How to Successfully Achieve Multinode Training in PyTorch
Blog post from Speechmatics
Multinode training, where multiple GPUs are used to train large neural networks, can be an effective way to speed up training time but requires careful implementation to avoid harming performance. To achieve this, companies must consider factors such as the number of nodes needed, networking setup, and containerization. Using InfiniBand with Remote Direct Memory Access (RDMA) can provide near-linear scaling across nodes, but requires careful software versioning and debugging. Additionally, errors can still occur during training, so it's essential to set up robust error handling mechanisms, such as webhooks, trap commands, and cleanup functions, to minimize downtime and increase the uptime of training runs.
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