{"id":18395765,"url":"https://github.com/x-raylaser/torchassistant","last_synced_at":"2026-04-28T18:02:21.345Z","repository":{"id":44096194,"uuid":"499387999","full_name":"X-rayLaser/TorchAssistant","owner":"X-rayLaser","description":"A deep learning framework and automation tool built on top of 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Introduction\n\n**TorchAssistant** is a deep learning framework built on top of **PyTorch**. \nIt provides a set of tools to automate the training and evaluation of models. \nIt also reduces the amount of trivial code one usually needs to write.\n\nJust create a specification file configuring the training session and let \nthe framework do everything else for you.\n\nMain features:\n- scripts for training, evaluation, and inference\n- automatically calculating metrics\n- automatically saving training session\n- resuming the interrupted training session\n- automatically saving metrics history to CSV files\n- nicely formatted information about the training session: epoch, iteration, loss, metrics, etc.\n- highly flexible and customizable\n- support for building complex training pipelines\n\n# Status: Early development stage\n\nThis project is in the early stage of development.\nFeatures and functionality provided here are subject to change.\nFurthermore, the code is not yet extensively tested and may contain bugs.\n\n# Prerequisites\n\nThis project has dependencies that require separate installation:\n- PyTorch (version \u003e= 1.10.1, \u003c 2.0)\n- Torchvision (version \u003e= 0.11.2, \u003c 0.12)\n- TorchMetrics (version \u003e= 0.7.2 \u003c 0.8)\n\nWhen possible, try to follow the recommended version range specified in parentheses.\n\nYou can install PyTorch and Torchvision together from \n[here](https://pytorch.org/get-started/locally/).\nAnd you can install TorchMetrics from \n[here](https://torchmetrics.readthedocs.io/en/stable/pages/quickstart.html).\n\n# Installation\n\n```\npip install torchassistant\n```\n\n# Examples\n\nThe examples directory contains projects that demonstrate how to use\nTorchAssistant to train different kinds of neural networks.\n\n# Documentation\n\nYou can find all the documentation for the project \n[here](https://github.com/X-rayLaser/TorchAssistant/wiki).\n\n# License\n\nThis project has an MIT 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