Accelerated Model Building with NVIDIA Data Science Workbench

Learn how building models with NVIDIA Data Science Workbench can improve management and increase productivity.

Data scientists wrestle with many challenges that slow development. There are operational tasks, including software stack management, installation, and updates that impact productivity. Reproducing state-of-the-art assets can be difficult as modern workflows include many tedious and complex tasks. Access to the tools you need is not always fast or convenient. Also, the use of multiple tools and CLIs adds complexity to the data science lifecycle.

Master your Data Science environment

Building data science models is easier said than done. That’s why we are announcing NVIDIA Data Science Workbench to simplify and orchestrate tasks for data scientists, data engineers, and AI developers. Using a GPU-enabled mobile or desktop workstation, users can easily manage the software development environment for greater productivity and ease-of-use while quickly reproducing state-of-the-art examples to accelerate development. Through Workbench, key assets are just a click away.

NVIDIA Data Science Workbench connects to the NVIDIA GPU Cloud and is also connected to JupyterLab, PyTorch, TensorFlow, RAPIDS, and multiple CLIs including Kaggle, NGC, NVIDIA Data Science Stack, and AWS.The foundation of the diagram is the NVIDIA-Certified Data Science Workstation and OS which supports Drivers, CUDA, nv-docker, and Docker]
Figure 1: NVIDIA Data Science Workbench and Enterprise Data Science Stack.

Workbench enhances the development experience in several ways: 

Better management

Easily set up your work environment and manage NVIDIA Data Science Stack software versions. Access tools that provide optimized frameworks for GPU accelerated performance as well as automatic driver, CUDA, nv-docker, and NGC™ container updates. Also, get notified about other important updates. 

Easy reproducibility

Build quality models faster based on state-of-the-art example code. Dockerize GitHub content and reproduce assets for your Jupyter environment. Use NGC™ the container for GPU-optimized code that also runs in AWS.

Greater productivity

Easy software and driver installation, quickly access the Jupyter notebook, software assets, Kaggle notebooks, GitHub, and more. Use NGC containers for GPU-optimized code that also runs in AWS.

Try Workbench

The released version for Ubuntu 18.04 and 20.04 is now available. Click here for installation instructions. Also, watch this 90-second Workbench demo:

Video 1: The video shows Workbench as a desktop application and illustrates NGC, Kaggle, and various data science tools and assets are easily accessed.

“I installed the NVIDIA Data Science Workbench and quickly discovered that it was easy to reproduce Git content and download NGC containers for use in Jupyter. I was pleasantly surprised to learn that Workbench installs a data science software environment for you as well as addressing updates – which is usually a big hassle and a big consumption of time. I’d expect Workbench will become a popular tool for anyone building deep-learning models and other data science projects.”

Dr. Chanin Nantasenamat
Associate Professor of Bioinformatics at Mahidol University
Founder of Data Professor YouTube Channel

Attend our session at the NVIDIA GTC Conference to learn more about Workbench. GTC registration is required (registration is free).

Session ID and Title: A31396–Three Ways NVIDIA Improves the Data Science Experience

Date and Time: November 11, 2021 at 3:00am – 3:50am Pacific Time (on-demand afterward)

Workbench – your personal assistant

NVIDIA Data Science Workbench can make you more productive by providing a convenient framework on workstations for building models that use best practices. Workbench will run on most GPU-enabled workstations, but NVIDIA-Certified Workstations are recommended. In the end, it’s easier to manage, reproduce, and leverage NGC, Kaggle, and Conda for helpful assets.

Workbench won’t build your code for you, but it will accelerate development, reduce confusion, and help deliver better quality models in less time. To learn more, read the Workbench webpage, or visit the Workbench forum.

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