DistBelief is an early, large-scale distributed deep learning system introduced by researchers in 2012. It serves as an important historical precursor in the evolution of deep learning tooling, having preceded more modern, widely adopted frameworks such as Caffe and TensorFlow. While it paved the way for distributed training, the lack of such mature computational tools at the time made implementing deep architectures like AlexNet a highly labor-intensive process.