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    "info": {
        "author": "Jeff Hilton",
        "author_email": "jeffhilton.code@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "# Trainable: The Flexible PyTorch Training Toolbox\n\nIf you're sick of dealing with all of the boilerplate code involved in training, evaluation, visualization, and\npreserving your models, then you're in luck. Trainable offers a simple, yet extensible framework to make understanding\nthe latest papers the *only* headache of Neural Network training.\n\n## Installation\n```bash\npip install trainable\n```\n\n## Usage\nThe typical workflow for trainable involves defining a callable Algorithm to describe how to train \nyour network on a batch, and how you'd like to label your losses:\n\n```python\nclass MSEAlgorithm(Algorithm):\n    def __init__(self, eval=False, **args):\n        super().__init__(eval)\n        self.mse = nn.MSELoss()\n\n    def __call__(self, model, batch, device):\n        x, target = batch\n        x, target = x.to(device), target.to(device)\n        y = model(x)\n\n        loss = self.mse(y, target)\n        loss.backward()\n\n        metrics = { self.key(\"MSE Loss\"):loss.item() }\n        return metrics\n```\n\nThen you simply instantiate your model, dataset, and optimizer...\n\n```python\ndevice = torch.device('cuda')\n\nmodel = MyModel().to(device)\noptim = FancyPantsOptimizer(model.parameters(), lr=1e-4)\n\ntrain_data = DataLoader(SomeTrainingDataset('path/to/your/data'), batch_size=32)\ntest_data = DataLoader(SomeTestingDataset('path/to/your/data'), batch_size=32)\n```\n\n...and let trainable take care of the rest!\n```python\ntrainer = Trainer(\n  visualizer=MyVisualizer(),  # Typically Plotter() or Saver()\n  train_alg=MyFancyAlgorithm(),\n  test_alg=MyFancyAlgorithm(eval=True)\n  display_freq=1,\n  visualize_freq=10,\n  validate_freq=10,\n  autosave_freq=10,\n  device=device\n)\n\nsave_path = \"desired/save/path/for/your/session.sesh\"\ntrainer.start_session(model, optim, path)\n\ntrainer.name_session('Name')\n\ntrainer.describe_session(\"\"\"\nA beautiful detailed description of what the heck \nyou were trying to accomplish with this training.\n\"\"\")\n\nmetrics = trainer.train(train_data, test_data, epochs=200)\n```\n\nPlotting your data is simple as well:\n```python\nimport matplotlib.pyplot as plt\n\nfor key in metrics:\n    plt.plot(metrics[key])\n    plt.show()\n```\n\n## Tunable Options\nThe Trainer interface gives you a nice handful of options to configure your training experience.\nThey include:\n* **Display Frequency:** How often (in batches) information such as your training loss is updated in your progress bar.\n* **Visualization Frequency:** How often (in batches) the training produces a visualization of your model's outputs. \n* **Validation Frequency:** How often (in epochs) the trainer performs validation with your test data.\n* **Autosave Frequency:** How often your session is saved out to disk. \n* **Device:** On which hardware your training should occur.\n\n## Customization\nDo you want a little more granularity in how you visualize your data? Or perhaps\nrunning an epoch with your model is a little more involved than just training\non each batch of data? Wondering why the heck pytorch doesn't have a built-in dataset for unsupervised images?\nMaybe your training algorithm involves VGG? Got you covered. Check out the source for the various submodules:\n* [trainable.visualize](https://github.com/hiltonjp/trainable/blob/master/trainable/visualize.py) -- for customizing visualization.\n* [trainable.epoch](https://github.com/hiltonjp/trainable/blob/master/trainable/epoch.py) -- for customizing epochs.\n* [trainable.data](https://github.com/hiltonjp/trainable/tree/master/trainable/data) -- for common datasets and transforms\n    not found in pytorch's modules.\n* [trainable.features](https://github.com/hiltonjp/trainable/tree/master/trainable/features) -- for working with intermediate\n    activations and features, such as with VGG-based losses.\n\n## Contributing\nFind any other headaches in neural net training that you think you can simplify with Trainable? Feel free to make a\npull request from my [github repo](https://github.com/hiltonjp/trainable). \n\n## Contact\nEmail me anytime at [jeffhilton.code@gmail.com.]()\n\n\n",
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