[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-09-04 (世界標準時間)。"],[],[],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."]]