Distributed deep learning model training is becoming increasingly important as data sizes are growing in many industries. Many applications in computer vision and natural language processing now require training of deep learning models, which are growing exponentially in complexity and are often trained with hundreds of terabytes of data. It then becomes important to use a vast cloud infrastructure to scale the training of such large models.
Developers can use open-source frameworks such as PyTorch to easily design intuitive model architectures. However, scaling the training of these models across multiple nodes can be challenging due to increased orchestration complexity.
Distributed model training mainly consists of two paradigms:

Model parallel – In model parallel training, the model itself is so large that it can’t fit in the memory of a single GPU, and multiple GPUs are needed to train the model. The Open AI’s GPT-3 model with 175 billion

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