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        "description": "# xt-training\n\n## Description\n\nThis repo contains utilities for training deep learning models in pytorch, developed by [Xtract AI](https://xtract.ai/).\n\n## Installation\n\nFrom PyPI:\n```bash\npip install xt-training\n```\n\nFrom source:\n```bash\ngit clone https://github.com/XtractTech/xt-training.git\npip install ./xt-training\n```\n\n## Usage\n\nSee specific help on a class or function using `help`. E.g., `help(Runner)`.\n\n#### Training a model\n\n##### Using xt-training (High Level)\nFirst, you must define a config file with the necessary items.\nTo generate a template config file, run:\n```bash\npython -m xt_training template path/to/save/dir\n```\nTo generate template files for nni, add the ```--nni``` flag\n\nInstructions for defining a valid config file can be seen at the top of the config file.\n\nAfter defining a valid config file, you can train your model by running:\n```bash\npython -m xt_training train path/to/config.py /path/to/save_dir\n```\n\nYou can test the model by running\n```bash\npython -m xt_training test path/to/config.py /path/to/save_dir\n```\n\n##### Using functional train (Middle Level)\nAs of version >=2.0.0, xt-training has functional calls for the train and test functions\nThis is useful if you want to run other code after training, or want any values/metrics returned after training.\nThis can be called like so:\n```python\nfrom xt_training.utils import functional\n\n# model = \n# train_loader = \n# optimizer = \n# scheduler = \n# loss_fn = \n# metrics = \n# epochs = \n# save_dir = \ndef on_exit(test_loaders, runner, save_dir, model):\n    # Do what you want after training.\n    # As of version >=2.0.0. whatever gets returned here will get returned from the functional call\n    return runner, model\n\nrunner, model = functional.train(\n    save_dir,\n    train_loader,\n    model,\n    optimizer,\n    epochs,\n    loss_fn,\n    val_loader=None,\n    test_loaders=None,\n    scheduler=scheduler,\n    is_batch_scheduler=False, # Whether or not to run scheduler.step() every epoch or every step\n    eval_metrics=metrics,\n    tokenizer=None,\n    on_exit=train_exit,\n    use_nni=False\n)\n\n# Do something after with runner and/or model...\n```\n\nA similar functional call exists for test.\n\n##### Using Runner (Low Level)\nIf you want a little more control and want to define the trianing code yourself, you can utilize the Runner like so:\n```python\nfrom xt_training import Runner, metrics\nfrom torch.utils.tensorboard import SummaryWriter\n\n# Here, define class instances for the required objects\n# model = \n# optimizer = \n# scheduler = \n# loss_fn = \n\n# Define metrics - each of these will be printed for each iteration\n# Either per-batch or running-average values can be printed\nbatch_metrics = {\n    'eps': metrics.EPS(),\n    'acc': metrics.Accuracy(),\n    'kappa': metrics.Kappa(),\n    'cm': metrics.ConfusionMatrix()\n}\n\n# Define tensorboard writer\nwriter = SummaryWriter()\n\n# Create runner\nrunner = Runner(\n    model=model,\n    loss_fn=loss_fn,\n    optimizer=optimizer,\n    scheduler=scheduler,\n    batch_metrics=batch_metrics,\n    device='cuda:0',\n    writer=writer\n)\n\n# Define dataset and loaders\n# dataset = \n# train_loader = \n# val_loader = \n\n# Train\nmodel.train()\nrunner(train_loader)\nbatch_metrics['cm'].print()\n\n# Evaluate\nmodel.eval()\nrunner(val_loader)\nbatch_metrics['cm'].print()\n\n# Print training and evaluation history\nprint(runner)\n```\n\n#### Scoring a model\n\n```python\nimport torch\nfrom xt_training import Runner\n\n# Here, define the model\n# model = \n# model.load_state_dict(torch.load(<checkpoint file>))\n\n# Create runner\n# (alternatively, can use a fully-specified training runner as in the example above)\nrunner = Runner(model=model, device='cuda:0')\n\n# Define dataset and loaders\n# dataset = \n# test_loader = \n\n# Score\nmodel.eval()\ny_pred, y = runner(test_loader, return_preds=True)\n```\n\n\n",
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