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Platform For AI:Use cloud storage for a DLC training job

Last Updated:Nov 13, 2024

When submitting a training job in Deep Learning Containers (DLC), you can use Object Storage Service (OSS), File Storage NAS (NAS), Cloud Parallel File Storage (CPFS), or MaxCompute storage by code or mounting. This enables direct data read from and write to the storage during training. This topic describes how to configure OSS, NAS, CPFS, or MaxCompute storage for a DLC job.

Prerequisites

Use OSS

Configure OSS by mounting

You can mount OSS dataset when creating a job. The following table describes supported mounting type. For more information, see Submit training jobs.image

Mounting type

Description

Data Set

Mount a custom or public dataset. Public datasets only support read-only mounting. Select a dataset of the OSS type and configure Mount Path. During the DLC job, the system can access OSS data based on this path.

Direct mount

Mount a path in the OSS bucket.

DLC uses JindoFuse to mount OSS. The default DLC configurations may have limitations (For more information, see JindoFuse) and may not be suitable for all scenarios. Take the following steps to adjust the parameters to meet your requirements. For more information, see JindoFuse

Configure OSS without mounting

DLC jobs can read and write OSS data by using OSS Pytorch Connector or OSS SDK. You can configure code builds when creating a job. For code samples, see OSS Connector for AI/ML or OSS SDK.image

Use NAS or CPFS

You can mount NAS or CPFS datasets when creating a job. For more information, see Use NAS.image

Use MaxCompute storage

You can configure code builds to use MaxCompute when creating a job. For code samples, see Use MaxCompute.image