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    "info": {
        "author": "Anton Fedotov",
        "author_email": "anton.fedotov.af@gmail.com.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# Neural Piepline\n\nNeural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance.\n\n[![Build Status](https://travis-ci.org/toodef/neural-pipeline.svg?branch=master)](https://travis-ci.org/toodef/neural-pipeline)\n[![Coverage Status](https://coveralls.io/repos/github/toodef/neural-pipeline/badge.svg?branch=master)](https://coveralls.io/github/toodef/neural-pipeline?branch=master)\n[![Maintainability](https://api.codeclimate.com/v1/badges/1feaafcc614adf27c30f/maintainability)](https://codeclimate.com/github/toodef/neural-pipeline/maintainability)\n\n* Core is about 2K lines, covered by tests, that you doesn't need to write again\n* Flexible and customizable training process\n* Checkpoints management and train process resuming (source and target device independent)\n* Metrics processing and visualization by builtin ([tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard), [Matplotlib](https://matplotlib.org)) or custom monitors\n* Training best practices (e.g. learning rate decaying and hard negative mining)\n* Metrics logging and comparison (DVC compatible)\n\n# Train MNIST example:\nThis code run MNIST image classification with Tensorboard monitoring. Code based on PyTorch [example](https://github.com/pytorch/examples/blob/master/mnist/main.py).\n\nSee full example [there](https://github.com/toodef/neural-pipeline/blob/master/examples/files/img_classification.py).\n```python\nfrom neural_pipeline.builtin.monitors.tensorboard import TensorboardMonitor\nfrom neural_pipeline import DataProducer, AbstractDataset, TrainConfig, TrainStage,\\\n    ValidationStage, Trainer, FileStructManager\n\nimport torch\nfrom torch import nn\nfrom torchvision import datasets, transforms\n\nclass Net(nn.Module):\n    # Network implementation\n\nclass MNISTDataset(AbstractDataset):\n    transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n\n    def __init__(self, data_dir: str, is_train: bool):\n        self.dataset = datasets.MNIST(data_dir, train=is_train, download=True)\n\n    def __len__(self):\n        return len(self.dataset)\n\n    def __getitem__(self, item):\n        data, target = self.dataset[item]\n        return {'data': self.transforms(data), 'target': target}\n\nfsm = FileStructManager(base_dir='data', is_continue=False)\nmodel = Net()\n\ntrain_dataset = DataProducer([MNISTDataset('data/dataset', True)], batch_size=4, num_workers=2)\nvalidation_dataset = DataProducer([MNISTDataset('data/dataset', False)], batch_size=4, num_workers=2)\n\ntrain_config = TrainConfig([TrainStage(train_dataset), ValidationStage(validation_dataset)], torch.nn.NLLLoss(),\n                           torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.5))\n\ntrainer = Trainer(model, train_config, fsm, torch.device('cuda:0')).set_epoch_num(50)\ntrainer.monitor_hub.add_monitor(TensorboardMonitor(fsm, is_continue=False))\ntrainer.train()\n```\n\n# Installation:\n[![PyPI version](https://badge.fury.io/py/neural-pipeline.svg)](https://badge.fury.io/py/neural-pipeline)\n[![PyPI Downloads/Month](https://pepy.tech/badge/neural-pipeline/month)](https://pepy.tech/project/neural-pipeline)\n[![PyPI Downloads](https://pepy.tech/badge/neural-pipeline)](https://pepy.tech/project/neural-pipeline)\n\n`pip install neural-pipeline`\n\n##### For `builtin` module using install:\n`pip install tensorboardX matplotlib`\n\n##### Install latest version before it's published on PyPi\n`pip install -U git+https://github.com/toodef/neural-pipeline`\n\n# Getting started:\n### Documentation\n[![Documentation Status](https://readthedocs.org/projects/neural-pipeline/badge/?version=master)](https://neural-pipeline.readthedocs.io/en/master/?badge=master)\n[See the full documentation there](https://neural-pipeline.readthedocs.io/en/master/)\n\nData flow scheme:\n![Data flow](https://github.com/toodef/neural-pipeline/blob/master/docs/img/data_flow.svg)\n\n### See the examples\n* MNIST classification - [notebook](https://github.com/toodef/neural-pipeline/blob/master/examples/notebooks/img_classification.ipynb), [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/img_classification.py)\n* Segmentation - [notebook](https://github.com/toodef/neural-pipeline/blob/master/examples/notebooks/img_segmentation.ipynb), [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/img_segmentation.py)\n* Resume training process - [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/resume_train.py)\n\n\n\n\n\n",
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