requested issue tracking

TODOs

[X] Create a git repository
[X] Make sure that all team members have write access to the github repository
[X] Create a dedicated environment for you project to keep track of your packages (using conda)
[X] Create the initial file structure using cookiecutter
[X] Fill out the make_dataset.py file such that it downloads whatever data you need and
[x] Add a model file and a training script and get that running
[X] Remember to fill out the requirements.txt file with whatever dependencies that you are using
[X] Remember to comply with good coding practices (pep8) while doing the project
[x] Do a bit of code typing and remember to document essential parts of your code
[X] Setup version control for your data or part of your data
[x] Construct one or multiple docker files for your code
[x] Build the docker files locally and make sure they work as intended
[x] Write one or multiple configurations files for your experiments
[x] Used Hydra to load the configurations and manage your hyperparameters
[ ] When you have something that works somewhat, remember at some point to to some profiling and see if you can optimize your code
[x] Use wandb to log training progress and other important metrics/artifacts in your code
[x] Use pytorch-lightning (if applicable) to reduce the amount of boilerplate in your code

[x] Revisit your initial project description. Did the project turn out as you wanted?
[ ] Make sure all group members have a understanding about all parts of the project
[x] Create a presentation explaining your project
[X] Uploaded all your code to github
[X] (extra) Implemented pre-commit hooks for your project repository
[not_targeted] (extra) Used Optuna to run hyperparameter optimization on your model