This is an interesting write-up from James Mauro from Nvidia on Storage Performance Basics for Deep Learning.
“The complexity of the workloads plus the volume of data required to feed deep-learning training creates a challenging performance environment. Deep learning workloads cut across a broad array of data sources (images, binary data, etc), imposing different disk IO load attributes, depending on the model and a myriad of parameters and variables.”
NVIDIA CEO Jensen announced NVIDIA’s first data center CPU, Grace, named after Grace Hopper, a U.S. Navy rear admiral and computer programming pioneer. Grace is a highly specialized processor targeting largest data intensive HPC and AI applications as the training of next-generation natural-language processing models that have more than one trillion parameters.
Further accelerating the infrastructure upon which hyperscale data centers, workstations, and supercomputers are built, Huang announced the NVIDIA BlueField-3 DPU.
The next-generation data processing unit will deliver the most powerful software-defined networking, storage and cybersecurity acceleration capabilities.
Where BlueField-2 offloaded the equivalent of 30 CPU cores, it would take 300 CPU cores to secure, offload, and accelerate network traffic at 400 Gbps as BlueField-3— a 10x leap in performance, Huang explained.
In this Python Tutorial, we will be learning how to install, setup, and use Jupyter Notebooks. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text.