For Python 3.8 and TensorFlow 2.5, I have a 3-D tensor of shape (3, 3, 3) where the goal is to compute the L2-norm for each of the three (3, 3) square matrices. The code that I came up with is:
The DevKit is an integrated hardware-software platform for creating, evaluating, and benchmarking HPC, AI, and scientific computing applications for Arm server based accelerated platforms.
Today NVIDIA announced the availability of the NVIDIA Arm HPC Developer Kit with the NVIDIA HPC SDK version 21.7. The DevKit is an integrated hardware-software platform for creating, evaluating, and benchmarking HPC, AI, and scientific computing applications for Arm server based accelerated platforms. The HPC SDK v21.7 is the latest update of the software development kit, and fully supports the new Arm HPC DevKit.
This DevKit targets heterogeneous GPU/CPU system development, and includes an Arm CPU, two NVIDIA A100 Tensor Core GPUs, two NVIDIA BlueField-2 data processing units (DPUs), and the NVIDIA HPC SDK suite of tools.
The integrated HW/SW DevKit delivers:
A validated system for quick and easy bring-up in a stable environment for accelerated computing code execution and evaluation, performance analysis, system experimentation, and system characterization.
A stable hardware and software platform for development and performance analysis of accelerated HPC, AI, and scientific computing applications
Experimentation and characterization of high-performance, NVIDIA-accelerated, Arm server-based system architectures
The NVIDIA Arm HPC Developer Kit is based on the GIGABYTE G242-P32 2U server, and leverages the NVIDIA HPC SDK, a comprehensive suite of compilers, libraries, and tools for HPC delivering performance, portability, and productivity. The platform will support Ubuntu, SLES, and RHEL operating systems.
HPC SDK 21.7 includes:
Full support for the NVIDIA Arm HPC Developer Kit
CUDA 11.4 support
HPC Compilers with Arm-specific performance enhancements including improved vectorization and optimized math functions
Maintenance support and bug fixes
Previously HPC SDK 21.5 introduced support for:
A subset of Arm Neon intrinsics have been implemented in the HPC Compilers and can be enabled with -Mneon_intrinsics.
The NVIDIA HPC SDK C++ and Fortran compilers are the first compilers to support automatic GPU acceleration of standard language constructs including C++17 parallel algorithms and Fortran intrinsics.
At GDC 2021, NVIDIA introduced a suite of Omniverse apps and tools to simplify and accelerate game development content creation pipelines.
Collaboration and simulation platform simplifies complex challenges like multi-app workflows, facial animation, asset searching, and building proprietary tools.
Content creation in game development involves multiple steps and processes, which can be notoriously complicated. To create the best experiences, game artists need to build massive libraries of 3D content while incorporating realistic lighting, physics and optimal game performance with AI. An explosion in the number of Digital Content Creation (DCC) tools to design different elements of a game leads to long review cycles, and difficulties in maximizing iteration. And often, studios are spending development hours creating their own proprietary tools to enable these workflows.
At GDC 2021, NVIDIA introduced a suite of Omniverse apps and tools to simplify and accelerate game development content creation pipelines. Developers can plug into any layer of the platform stack — whether at the top level, utilizing pre-built Omniverse Apps such as Create, Machinima, or Audio2Face; or the platform component level, to easily build custom extensions and tools to accelerate their workflow.
USD Comes to Game Development
Universal Scene Description (USD), the foundation of NVIDIA Omniverse, is an easily extensible, open-source 3D scene description and file format developed by Pixar for content creation and interchange among different tools.
Because of its versatility, USD is now being widely adopted across industries like media and entertainment, architecture, robotics, manufacturing — and now, game development.
Luminous and Embark Studios, two of the early evaluators of NVIDIA Omniverse for game development, have adopted USD to leverage the Omniverse-connected ecosystem and accelerate their workflows.
“Game development content pipelines are complex and require us to use the best aspects of multiple applications,” said Takeshi Aramaki, Studio Head and VP at Luminous. “By adopting Pixar’s Universal Scene Description (USD), we will leverage universal asset and application interoperability across our tools to accelerate time to production and optimize our workflows.”
Accelerate Workflows with Live-Sync Collaboration and Intuitive Tools
When it comes to creating content, game developers must use various industry tools, many of which are often incompatible. Omniverse Connectors, which are plug-ins to popular applications, provide game developers with the ability to work live and simultaneously across their favorite applications, so they can easily speed up workflows.
And with Omniverse Create, developers can leverage simple, intuitive tools to build and test content, and rapidly iterate during creation pipelines. Use paint tools for set dressing, or tap into Omniverse Physics — like PhysX 5, Flow, and Blast — to bring realistic details to 3D models. NVIDIA RTX technology enables real-time ray tracing and path tracing for ground truth lighting. And users can easily stream content from Omniverse, so they can view models or assets on any device.
Simplify Asset Management Woes
Game developers are burdened with extremely large asset catalogs being built over several years, by thousands of artists and developers, across several studios. Accelerating and simplifying asset search and management is critical to maintaining productivity and limiting cost spent on duplicating assets that can’t be found.
With Omniverse Nucleus, the core collaboration and database engine for ease of 3D asset interchange, assets are stored as ground truth, and can easily be passed from artist to artist, or studio to studio.
Plus, with Omniverse’s AI and advanced rendering capabilities, developers can leverage Omniverse DeepSearch to easily search through thousands of 3D assets using still images or natural language, including adjectives or qualifiers.
An AI-Powered Playground
Realistic facial animation is a notoriously tedious process, but game creators can add enhanced levels of detail to characters using Omniverse Audio2Face, an app that automatically generates facial animation using AI. Audio2Face allows developers to create realistic facial expressions and motions to match any voice-over track. The technology feeds the audio input into a pre-trained Deep Neural Network, and the output of the network drives the facial animation of 3D characters in real time.
And Omniverse Machinima is a tool that helps game developers create cinematic animations and storytelling with their USD-based assets, or they can seed to their community to generate remixed User Generated Content to promote iconic characters or scenes. Today, Machinima includes notable assets from Mount & Blade II: Bannerlord and Squad, with more to come.
Kit Extensions System
The Omniverse Kit Extensions system enables anybody with basic programming knowledge to build powerful tools quickly and distribute them to the content makers, or to package them into micro-services to empower new distributed workflows. Extensions are mostly authored in Python for ultimate usability and have source code provided, so developers can inspect, experiment and build to suit their needs using a Script Editor.
Image: Extension Manager in Omniverse Kit
Developers can also use the powerful Omni.UI system — an ultra-lightweight, GPU-accelerated user interface framework that is the foundational UI for all Omniverse Kit-based applications which is fully styleable, similar to HTML stylesheets, and works on Linux and Windows with DX12 and Vulkan-accelerated backends.
Graph Editing Framework For team members without extensive scripting or coding experience, Omni.UI Graph is an easy-to-use graph editing framework to develop custom behaviors for extensions or apps. With Omni.UI Graph, Omniverse Kit and some skills in Python, users can intuitively create and customize extensions at runtime for fast iteration.
NVIDIA Operators streamline installing and managing GPUs and NICs on Kubernetes to make the software stack ready to run the most resource-demanding workloads, such as AI, ML, DL, and HPC, in the cloud, data center, and at the edge.
Kubernetes is an open-source container-orchestration system for automating computer application deployment, scaling, and management. It’s an extremely popular tool, and can be used for automated rollouts and rollbacks, horizontal scaling, storage orchestration, and more. For many organizations, Kubernetes is a key component to their infrastructure.
A critical step to installing and scaling Kubernetes is ensuring that it is properly utilizing the other components of the infrastructure. NVIDIA Operators streamline installing and managing GPUs and NICs on Kubernetes to make the software stack ready to run the most resource-demanding workloads, such as AI, ML, DL, and HPC, in the cloud, data center, and at the edge. NVIDIA Operators consist of the GPU Operator and the Network Operator, and are open source and based on the Operator Framework.
NVIDIA GPU Operator
The NVIDIA GPU Operator is packaged as a Helm Chart and installs and manages the lifecycle of software components so that the GPU-accelerated applications can be run on Kubernetes. The components are the GPU feature discovery, the NVIDIA Driver, the Kubernetes Device Plugin, the NVIDIA Container Toolkit, and DCGM Monitoring.
The GPU Operator enables infrastructure teams to manage the lifecycle of GPUs when used with Kubernetes at the Cluster level, therefore eliminating the need to manage each node individually. Previously infrastructure teams had to manage two operating system images, one for GPU nodes and one CPU nodes. When using the GPU Operator, infrastructure teams can use the CPU image with GPU worker nodes as well.
NVIDIA Network Operator
The Network Operator is responsible for automating the deployment and management of the host networking components in a Kubernetes cluster. It includes the Kubernetes Device Plugin, NVIDIA Driver, NVIDIA Peer Memory Driver, and the Multus, macvlan CNIs. These components were previously installed manually, but are automated through the Network Operator, streamlining the deployment process and enabling accelerated computing with enhanced customer experience.
Used independently or together, NVIDIA Operators simplify GPU and SmartNIC configurations on Kubernetes and are compatible with partner cloud platforms. To learn more about these components and how the NVIDIA Operators solve the key challenges to running AI, ML, DL, and HPC workloads and simplify initial setup and Day 2 operations, check out the on-demand webinar “Accelerating Kubernetes with NVIDIA Operators“.
This GFN Thursday brings in hordes of fun — and a whole lot of orcs. Orcs Must Die! 3, the newest title from the action-packed, orc-slaying series from Robot Entertainment, is joining the GeForce NOW library when it releases tomorrow, Friday, July 23. In addition, 10 more games are coming to the service this Read article >
Learn how to up a confidential AI inference service. Using TensorFlow Serving, we’ll showcase a multi-stakeholder scenario including cloud service provider, model owner, inference service provider, and users.
What would be a good way to continuously add user ratings to the model, instead of training it from scratch with the complete dataset every time there is an update? What I would like to avoid is having to fetch and process the complete dataset every time there are only a few new user ratings. Is this even possible?
Or, in other words: What are options for doing this in real time? Re-training the model from scratch every time a user rates something seems a bit of an overkill for whatever server that processes this data.
I would be very thankful for some insights and/or somebody pointing me in the right direction with this. Thanks!
I am trying to learn Tensorflow, but most of the courses and tutorials focus on the v1.Session style code. Specifically I am looking at the estimators. When consulting the Tensorflow documentation there is a big red notice:
Warning: Estimators are not recommended for new code. Estimators run
-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under compatibility guarantees, but will receive no fixes other than security vulnerabilities. See the migration guide for details.
Looking at the migration guide, they only mention estimators in the capacity that they are still compatible.
My question
What are we to use in place of the estimators?
A secondary question, where can I get some good training material on purely v2.5 stuff that outlines a “clean” way to code networks without Session or anything that is going to be imminently deprecated?
Solving a mystery that stumped scientists for decades, last November a group of computational biologists from Alphabet’s DeepMind used AI to predict a protein’s structure from its amino acid sequence. Not even a year later, a new study offers a more powerful model, capable of computing protein structures in as little as 10 minutes, on … Continued
Solving a mystery that stumped scientists for decades, last November a group of computational biologists from Alphabet’s DeepMind used AI to predict a protein’s structure from its amino acid sequence.
Not even a year later, a new study offers a more powerful model, capable of computing protein structures in as little as 10 minutes, on one gaming computer.
The research, from scientists at the University of Washington (UW), holds promise for faster drug development, which could unlock solutions for treating diseases like cancer.
Present in every cell in the body, proteins play a role in many processes such as blood clotting, hormone regulation, immune system response, vision, and cell and tissue repair. Made from long chains of amino acids that interact to form a folded three-dimensional structure, the shape of a protein determines its function.
Unfolded or misfolded proteins are also thought to cause degenerative disorders including cystic fibrosis, Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. Understanding and predicting how a protein structure develops could help scientists design effective interventions for many of these diseases.
The researchers at UW developed the RoseTTAFold model by creating a three-track neural network that simultaneously considers the sequence patterns, amino acid interaction, and possible three-dimensional structure of a protein.
To train the model, the team used discontinuous crops of protein segments, with 260 unique amino acid elements. With the cuDNN-accelerated PyTorch deep learning framework, and NVIDIA GeForce 2080 GPUs, this information flows back and forth within the deep learning model. The network is then able to deduce a protein’s chemical parts along with its folded structure.
“The end-to-end version of RoseTTAFold requires about 10 minutes on an RTX 2080 GPU to generate backbone coordinates for proteins with less than 400 residues. The pyRosetta version requires 5 minutes for network calculations on a single NVIDIA RTX 2080 GPU, and an hour for all-atom structure generation with 15 CPU cores,” the researchers write in the study.
Predicted protein structures and their ground truth score. Credit: UW/Baek et al
The tool not only quickly predicts proteins, but can do so with limited input. It also has the ability to compute beyond simple structures, predicting complexes consisting of several proteins bound together. More complex models are computed in about 30 minutes on a 24G NVIDIA TITAN RTX.
A public server is available for anyone interested in submitting protein sequences. The source code is also freely available to the scientific community.
“In just the last month, over 4,500 proteins have been submitted to our new web server, and we have made the RoseTTAFold code available through the GitHub website. We hope this new tool will continue to benefit the entire research community,” said lead author Minkyung Baek, a postdoctoral scholar at the University of Washington, Institute for Protein Design.
The DevKit is an integrated hardware-software platform for creating, evaluating, and benchmarking HPC, AI, and scientific computing applications for Arm server based accelerated platforms.
At GDC 2021, NVIDIA introduced a suite of Omniverse apps and tools to simplify and accelerate game development content creation pipelines.
NVIDIA Operators streamline installing and managing GPUs and NICs on Kubernetes to make the software stack ready to run the most resource-demanding workloads, such as AI, ML, DL, and HPC, in the cloud, data center, and at the edge. 
Solving a mystery that stumped scientists for decades, last November a group of computational biologists from Alphabet’s DeepMind used AI to predict a protein’s structure from its amino acid sequence. Not even a year later, a new study offers a more powerful model, capable of computing protein structures in as little as 10 minutes, on … 
