White Papers

7 CheXNet Inference with Nvidia T4 on Dell EMC PowerEdge R7425
CheXNet is a Deep Learning based model for Radiologist-Level Pneumonia Detection on
Chest X-Rays, developed by the Stanford University ML Group and trained on the Chest X-
Ray dataset. For the pneumonia detection, the ML group have labeled the images that have
pneumonia as the positive examples and labeled all other images with other pathologies as
negative examples.
NIH Chest X-ray Dataset
The National Institutes of Health released the NIH Chest X-ray Dataset, which includes
112,120 X-ray images from 30,805 unique patients, and labeled with 14 different thoracic
deceases through the application of Natural Language Processing algorithm to text-mine
disease classification from the original radiological reports.
1.1 Dell EMC PowerEdge R7425
Dell EMC PowerEdge R7425 server supports the latest GPU accelerators to speed results
in data analytics and AI applications. It enables fast workload performance on more cores
for cutting edge applications such Artificial Intelligence (AI), High Performance Computing
(HPC), and scale up software defined deployments. See Figure 3
Figure 3:DELL EMC PowerEdge R7425
The Dell™ PowerEdge™ R7425 is Dell EMC’s 2-socket, 2U rack server designed to run
complex workloads using highly scalable memory, I/O, and network options The systems
feature AMD high performance processors, named AMD SP3, which support up to 32 AMD
Zen x86 cores (AMD Naples Zeppelin SP3), up to 16 DIMMs, PCI Express® (PCIe) 3.0
enabled expansion slots, and a choice of OCP technologies.
The PowerEdge R7425 is a general-purpose platform capable of handling demanding
workloads and applications, such as VDI cloud client computing, database/in-line analytics,
scale up software defined environments, and high-performance computing (HPC).
The PowerEdge R7425 adds extraordinary storage capacity options, making it well-suited
for data intensive applications that require greater storage, while not sacrificing I/O
performance.