Noggin is a simple Python tool for ‘live’ logging and plotting measurements during experiments. Although Noggin can be used in a general context, it is designed around the train/test and batch/epoch paradigm for training a machine learning model.
Noggin’s primary features are its abilities to:
- Log batch-level and epoch-level measurements by name
- Seamlessly update a ‘live’ plot of your measurements, embedded within a Jupyter notebook
- Organize your measurements into a data set of arrays with labeled axes, via xarray
- Save and load your measurements & live-plot session: resume your experiment later without a hitch
You can read more about Noggin here
