When I try to run the training process of my neural network on my GPU, I get these errors:
W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices...
I have followed all the steps in the GPU installation guide. I have downloaded the latest Nvidia GPU driver which is compatible with my graphic card. I have also downloaded the CUDA tool kit as well as the cuDNN packages. I have also made sure to add the directories of the cuDNN C:cudabin, C:cudainclude, C:cudalibx64 folders as variables in the PATH environment. I also checked that the file cudnn64_8.dll exists, which it does in cudalibx64.
I just started learning Tensorflow/Keras and in this paper it says “We use SGD with momentum of 0.9 to optimize for sum-squared error in the output of our model and use a learning rate of 0.0001 and a weight decay of 0.0001 to train for 5 epochs.” I’m trying to implement that and I have this now
I have a dataset of type:
<BatchDataset shapes: ((None, 256, 256, 3), (None,)), types: (tf.float32, tf.int32)>
How do i convert it into a dataset of type:
<PrefetchDataset shapes: {image: (256, 256, 3), label: ()}, types: {image: tf.uint8, label: tf.int64}>
in tensorflow
I’m doing some testing using Google Cloud AI Platform and have seen some strange variation in training times. As an example, I did a test run that had an average training time of around 3.2 seconds per batch. I repeated it with the exact same hyperparameters and machine type and it took around 2.4 seconds the next time. Is there some explanation for this other than one GPU I’m assigned to being better in some way than another? That doesn’t really make sense either, but I don’t know how else to explain it.
An impressive array of NVIDIA GDC announcements elevates game development to the next level. Real-time ray tracing comes to Arm and Linux, DLSS gets an expansive update, the newly announced RTX Memory Utility enables efficient memory allocation, and Omniverse supercharges the development workflow.
Increasingly, game developers are making full use of real-time ray tracing and AI in their games. As a result, more gamers than ever are enjoying the beautifully realized lighting and AI-boosted images that you can only achieve with NVIDIA technology. At GDC 2021, NVIDIA’s updates, enhancements, and platform compatibility expansions enable RTX to be turned ON for a larger base than ever before.
NVIDIA RTX enables game developers to integrate stunning real-time ray traced lighting into games. Now, NVIDIA ray tracing and AI are making their premiere on Arm and Linux systems. Arm processors are the engines inside of billions of power-efficient devices. Linux is an extensively adopted open-source operating system with an avid user base. Together, these two platforms offer a massive new audience for ray tracing technology. To show our commitment to nurturing the Arm and Linux gaming ecosystems, NVIDIA has prepared a demo of Wolfenstein: Youngblood and Amazon’s Bistro scene running RTX on an Arm-based MediaTek processor.
For more information, contact NVIDIA’s developer relations team or visit developer.nvidia.com.
New Ray Tracing SDK: RTX Memory Utility Improves Memory Allocation for Games
Real-time ray tracing elevates game visuals to new heights with dynamic, physically-accurate lighting running at interactive frame rates. Though the results of ray tracing are stunning, the process is computationally expensive and can put a strain on memory availability in hardware. To alleviate this heavy cost, NVIDIA is releasing a new open source ray tracing SDK, RTX Memory Utility (RTXMU), built to optimize and reduce memory consumption of acceleration structures.
RTXMU uses sophisticated compaction and suballocation techniques that eliminates wasted memory, resulting in a roughly 50% reduction in memory footprint. By freeing this space, larger and more robust ray tracing worlds can be built than ever before.
RTXMU is easy to integrate, provides immediate benefits, and is available today.
DLSS Update brings Linux Support, Streamlined Access, new Customizable Options
A new DLSS update brings support for games running natively on Linux, alongside support for Vulkan API games on Proton introduced in June 2021. Arm support for DLSS has been announced as well. This update also brings a host of customizable options for both users and developers. New features include a Sharpening Slider that enables user-specific sharpening preferences, an Auto Mode that calibrates DLSS to the optimal quality given a particular resolution, and an Auto-Exposure Option that can improve image quality in low-contrast scenes.
Furthermore, accessing DLSS has been streamlined, and an application is no longer required to download DLSS SDK 2.2.1. DLSS is a game-changing software, and integrating it has never been easier. Learn more about using DLSS in NVIDIA’s DLSS Overview GDC session.
Read more about Linux and Arm support, new customizable options, and how to access DLSS in the DLSS 2.2.1 developer blog.
NvRTX: NVIDIA’s Branch of Unreal Engine Includes Best-in-Class Ray Tracing Technology
NVIDIA makes it easy for Unreal Engine developers to use RTX and AI in their games with the NvRTX branch, which adds RTX Direct Illumination (RTXDI), RTX Global Illumination (RTXGI), and DLSS to Unreal Engine 4.26 and Unreal Engine 5. Enhancing the game development ecosystem with support like NvRTX is incredibly important to NVIDIA; read more about how NVIDIA strives to empower game creation here.
Delve into how NVIDIA integrates ray tracing support into Unreal Engine with the NvRTX branch, solving ray tracing challenges on our end so that implementation is seamless and intuitive in the hands of developers. Watch the NvRTX Technical Overview GDC session.
Learn about how RTXDI and RTXGI in the NvRTX branch collaborate with a variety of Unreal Engine tools to enable artists to create ray traced scenes and effects. Watch the NvRTX Artists Guide GDC session.
Omniverse Accelerates Game Development to the Speed of Light
For game development, NVIDIA Omniverse offers the ultimate platform for cross-collaboration between the library of applications and development teams that must work in unison to push a game from concept to credits. Omniverse is a powerful collaboration tool that enables seamless communication and so much more — providing engines for simulation, ray traced rendering, and AI development to name a few. Learn about NVIDIA Omniverse’s extensive feature list and everything the platform can offer to game creation in the Omniverse at GDC blog.
Watch NVIDIA’s Omniverse Game Development GDC session, covering how to connect your favorite industry tools to the Omniverse Nucleus as well as how to use Extensions to build custom tools for the workflow.
NVIDIA Nsight Updates: Optimize and Debug GPU Performance on a Super-Granular Scale
NVIDIA Nsight enables developers to build and profile state-of-the-art games and applications that harness the full power of NVIDIA GPUs. Announced at GDC is a new serving of Nsight Developer Tools to improve the debugging and optimization process, learn more about these new additions in the Nsight Tools update blog.
Included in the newly available developer tools: Nsight Systems 2021.3, the latest Nsight release adding new features for register dependency visualization. Learn more about Nsight Systems 2021.3 in the release blog.
You can also read more about Nsight Graphics 2021.3, NVIDIA’s tool for deep analysis of GPU performance, in the Nsight Graphics 2021.3 release blog.
Ensuring that the GPU is being fully utilized when performing ray tracing tasks can be challenging. Explore how Nsight and other NVIDIA Developer Tools allow for optimizing and debugging GPU performance in the Nsight: Developer Tools GDC session.
Watch a demo of NSight Graphics in action below.
That’s a wrap on NVIDIA at GDC 2021!
Take a closer look at our game development SDKs and developer resources here.
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“.
An impressive array of NVIDIA GDC announcements elevates game development to the next level. Real-time ray tracing comes to Arm and Linux, DLSS gets an expansive update, the newly announced RTX Memory Utility enables efficient memory allocation, and Omniverse supercharges the development workflow.
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.