Designers, engineers, researchers, creative professionals all need the flexibility to run complex workflows – no matter where they’re working from. With the newest release of NVIDIA virtual GPU (vGPU) technology, enterprises can provide their employees with more power and flexibility through GPU-accelerated virtual machines from the data center or cloud. Available now, the latest version Read article >
When it comes to autonomous vehicle sensor innovation, it’s best to keep an open mind — and an open development platform. That’s why NVIDIA DRIVE is the chosen platform on which the majority of these sensors run. In addition to camera sensors, NVIDIA has long recognized that lidar is a crucial component to an autonomous Read article >
Nsight Graphics 2021.1 is available to download – check out this article to see what’s new.
Nsight Graphics 2021.1 is available to download.
We now provide you with the ability to set any key to be the capture shortcut. This new keybinding is supported for all activities, including GPU Trace. F11 is the default binding for both capture and trace, but if you prefer the old behavior, the original capture keybinding is still supported (when the ‘Frame Capture (Target) > Legacy Capture Chord’ setting is set to Yes).
You can now profile applications which use D3D12 or Vulkan strictly for compute tasks using the new ‘One-shot’ option in GPU Trace. Tools that generate normal maps or use DirectML for image upscaling can now be properly profiled and optimized. To enable this, set the ‘Capture Type’ to ‘One-shot [Beta]’
While TraceRays/DispatchRays has been the common way to initiate ray generation, it’s now possible to ray trace directly from your compute shaders using DXR1.1 and the new Khronos Vulkan Ray Tracing extension. In order to support this new approach, we’ve added links to the acceleration structure data for applications that use RayQuery calls in compute shaders.
It’s important to know how much GPU Memory you’re using and to keep this as low as possible in Ray Tracing applications. We’re now making this even easier for you by adding size information to the Acceleration Structure Viewer.
Finally, we’ve added the Nsight HUD to Windows Vulkan applications in all frame debugging capture states. Previously the HUD was only activated once an application was captured.
We’re always looking to improve our HUD so please make sure to give us any feedback you might have.
We want to hear from you! Please continue to use the integrated feedback button that lets you send comments, feature requests, and bugs directly to us with the click of a button. You can send feedback anonymously or provide an email so we can follow up with you about your feedback. Just click on the little speech bubble at the top right of the window.
Khronos released the final Vulkan Ray Tracing extensions today. NVIDIA Vulkan beta drivers available for download. Welcome to the era of portable, cross-vendor, cross-platform ray tracing acceleration!
AI, the most powerful technology of our time, demands a new generation of computers tuned and tested to drive it forward. Starting today, data centers can get boot up a new class of accelerated servers from our partners to power their journey into AI and data analytics. Top system makers are delivering the first wave Read article >
Hey, Very much a beginner with tensorflow, but been enjoying it
so far.
Background: response between 0-200, have 43 variables,
regression type problem, data set is over 200k rows
I’ve built a basic sequential model using Keras, and my loss
and validation loss are ideal – I.e validation loss is slightly
above loss, and it looks as it should.
However my actual loss seems quite high, it is converging around
34 and I’d have liked it to be around 20, now because of the
above I’m not sure if this means my data isn’t actually
predictive?!
I have standardised many variables rather than normalised, I’m
not sure if this would make any difference.
Is there anything I could add you think? I don’t think the
data set is lacking particularly woth the dimensions.
I am new to tensorflow and am trying to figure out what I think
should be a rather simple task. I have a model (.pb file) given to
me and I need to use it to markup an image.
I have two classes that the model was trained on: background and
burnish.
From this point on, I have literally no idea what I am doing. I
tried searching online and there is a lot about how to train a
model but I don’t need to do be able to do that.
Any help pointing me in the right direction would be
awesome!
JetPack SDK 4.5 is now available. This production release features enhanced secure boot, disk encryption, a new way to flash Jetson devices through Network File System, and the first production release of Vision Programming Interface.
JetPack SDK 4.5 is now available. This production release features enhanced secure boot, disk encryption, a new way to flash Jetson devices through Network File System, and the first production release of Vision Programming Interface.
For AI embedded and edge developers, the latest update for NVIDIA JetPack is available. It includes the first production release of Vision Programming Interface (VPI) to accelerate computer vision on Jetson. Visit our download page to learn more.
This production release features:
Enhanced secure boot and support for disk encryption
Improved Jetson Nano bootloader functionality
A new way of flashing Jetson devices using network file system
The Jetson team is hosting two webinars with live Q&A to dive into JetPack’s new capabilities. Learn how to get the most out of your Jetson device and accelerate development.
NVIDIA JetPack 4.5 Overview and Feature Demo February 9 at 9 a.m. PT
This webinar is a great way to learn about what’s new in JetPack 4.5. We’ll provide an in-depth look at the new release and show a live demo of select features. Come with questions—our Jetson experts will be hosting a live Q&A after the presentation.
Implementing Computer Vision and Image Processing Solutions with VPI February 11 at 9 a.m. PT
Get a comprehensive introduction to VPI API. You’ll learn how to build a complete and efficient stereo disparity-estimation pipeline using VPI that runs on Jetson-family devices. It provides a unified API to both CPU and NVIDIA CUDA algorithm implementations, as well as interoperability between VPI and OpenCV and CUDA. Register now >>
I am have searched a lot of tutorials and courses, most start
with a BERT model or some variation of it. I want to watch/ learn
how a transformer/ attention is trainned from scratch.
I want to try to build a attention/ transformer model for solved
games like chess, (ie I will have generate-able data)
If there was some way to load the saved model, and then edit
it’s structure that way, that could work. But I’m unsure if there’s
a better way to do this.
Nsight Graphics 2021.1 is available to download – check out this article to see what’s new. 
JetPack SDK 4.5 is now available. This production release features enhanced secure boot, disk encryption, a new way to flash Jetson devices through Network File System, and the first production release of Vision Programming Interface. 
bootloader functionality