Take control back!
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Minimum inversion of control. Your code calls framework code, not vice versa. This approach provides almost same flexibility as pure PyTorch, but allows solving common problems like metrics, checkpoints, progress.
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Callbacks only for misc functionality (checkpoints, tensorboard, benchmarking, debug etc).
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No oversimplification of most common cases like supervised learning. Other libraries out there do this, and as a result they have complex messy internals and low flexibility for not-so-common usecase, like multiple optimizers or multiple backward passes for single batch.
- Flexible metrics saving to tensorboard (batch/epoch/whatever)
- DDP
- Best model checkpoint by metric
- Automatic logdir naming
- Callback for checking that repo has no uncommitted changes
- Development mode to check pipeline fast
- Nice TQDM progress-bar that correctly handles printing
- Simple and clear API. Also, it's quite future-proof and will have minimum breakage over future development
- Comprehensive internals. Library code is clean and easy to understand, which prevents bugs and simplifies debugging if bug still happened
- DeepSpeed support
- Logging abstraction to seamlessly work with multiple sinks (eg tensorboard and WandB)
- Training stages/phases
- Rich configuration API with CLI support
- Train resuming
pip install git+https://github.com/hexfaker/matchesExamples are located in examples/ dir.
- DCGAN example has complex batch handling, image saving to tensorboard and configs
- CIFAR example is well-commented and has OneCycle scheduler.