{ "info": { "author": "Yasufumi Taniguchi", "author_email": "yasufumi.taniguchi@gmail.com", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8" ], "description": "# Torchpipe: Simple ETL Pipeline for PyTorch\n\nTorchpipe is a simple ETL framework, especially for PyTorch.\nIt is an alternative to [tf.data](https://www.tensorflow.org/api_docs/python/tf/data) in TensorFlow\n\n\n# Requirements\n\n- Python 3.6+\n- PyTorch 1.2+\n\n\n# Installation\n\nTo install Torchpipe:\n\n```bash\npip install torchpipe\n```\n\n\n# Basic Usage\n\n```py\nimport torchpipe as tp\n\nd = tp.Dataset(range(1_000))\nd.shuffle(buffer_size=100).batch(batch_size=10).first() # [4, 44, 71, 92, 97, 86, 43, 57, 60, 62]\n```\n\n# Usage with PyTorch\n\n```py\nfrom torch.utils.data import DataLoader\nimport torchpipe as tp\n\n\nd = tp.Dataset(range(1_000)).shuffle(100).batch(10)\nloader = DataLoader(d, num_workers=4, collate_fn=lambda x: x)\nfor x in loader:\n ...\n```\n\n# Usage with LineFlow\n\nYou can use Torchpipe with pre-defined datasets in [LineFlow](https://github.com/tofunlp/lineflow):\n\n```py\nfrom torch.utils.data import DataLoader\nfrom lineflow.datasets.wikitext import cached_get_wikitext\nimport torchpipe as tp\n\ndataset = cached_get_wikitext('wikitext-2')\n# Preprocessing dataset\ntrain_data = tp.Dataset(dataset['train']) \\\n .flat_map(lambda x: x.split() + ['']) \\\n .window(35) \\\n .parallel() \\\n .shuffle(64 * 100) \\\n .batch(64)\n\n# Iterating dataset\nloader = DataLoader(train_data, num_workers=4, collate_fn=lambda x: x)\nfor x in loader:\n ...\n```", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/yasufumy/torchpipe", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "torchpipe", "package_url": 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