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Misc

Updates to NVIDIA’s Unreal Engine 4 Branch, DLSS, and RTXGI Available Now

NVIDIA has released updates to DLSS, NVIDIA’s Unreal Engine 4 Branch, and RTXGl.

To help developers get the most out of Unreal Engine 4 as they head into the new year, NVIDIA RTX UE4.26 has just been released. We have also released the first DLSS plugin that can be used with both NVIDIA’s NvRTX branch and mainline UE4, along with an updated UE4 Plugin for RTX Global Illumination.

NVIDIA RTX UE4.26 

The new NVIDIA UE4.26 Branch offers all of the benefits of mainline UE4.26, while providing some additional features: 

  • Faster ray tracing
    • NVRTX includes a number of improvements to ray tracing performance. Some of these are tunable, some are automatic. 
  • New tools
    • New debugging tools like the BVH viewer and Ray Timing Visualization allows developers to get a handle on ray tracing cost in their scene and get it tuned for speed.
  • Hybrid Translucency
    • Another way to do ray traced translucency, with greater compatibility, speed and rendering options.  
  • World position offset simulation for ray traced instanced static meshes (beta)
    • Allows ambient motion of foliage like trees and grass.
    • Uses approximate technique of shared animations to reduce overhead for simulating a full forest.
    • Selectable per instance type.
  • Inexact Shadows (beta)
    • Deals with potential mesh mismatches of ray traced and raster geometry.
    • Dithers shadow testing to hide potential artifacts. 
    • Enables approximations that improve performance in the management of ray tracing data.

An updated build of NVIDIA RTX UE4.25 has also been released, which includes all of the new features listed. 

Both branches can be found here

NVIDIA DLSS Plugin for UE4

NVIDIA DLSS is a deep learning neural network that boosts frame rates and generates beautiful, sharp images for your games. It delivers the performance headroom needed to maximize ray tracing settings and increase output resolution. It is available for the first time for mainline UE4 (in beta), compatible with UE4.26 Enjoy great scaling across all RTX GPUs and resolutions, and the new ultra performance mode for 8K gaming. 

Request access to the beta for NVIDIA DLSS plugin for UE4 here.

NVIDIA RTXGI Plugin for UE4

Leveraging the power of ray tracing, NVIDIA RTX Global Illumination (RTXGI) provides scalable solutions to compute multi-bounce indirect lighting without bake times, light leaks, or expensive per-frame costs. RTXGI is supported on any DXR-enabled GPU and is an ideal starting point to bring the benefits of ray tracing to your existing tools, knowledge, and capabilities. We have updated our RTXGI UE4 plugin with bugs fixes, image quality improvements, and support for UE4.26.

Request access to the RTXGI plugin for UE4 here

Categories
Misc

Developer Blog: Enhancing Memory Allocation with New NVIDIA CUDA 11.2 Features

CUDA 11.2 has several important features including programming model updates, new compiler features, and enhanced compatibility across CUDA releases. This post offers an overview of the key CUDA 11.2 software features and highlights:

CUDA 11.2 has several important features including programming model updates, new compiler features, and enhanced compatibility across CUDA releases. This post offers an overview of the key CUDA 11.2 software features and highlights:

Categories
Misc

Developer Blog: Monitoring High-Performance ML Models with RAPIDS and whylogs

With RAPIDS, data scientists can now train models 100X faster and more frequently. Like RAPIDS, we’ve ensured that our data logging solution at WhyLabs empowers users working with larger than memory datasets.

With RAPIDS, data scientists can now train models 100X faster and more frequently. Like RAPIDS, we’ve ensured that our data logging solution at WhyLabs empowers users working with larger than memory datasets.

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Misc

Sustainable and Attainable: Zoox Unveils Autonomous Robotaxi Powered by NVIDIA

When it comes to future mobility, you may not have to pave as many paradises for personal car parking lots. This week, autonomous mobility company Zoox unveiled its much-anticipated purpose-built robotaxi. Designed for everyday urban mobility, the vehicle is powered by NVIDIA and is one of the first level 5 robotaxis featuring bi-directional capabilities, providing Read article >

The post Sustainable and Attainable: Zoox Unveils Autonomous Robotaxi Powered by NVIDIA appeared first on The Official NVIDIA Blog.

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Misc

Kubeflow: Cloud Native ML Toolbox


Kubeflow: Cloud Native ML Toolbox
submitted by /u/SoulmanIqbal

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Misc

Defining loss when number of inputs is greater than number of outputs

I have trained a model which outputs multiple images (say 2) at
a time, and takes in multiple inputs (say 5) to do so. However my
loss (MSE) is supposed to apply to only 2 pairs made of 2 of the
input and output. Meaning I define my model as:

themodel = Model( [ip1,ip2,ip3,ip4,ip5], [op1,op2] ) themodel.compile( optimizer='Adam', loss=['mse','mse'] ) 

My model seems to train correctly, I just couldn’t find
confirmation in the docs (compile
method)
of how Tf takes care of which tensors to apply the loss
to. My assumption is that it applies the first loss to the first
ip-op pair, and so on till output tensors are available and nothing
is applied to remaining input tensors. Is this what is happening?
Also another silly question, does this mean for multiple output
models, loss has to be defined for all the outputs, and is not
related to the number of inputs?

submitted by /u/juggy94

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Paid ML gigs: Get compensated while further sharpening your skills on your own schedule.

submitted by /u/MLtinkerer

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Misc

`set_session` is not available when using TensorFlow 2.0.

Hi All.

I am using Keras and Tensorflow 2.0. I have code that tries to
set the number of inter and intra op threads. I have added the
session stuff for compatability, but it still won’t work right.

from keras import backend as K

….

….

import tensorflow as tf

session_conf =
tf.compat.v1.ConfigProto(inter_op_parallelism_threads=int(os.environ[‘NUM_INTER_THREADS’]),

intra_op_parallelism_threads=int(os.environ[‘NUM_INTRA_THREADS’]))

sess =
tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(),
config=session_conf)

K.set_session(sess)

Then it blows up with:

RuntimeError: `set_session` is not available when using
TensorFlow 2.0.

Any advice?

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Misc

All AIs on Quality: Startup’s NVIDIA Jetson-Enabled Inspections Boost Manufacturing

Once the founder of a wearable computing startup, Arye Barnehama understands the toils of manufacturing consumer devices. He moved to Shenzhen in 2014 to personally oversee production lines for his brain waves-monitoring headband, Melon. It was an experience that left an impression: manufacturing needed automation. His next act is Elementary Robotics, which develops robotics for Read article >

The post All AIs on Quality: Startup’s NVIDIA Jetson-Enabled Inspections Boost Manufacturing appeared first on The Official NVIDIA Blog.

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Misc

Pinterest Trains Visual Search Faster with Optimized Architecture on NVIDIA GPUs

Pinterest now has more than 440 million reasons to offer the best visual search experience. That’s because its monthly active users are tracking this high for its popular image sharing and social media service. Visual search enables Pinterest users to search for images using text, screenshots or camera photos. It’s the core AI behind how Read article >

The post Pinterest Trains Visual Search Faster with Optimized Architecture on NVIDIA GPUs appeared first on The Official NVIDIA Blog.