piepline 0.3.2

Neural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance

PiePline

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)

Getting started:

Documentation

| Stable: | Latest: |

• See the full documentation there
• Read getting started guide

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.

Installation:

pip install piepline

For 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