OoM issue with multiple gpus using Distributed Data Parallel (DDP) training

[SebastienThibert]

When I run this example runs on multiple gpus using Distributed Data Parallel (DDP) training on AWS SageMaker with 4 GPUS and a batch_size of 8192, I got a OoM issue despite the 96GiB capacity:

Tried to allocate 4.00 GiB. GPU 2 has a total capacity of 21.99 GiB of which 1.21 GiB is free. Including non-PyTorch memory, this process has 20.77 GiB memory in use.

[guarin]

Hi, batch size 8192 is quite big even for 4 GPUs (the original paper used batch size 4096 on 128 TPUs). Are you using CIFAR (32x32) or normal ImageNet sized images (224x224)?

[SebastienThibert]

I use exactly the code from the example so 32x32 I think

cordialement, Sébastien Thibert

Le ven. 20 sept. 2024, 16:55, guarin @.***> a écrit :

Hi, batch size 8192 is quite big even for 4 GPUs (the original paper used batch size 4096 on 128 TPUs). Are you using CIFAR (32x32) or normal ImageNet sized images (224x224)?

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[guarin]

I just tested it on 4x24GB GPUs and it indeed fails with OOM. I had to reduce the batch size to 4096 for it to succeed. I think this is expected. Please note that the batch size is per GPU as it is set directly in the dataloader. You can train with larger batch sizes if you set precision="16-mixed" to enable half precision.

[SebastienThibert]

Ok, I thought we could split the batch on all the GPUs. So any other tips to increase the batch size ?