{
    "info": {
        "author": "Joris Cadow, Antonio Foncubierta Rodriguez, Matteo Manica",
        "author_email": "joriscadow@gmail.com, antonio.foncubierta@gmail.com, drugilsberg@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "[![Build Status](https://travis-ci.org/PhosphorylatedRabbits/brontes.svg?branch=master)](https://travis-ci.org/PhosphorylatedRabbits/brontes)\n[![codecov](https://codecov.io/gh/PhosphorylatedRabbits/brontes/branch/master/graph/badge.svg)](https://codecov.io/gh/PhosphorylatedRabbits/brontes)\n[![Updates](https://pyup.io/repos/github/PhosphorylatedRabbits/brontes/shield.svg)](https://pyup.io/repos/github/PhosphorylatedRabbits/brontes)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![PyPI version](https://badge.fury.io/py/brontes.svg)](https://badge.fury.io/py/brontes)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/6dc4922e69954e96820dae93ed704736)](https://www.codacy.com/manual/drugilsberg/brontes?utm_source=github.com&utm_medium=referral&utm_content=PhosphorylatedRabbits/brontes&utm_campaign=Badge_Grade)\n\n# Brontes\n\nBrontes is your helping cyclops for pytorch models training.\nIt is based on [pytorch_lightning](https://github.com/williamFalcon/pytorch-lightning)\nand comes with an example script in [examples/mnist/run.py](examples/mnist/run.py) that you can adapt for your needs.\n\nAdditionally, there is an example in [examples/mlflow](examples/mlflow) which describes how to use mlflow with Brontes.\n\n## Just do this\n\nDefine your dataset_loaders as a dictionary: use `train`, `val` and optionally a `test` split:\n\n```python\ndataset_loaders = {\n    'train':\n        torch.utils.data.DataLoader(\n            datasets.MNIST(\n                root=DATA_PATH,\n                train=True,\n                download=True\n            ),\n            batch_size=BATCH_SIZE,\n            shuffle=True,\n            num_workers=NUMBER_OF_WORKERS\n        ),\n    'val':\n        torch.utils.data.DataLoader(\n            datasets.MNIST(\n                root=DATA_PATH,\n                train=False,\n                download=True\n            ),\n            batch_size=BATCH_SIZE,\n            shuffle=True,\n            num_workers=NUMBER_OF_WORKERS\n        )\n}\n```\n\ndefine your acrhitecture as a `torch.nn.Module` (or pick an existing architecture):\n\n```python\nbase_model = brontes.examples.Net()\n```\n\nand wrap it with Brontes:\n\n```python\nbrontes_model = Brontes(\n    model=base_model,\n    loss=torch.nn.NLLLoss(),\n    data_loaders=dataset_loaders,\n    optimizers=optimizer\n)\n```\n\nfinally train the model using `pytorch_lighning`\n\n```python\ntrainer = pl.Trainer(max_nb_epochs=EPOCHS)\ntrainer.fit(brontes_model)\n```\n\n## Development setup\n\nSetup the conda environment\n\n```sh\nconda env create -f conda.yml\n```\n\nActivate it:\n\n```sh\nconda activate brontes\n```",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/PhosphorylatedRabbits/brontes",
        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "brontes",
        "package_url": "https://pypi.org/project/brontes/",
        "platform": "",
        "project_url": "https://pypi.org/project/brontes/",
        "project_urls": {
            "Homepage": "https://github.com/PhosphorylatedRabbits/brontes"
        },
        "release_url": "https://pypi.org/project/brontes/0.0.2/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "Brontes is your helping cyclops for pytorch models training.",
        "version": "0.0.2"
    },
    "last_serial": 5850453,
    "releases": {
        "0.0.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "0070e52f1f940077ae2d7867e3d96f83",
                    "sha256": "21d5a7fe3cb8acfc9a353e4a55651ecc001eb5f507c57d94544939099d45de17"
                },
                "downloads": -1,
                "filename": "brontes-0.0.2.tar.gz",
                "has_sig": false,
                "md5_digest": "0070e52f1f940077ae2d7867e3d96f83",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 4940,
                "upload_time": "2019-09-18T13:59:05",
                "url": "https://files.pythonhosted.org/packages/25/2c/1792ffcecbfff693c46c04d05944394f02a015722d0f85c4a761a0f9c12a/brontes-0.0.2.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "0070e52f1f940077ae2d7867e3d96f83",
                "sha256": "21d5a7fe3cb8acfc9a353e4a55651ecc001eb5f507c57d94544939099d45de17"
            },
            "downloads": -1,
            "filename": "brontes-0.0.2.tar.gz",
            "has_sig": false,
            "md5_digest": "0070e52f1f940077ae2d7867e3d96f83",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 4940,
            "upload_time": "2019-09-18T13:59:05",
            "url": "https://files.pythonhosted.org/packages/25/2c/1792ffcecbfff693c46c04d05944394f02a015722d0f85c4a761a0f9c12a/brontes-0.0.2.tar.gz"
        }
    ]
}