Successfully configured the dataloader and trained for one epoch. Results are not so good, but it's something. Still the Fastaiv1 looked better qualitatively

Finished training of model, saving before qualitative testing. Seems model has actually learned something. Need to add metrics and params to the pipeline.

Fixed a bug in the training stage where the model was not saved, commiting before training on colab

Training stage seems to work, creating a non-run commit to use colab as an orchestration machine

Seems like we are now using the correct format for fastai2. Still there is a strange bug where the signal is killed in training

Merge branch 'split_commands_in_readme' of OperationSavta/SavtaDepth into pipeline-setup

*Split commands of preparing environment in README file > *added -y for conda env creation for less interactions for the user

Migrated to fastai2, creating the DataLoader now works but I'm stuck on not being able to change the batch_size or num_workers as the interface seems to have changed

Hopefully finished with the requirements debacle, now using conda but freezing requirements with pip as usual

Fixed requirements.txt to comply with conda format

Update readme to include aws cli

Finished data import and processing setup, bug in training step

Transition to MLWorkspace docker and setup makefile with environment commands

Merge branch 'master' of https://dagshub.com/OperationSavta/SavtaDepth

Added notebook for baseline - starting conversion to python modules + pipeline

Fix to do in readme

Initial commit for setting up DS environment

added BTS demo as a qualitative baseline for SavtaDepth