This implementation can either be used via command line or via Python API for easy use from Jupyter Notebooks or Python applications. The focus is on a good user experience with the most important parameters to tweak. More finegrained tuning options will be added over time, but always putting a good user experience first.
Usage is similar for command line and high-level Python API.
Starting a project is as easy as:
- Create a new project:
$ python main.py init /path/to/project
- Edit your config (e.g. where's your training data?):
$ editor /path/to/project/config.yml
- Start (or resume) training:
$ python main.py train /path/to/project
If training is interrupted, you can simply start training again. Training will resume at the latest checkpoint:
$ python main.py train /path/to/project
/path/to/dataset
/path/to/results
|- config.yml // All configuration to run, resume and reproduce this project
|- samples/
| |- fake-0000000.png
| |- fake-0001040.png
| \ ...
|- checkpoints/
| |- snapshot-001040.pkl
| |- snapshot-004120.pkl
| \ ...
|- training.log
|- events.tfevents
\ ...