Neural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance
Neural networks training pipeline based on PyTorch. Designed to standardize training process and accelerate experiments.
- Core is about 2K lines, covered by tests, that you don't need to write again
- Flexible and customizable training process
- Checkpoints management and train process resuming (source and target device independent)
- Metrics processing and visualization by builtin (tensorboard, Matplotlib) or custom monitors
- Training best practices (e.g. learning rate decaying and hard negative mining)
- Metrics logging and comparison (DVC compatible)
See the examples
- MNIST classification - notebook, file, Kaggle kernel
- Segmentation - notebook, file
- Resume training process - file
PiePline short overview:
import torch from neural_pipeline.builtin.monitors.tensorboard import TensorboardMonitor from neural_pipeline.monitoring import LogMonitor from neural_pipeline import DataProducer, TrainConfig, TrainStage,\ ValidationStage, Trainer, FileStructManager from somethig import MyNet, MyDataset fsm = FileStructManager(base_dir='data', is_continue=False) model = MyNet().cuda() train_dataset = DataProducer([MyDataset()], batch_size=4, num_workers=2) validation_dataset = DataProducer([MyDataset()], batch_size=4, num_workers=2) train_config = TrainConfig(model, [TrainStage(train_dataset), ValidationStage(validation_dataset)], torch.nn.NLLLoss(), torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.5)) trainer = Trainer(train_config, fsm, torch.device('cuda:0')).set_epoch_num(50) trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm, is_continue=False))\ .add_monitor(LogMonitor(fsm)) trainer.train()
This example of training MyNet on MyDataset with vizualisation in Tensorflow and with metrics logging for further experiments comparison.
pip install piepline
builtin module using install:
pip install tensorboardX matplotlib
Install latest version before it's published on PyPi
pip install -U git+https://github.com/PiePline/piepline
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