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when I run the code in ubuntu, which has more than one gpu, it appeared the below error:
(pytorch) m11113013@htsc-Server3:~/ProjectCode/MasterProject2$ python -m torch.distributed.run --standalone …
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I have two servers configured as follows: Server-1 with 6 GPUs and Server-2 with 4 GPUs, each GPU having 24GB of VRAM. I'm attempting to load the LLaMA-3.1-70B model across both servers using DeepSpee…
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### 软件环境
```Markdown
- paddlepaddle:
- paddlepaddle-gpu: 基于131999233ef997fc8d3f24b27830925b78cf17aa(比2.6.1新)的我们自己适配在musa的版本
- paddlenlp: https://github.com/ZHUI/PaddleNLP/tree/sci/benchmark comm…
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### System Info
- `transformers` version: 4.44.2
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.8.0
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.4
- Accelerate version…
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Huawei’s OPEN submission utilized local SSDs on the benchmark hosts, with an aggregated capacity of 12TiB per node.
The submission indicates that 7 accelerators (H100, 3DUnet) were simulated per be…
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### Description & Motivation
I have on gpu augmentations I perform via some custom `torch.nn.Modules` I define in my `LightningDataModule`. How can I ensure these get moved to the correct device duri…
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The script mentioned in https://github.com/pytorch/examples/tree/master/imagenet does provides good guideline on single node training however it doesn't have good documentation on Distributed training…
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Hi all,
thanks for such an elegant and performant algorithm!
I wanna know how long it takes for you to perform a from-scratch `mt80` offline training using current code?
in the original paper, …
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There is some code may be wrong in lavis/runners/runner_base.py:
```
def model(self):
if self._model.device != self.device:
self._model = self._model.to(self.device)
…
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training any model that uses layerdrop (probabilistically skip transformer layers during training) works poorly with c10d distributed training backend (using fairseq). in pytorch 1.4 training would fr…