Automated hardware resource optimization

An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory. The software code also optimizes, based on the determined batch size and the tuned second performance parameter, a process flow for performing the data processing, and generates a configuration file identifying the computing hardware, the neural network based application, the determined batch size, the tuned second performance parameter, and the optimized process flow.

BACKGROUND (1) An artificial neural network, also known simply as a neural network, is a type of machine learning framework in which patterns or learned representations of observed data are processed using highly connected computational layers that map the relationship between inputs and outputs. A “deep neural network,” in the context of deep learning, can refer to a neural network that utilizes multiple hidden layers between input and output layers, which may allow for learning based on features not explicitly defined in raw data. Various forms of neural networks may be used to make predictions about new data based on past examples or “training datasets.” In various implementations, deep neural networks may be trained as classifiers and may be utilized to perform image processing or natural-language processing, for example. (2) Deep neural network based applications typically require powerful computing resources for training and at run time. Moreover, such applications usually have resource-consuming preprocessing steps. As a result, it is important to properly dimension and configure the computing hardware on which those neural network based applications are to be trained or run. However, the hardware that hosts a neural network based application can be heterogeneous hardware resources having a variety of different hardware configurations. Furthermore, the hardware computing environment in which a neural network based application is executed may be shared with other applic