Interactive Neural Network Design on Rescale

Rescale Deep Learning Cloud introduces an interactive notebook feature that facilitates iterative deep learning workflows by enabling users to switch between interactive data preprocessing and batch neural network training. The guide provides a detailed example using the CIFAR10 image classification dataset, demonstrating how to start a Rescale Linux Desktop with a NVIDIA K80 GPU, attach TensorFlow software, and utilize Jupyter Notebook for network training. Users are shown how to adapt TensorFlow's CIFAR10 training example, run different convolutional layer configurations, and observe training progress using TensorBoard via SSH tunnel. The process allows launching batch training jobs directly from the notebook, with a focus on comparing performance between two-layer and three-layer convolutional networks. The ability to iterate development in a consistent environment with the batch training cluster minimizes configuration discrepancies, enhancing reliability. For more intensive workloads, Rescale offers configurations supporting up to eight K80 GPUs, and users are encouraged to explore this workflow by signing up for the service.