✨PyTorch Distributed: Experiences on Accelerating Data Parallel Training
📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.
🔹 Publication Date: Published on Jun 28, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC
📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.
🔹 Publication Date: Published on Jun 28, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC