Abstract: Aspects of the present disclosure provide methods and systems for training a neural network. Embodiments include determining a symmetric key that indicates a scrambled ordering of data components. Embodiments include receiving a plurality of training data instances. Embodiments include training a neural network by, for each respective training data instance of the plurality of training data instances: identifying a training input and a training output of the respective training data instance; identifying a plurality of training input components of the training input; providing the plurality of training input components to an input layer of the neural network based on the scrambled ordering indicated by the symmetric key; receiving an output from the neural network in response to the plurality of training input components; and determining whether to modify one or more parameters of the neural network based on the output and the training output of the respective training data instance.

Abstract: Certain aspects of the present disclosure provide methods and systems for in-platform data storage. Embodiments include receiving data for storage in a distributed computing platform. Embodiments include determining that graphics processing unit (GPU) memory resources are available. Embodiments include storing the data in the GPU memory resources. Embodiments include monitoring demand for the GPU memory resources in the distributed computing platform. Embodiments include identifying a contention for the GPU memory resources. Embodiments include evacuating the data from the GPU memory resources based on the contention.