Deep learning frameworks

Last revised by Dr Candace Makeda Moore on 03 May 2021

Deep learning frameworks are instruments for training and validating deep neural networks, through high-level programming interfaces.

Widely used deep learning frameworks include the libraries PyTorch, TensorFlow, and Keras. A programmer can use these libraries of higher functions to quickly define the architecture and the parameters of an artificial neural network without the need of writing many more lines of code in Python (or another language) or explicitly defining processes such as backpropagation.

These particular frameworks rely on other libraries for GPU-acceleration (such as cuDNN, NCCL and DALI) to deliver relatively easy-to-implement high-performance multi-GPU accelerated training. 

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