[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[],[],null,["# Efficient PyTorch training with cloud data\n\nVertex AI Neural Architecture Search has no requirements describing how to\ndesign your trainers. Therefore, choose any training frameworks to build the trainer.\n\nFor PyTorch training with large amounts of data, the best practice is to use the distributed training\nparadigm and to read data from Cloud Storage.\nCheck out the blog post\n[Efficient PyTorch training with Vertex AI](https://cloud.google.com/blog/products/ai-machine-learning/efficient-pytorch-training-with-vertex-ai) for methods to improve the training\nperformance. You can see an overall 6x performance improvement with data on\nCloud Storage using `WebDataset` and choosing `DistributedDataParallel` or\n`FullyShardedDataParallel` distributed training strategies. The training\nperformance using data on Cloud Storage is similar to the training performance using data on\na local disk.\n\nThe prebuilt\n[MNasNet classification example](https://github.com/google/vertex-ai-nas/blob/main/pytorch/classification/cloud_search_main.py)\nhas incorporated these methods into its training pipeline."]]