Deep Learning with Multiple GPUs
What Is Multi GPU in Deep Learning?
In this article, from the blog of our Partner Run:AI, you can learn what Multiple GPUs systems are and why they are ideal for Deep Learning processes.
Deep learning is a subset of machine learning that does not rely on structured data to develop accurate predictive models. This method uses networks of algorithms modeled after neural networks to distill and correlate large amounts of data. The more data you feed your network, the more accurate the model becomes.
You can functionally train deep learning models using sequential processing methods. However, the amount of data needed and the length of data processing make it impractical if not impossible to train models without parallel processing. Parallel processing enables multiple data objects to be processed at the same time, drastically reducing training time. This parallel processing is typically accomplished through the use of graphical processing units (GPUs).
GPUs are specialized processors created to work in parallel. These units can provide significant advantages over traditional CPUs, including up to 10x more speed. Typically, multiple GPUs are built into a system in addition to CPUs. While the CPUs can handle more complex or general tasks, the GPUs can handle specific, highly repetitive processing tasks.