FAQ

We answer some of the simple and frequently asked questions about the framework below. If you think of any other question that should be in this list, send a mail to one of the maintainers, and it will be added here.

What it is…

  • This framework is used to simplify the exploration, development, and testing of models that you create yourself, or that you import from other libraries or frameworks.

  • This framework is used to enforce good reproducibility standards for your experiments via the use of global configuration files and checkpoints.

  • This framework is used to easily swap, split, scale, combine, and augment datasets used in your experiments.

  • This framework can help you fine-tune, debug, visualize, and understand the behavior of your models more easily.

  • This framework is NOT used to obtain off-the-shelf solutions. In most cases, you will have to put in some work by at least modifying pre-existing configuration files.

What it supports…

  • PyTorch. For now, the entire backend is based on the design patterns, interfaces, and concepts of the PyTorch library ([more info]).

  • Image classification, segmentation, object detection, super-resolution, and generic regression tasks. More types of tasks are planned in the near future.

  • Live evaluation and monitoring of predefined metrics. The framework implements [several types of metrics], but custom metrics can also be defined and evaluated at run time.

  • Data augmentation. The framework implements basic [transformation operations and wrappers] for large augmentation libraries such as albumentations ([more info]).

  • Model fine-tuning and exportation. Models obtained from the torchvision package ([more info]) or pre-trained using the framework can be loaded and fine-tuned directly for any compatible task. They can also be exported in PyTorch-JIT/ONNX format for external inference.

  • Tensorboard. Event logs are generated using tensorboardX ([more info]) and may contain plots, visualizations, histograms, graph module trees and more.

How do I…

This section is still a work in progress; see the use case examples [here] for a list of code snippets and tutorials on how to use the framework. For high-level documentation, refer to the [user guide].