How do you implement distributed deep learning in Azure ML?

Distributed deep learning in Azure ML is implemented using multi-node GPU clusters and frameworks such as PyTorch Distributed, TensorFlow Distributed, Horovod, or DeepSpeed.

Modern deep learning models often require enormous computational resources and training datasets. Azure ML supports distributed training strategies that parallelize workloads across multiple GPUs or nodes.

Common distributed strategies include:

Azure ML supports orchestration for: