{ "info": { "author": "Kakao Brain", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Typing :: Typed" ], "description": "torchgpipe\n==========\n\nA GPipe_ implementation in PyTorch_.\n\n.. _GPipe: https://arxiv.org/abs/1811.06965\n.. _PyTorch: https://pytorch.org/\n\n.. sourcecode:: python\n\n from torchgpipe import GPipe\n\n model = nn.Sequential(a, b, c, d)\n model = GPipe(model, balance=[1, 1, 1, 1], chunks=8)\n\n for input in data_loader:\n output = model(input)\n\nWhat is GPipe?\n~~~~~~~~~~~~~~\n\nGPipe is a scalable pipeline parallelism library published by Google Brain,\nwhich allows efficient training of large, memory-consuming models. According to\nthe paper, GPipe can train a 25x larger model by using 8x devices (TPU), and\ntrain a model 3.5x faster by using 4x devices.\n\n`GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism\n`_\n\nGoogle trained AmoebaNet-B with 557M parameters over GPipe. 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