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I trained model while setting 'args.ckpt_format = torch_dist', and the checkpoint files saved like '__0_.distcp, ..., common.pt, metadata.json'.
When i resume training, load_checkpoint works well.
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Is it possible to do Distributed training on multiple GPUs and machines using SciANN?
Like can something like horovod, tf distributed etc be used readily?
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Hi, thanks for the nice work!
I tried to implement your code but found that the training was very slow. I saw that you use distributed training in the code. Could you kindly provide more info on your…
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**Describe the bug**
I encountered the error "OverflowError: int too big to convert" when trying to run `ilab model train` on my local system.
**To Reproduce**
Steps to reproduce the behavior:
1…
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[torch-neuronx] FSDP support - Distributed Training on Trn1
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### Description
Multi-node multi-*PU training. This is required for really scaling our use of the data pipeline for big predictions and given the construction of the pipeline as it exists, we just …
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As the title described, does standalone mode support multiple GPUs to speed up training?
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worker-1: File "loader.py", line 163, in get_dataset
worker-1: with training_args.main_process_first(desc="pre-process dataset"):
worker-1: File "/usr/local/python3.10.12/lib/python3.10/cont…
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Related: https://github.com/kubeflow/training-operator/issues/2170
We should create `ClusterTrainingRuntime` for PyTorch multi-node distributed training.
/area runtime
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Now, I want to run the graphsage distributed code in the examples/distributed directory, but I don’t have an actual machine, so I used vmware to build three virtual machines as nodes for distributed t…