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    "info": {
        "author": "Kai Chen",
        "author_email": "chenkaidev@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Utilities"
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        "description": "# torchpack\n\n[![PyPI Version](https://img.shields.io/pypi/v/torchpack.svg)](https://pypi.python.org/pypi/torchpack)\n\nTorchpack is a set of interfaces to simplify the usage of PyTorch.\n\nDocumentation is ongoing.\n\n\n## Installation\n\n- Install with pip. \n```\npip install torchpack\n```\n- Install from source.\n```\ngit clone https://github.com/hellock/torchpack.git\ncd torchpack\npython setup.py install\n```\n\n**Note**: If you want to use tensorboard to visualize the training process, you need to\ninstall tensorflow([`installation guide`](https://www.tensorflow.org/install/install_linux)) and tensorboardX(`pip install tensorboardX`).\n\n## What can torchpack do\n\nTorchpack aims to help users to start training with less code, while stays\nflexible and configurable. It provides a `Runner` with lots of `Hooks`.\n\n## Example\n\n```python\n######################## file1: config.py #######################\nwork_dir = './demo'  # dir to save log file and checkpoints\noptimizer = dict(\n    algorithm='SGD', args=dict(lr=0.001, momentum=0.9, weight_decay=5e-4))\nworkflow = [('train', 2), ('val', 1)]  # train 2 epochs and then validate 1 epochs, iteratively\nmax_epoch = 16\nlr_policy = dict(policy='step', step=12)  # decrese learning rate by 10 every 12 epochs\ncheckpoint_cfg = dict(interval=1)  # save checkpoint at every epoch\nlog_cfg = dict(\n    # log at every 50 iterations\n    interval=50,\n    # two logging hooks, one for printing in terminal and one for tensorboard visualization\n    hooks=[\n        ('TextLoggerHook', {}),\n        ('TensorboardLoggerHook', dict(log_dir=work_dir + '/log'))\n    ])\n\n######################### file2: main.py ########################\nimport torch\nfrom torchpack import Config, Runner\nfrom collections import OrderedDict\n\n# define how to process a batch and return a dict\ndef batch_processor(model, data, train_mode):\n    img, label = data\n    label = label.cuda(non_blocking=True)\n    pred = model(img)\n    loss = F.cross_entropy(pred, label)\n    accuracy = get_accuracy(pred, label_var)\n    log_vars = OrderedDict()\n    log_vars['loss'] = loss.item()\n    log_vars['accuracy'] = accuracy.item()\n    outputs = dict(loss=loss, log_vars=log_vars, num_samples=img.size(0))\n    return outputs\n\ncfg = Config.from_file('config.py')  # or config.yaml/config.json\nmodel = resnet18()\nrunner = Runner(model, cfg.optimizer, batch_processor, cfg.work_dir)\nrunner.register_default_hooks(lr_config=cfg.lr_policy,\n                              checkpoint_config=cfg.checkpoint_cfg,\n                              log_config=cfg.log_cfg)\n\nrunner.run([train_loader, val_loader], cfg.workflow, cfg.max_epoch)\n```\n\nFor a full example of training on ImageNet, please see `examples/train_imagenet.py`.\n\n```shell\npython examples/train_imagenet.py examples/config.py\n```\n\n",
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        "license": "MIT",
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        "name": "torchpack",
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