirements.txt

Running

Training

After installing the requirements, in order to train the model simply run:

python train.py

Monitoring

Once the training is completed in the end of the logs you will see something like:

wandb: Synced 5 W&B file(s), 4 media file(s), 3 artifact file(s) and 0 other file(s)
wandb: 
wandb: Synced proud-mountain-77: https://wandb.ai/raviraja/MLOps%20Basics/runs/3vp1twdc

Follow the link to see the wandb dashboard which contains all the plots.

Inference

After training, update the model checkpoint path in the code and run

python inference.py

Running notebooks

I am using Jupyter lab to run the notebooks.

Since I am using a virtualenv, when I run the command jupyter lab it might or might not use the virtualenv.

To make sure to use the virutalenv, run the following commands before running jupyter lab

conda install ipykernel
python -m ipykernel install --user --name project-setup
pip install ipywidgets