Model parallelism

Model parallelism is another way to scale the model training in deep learning modeling. Model parallelism is heavily compute-based and, in most cases, GPU-based computing is needed to get better performance and time. Let’s look at some distributed training libraries available for us to use in the Azure Machine Learning service.

In Azure Machine Learning, we can perform distributed learning in various ways:

• Distributed training with PyTorch: PyTorch is one of the most well-known and widely used machine learning libraries for large-scale vision, text, and other unstructured data machine learning. It uses deep learning, such as convolutional neural network or recurrent neural network-based development. PyTorch is a deep learning framework developed by Meta (Facebook).

PyTorch implementations are very simple and easy to use and tend to eliminate the complications of other libraries in the marketplace.

• Distributed training with TensorFlow...