5 Reasons Why Your Data Science Team Needs The DGX Station
•
6 likes•8,429 views
This presentation reviews the top challenges facing your data science team and the benefits an NVIDIA DGX Station can offer. Learn how your teams can get the performance of GPU-powered deep learning, without the dependency on a data center server. Discover how to help your researchers and developers save time and avoid wrestling with IT, and gain instant productivity with an optimized deep learning software stack, that’s up and running in just a couple hours instead of weeks. Explore the benefits of a personal supercomputer that fits neatly desk-side, with the versatility to take your team from experimentation to training at scale, to deep learning inference and insights.
Recommended
Recommended
More Related Content
More from NVIDIA
More from NVIDIA (20)
Recently uploaded
Recently uploaded (20)
5 Reasons Why Your Data Science Team Needs The DGX Station
- 1. 5 REASONS WHY YOUR DATA SCIENCE TEAM NEEDS THIS POWERFUL DEEP LEARNING WORKSTATION THE PERSONAL AI SUPERCOMPUTER NVIDIA® DGX Station™
- 2. Challenges: - You need the power of a server, but need the convenience and ease of a workstation. - Traditional platforms lack the speed to experiment fast, and iterate faster. - Researchers don’t have time to waste on IT, troubleshooting code, or optimizing a deep learning stack. - You need a software stack that lets your models follow you across desk, data center and cloud. - Today’s platforms lack the versatility to support you from experimentation to training to inference.
- 3. “The School of Computer Science and Communication has a powerful compute server. However, for our current projects we need a compute server that we have exclusive access to.” — Professor, Computer Science Faculty of an European University “[We’re looking for a] … a workstation configured to create and train convolutional neural networks to be set up in the department that will have the ability to download and store multiple images and reports. [..] Training of an algorithm utilization high resolution medical images requires a large memory bandwidth such as that available in NVIDIA GPUs.” — Scientist, a Well Known North American University’s Medical Center “I use deep learning to secure cyber assets. Access to a deep learning workstation will increase the speed of innovation and improve security.” — Scientist at large research lab DATA SCIENTISTS HAVE TOLD US…
- 4. TOP 5 REASONS YOU CAN ACCELERATE DEEP LEARNING VALUE AND INSIGHTS WITH NVIDIA DGX STATION
- 5. #1: DESIGNED FOR WHERE YOU WORK The power of 400 CPUs – at your desk Consumes only 1500W, drawing 1/20th the power of a traditional workstation Emitting only 1/10th the noise of other workstations 1 Download Infographic
- 6. #2: 3X FASTER THAN THE FASTEST WORKSTATIONS 2 Learn More Watch Video Water-cooled performance – the only workstation built on 4 Tesla V100’s 3X the performance of today’s fastest GPU workstations 30% faster training over non-DGX stack solutions 5X increase in I/O performance with 4-way NVLink vs. PCIe- connected GPU’s 480 TFLOPS 30% 5X 3X
- 7. #3: EFFORTLESS PRODUCTIVITY 3 Access popular deep learning frameworks, NVIDIA-optimized for maximum performance DGX containers enable easier experimentation and keep base OS clean Develop on DGX Station, scale on DGX-1 or the NVIDIA GPU Cloud Watch Video
- 8. - Single, unified stack for deep learning frameworks - Predictable execution across platforms - Pervasive reach #4 - COMMON SOFTWARE STACK ACROSS DGX FAMILY DEEP LEARNING FRAMEWORKS DGX Station DGX-1 NVIDIA Cloud Service NVIDIA GPU Cloud DEEP LEARNING USER SOFTWARE NVIDIA DIGITS™ NVIDIA DEEP LEARNING SDK CONTAINERIZATION TOOL NVIDIA Docker Docker GPU DRIVER NVIDIA Driver SYSTEM Host OS 4
- 9. #5 – DEEP LEARNING FROM DEVELOPMENT TO INFERENCE Accelerated Deep Learning Value with DGX Solutions Experiment Tune/ Optimize Deploy Train Insights Procure DGX Station Install / Compile Training Productive Experimentation Fast Bring-up DGX/CSP DGX Station From Desk installed optimized Inference to Insights refine, re-train 5
- 10. DGX STATION IN THE NEWS
- 11. RESEARCHER “I felt I won the software stack lottery as NVIDIA- docker was already installed. I immediately pulled a container and started work on a CNTK NCCL project, the next day pulled another container to work on a TF biomedical project. I haven’t looked back at how to reimage because felt too productive.”
- 12. DEVELOPER “For the numbers, it’s taking about 1-2 hrs to train a 152 layer ResNet on a ~20GB dataset, which is pretty good and keeping me active with experiments rolling, just on the workstation. It feels right for this work to allow fast iteration. The last time I did some serious model architecture/tuning work it took halfdays to days on Kepler GPUs.”
- 13. VENTURE BEAT NVIDIA also said it was launching its Optix 5.0 SDK on the Nvidia DGX AI workstation. That will give designers, artists, and other content-creation professionals the rendering capability of 150 standard central processing unit (CPU) servers. By running Nvidia Optix 5.0 on a DGX Station, content creators can significantly accelerate training, inference and rendering (meaning both AI and graphics tasks). – Dean Takahashi, Venture Beat
- 14. TOP 5 1. You can now get the right kind of power, conveniently at your desk 2. Now you can save time and money by starting your experimentation in hours, not weeks, powered by DGX Stack 3. Be more productive with your training and inference work that goes from desk to data center to cloud 4. Get breakthrough performance and precision – powered by Volta 5. Flexibility to do AI work at the desk, data center, or edge The Fastest Personal Supercomputer for Researchers and Data Scientists
Editor's Notes
- Watch Video – Exploding Animation Video or CVPR
- Watch Video – TensorRT
- Access popular deep learning frameworks, NVIDIA-optimized for maximum performance DGX containers enable easier experimentation and keep base OS clean Develop on DGX Station, scale on DGX-1 or the NVIDIA Cloud
- DGX Station is perfect for individual researchers or data scientists, or small groups thereof, that need flexibility and want full control over their compute resources. As both system share the same architecture, the DGX Station provides the freedom to seamlessly scale workloads to the infinite compute power of a DGX-1 cluster. A typical deep learning workflow could like the following: DGX Station: build a promising model. Iterate frequently in hours. Use 2 to 4 GPUs. DGX-1: Make the model work with real data and optimise data and optimise data and optimise data and optimise data and optimise it. Iterate in days to weeks. Use 4 to 8 GPUS. DGX-1: Train the model in production, and prepare it for your customers. Iterate from hours to weeks. Use 64 to 128 GPUs in a DGX-1 cluster. And eventually: Cloud/Datacenter/Embedded devices: Serve the trained model and provide functionality to your customers. In milliseconds. Use P40s or P4 or TX1.