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DL models are very complex and large, and the DL framework is an interface, library, or a tool which allow developers to building DL easily and quickly, without requiring in-depth understanding of all the details of the underlying algorithms. These frameworks provide a clear and concise way for defining models using a collection of pre-built and pre-optimized components. The popular DL framework includes TensorFlow, Keras, PyTorch, Caffe so on
Below are some key characteristics of a well-designed DL framework:
Training with large datasets and deep learn networks can be accelerated by using multiple GPUs and/or more servers, but only if the underlying infrastructure is architecturally correctly.
In the market, there are some popular platforms and toolkits to allow developers to test distributed execution of different DL platforms on GPU clusters including MPI based Uber Horovod and Microsoft website Microsoft Distributed Machine Learning Toolkit (DMTK). Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed DL fast and easy to use. These platforms are designed to make large-scale parallel distributed DL jobs easy and better.