Category: Misc

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Misc

How do you install TF (and TFLite) on a Raspberry Pi Zero?

After several hours of Google searching, I cannot find a straightforward answer. I’m not working with the W model, so my RPi doesn’t have a wireless adapter. I have found results that all involve cloning repos on the RPi itself, but I have no clue how to go about it. Do I need a Raspberian build that already has TF built-in to it? Or is there a way I can scp all the files over to my Pi?

I’ve tried what’s listed here, but instead downloading TF and its dependencies on my local machine, but didn’t know what to do in regards to the file path.

If anyone knows of a good guide or knows how to do this…please let me know. Thanks!

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submitted by /u/seekingstars
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GPU bug using tensorflow 2.x Object Detection API

Hey!

I’m following this installation guide for object detection using tensorflow 2, my goal is to train a CNN using my GPU. Tensorflow seems to recognize it after the GPU support section and everything runs smoothly. However, after I install the Object Detection API, tensorflow just starts ignoring it and runs on CPU. Any help would be deeply apreciated, thanks!

submitted by /u/smcsb
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3D Scene Understanding with TensorFlow 3D

3D Scene Understanding with TensorFlow 3D submitted by /u/AR_MR_XR
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tf.distribute.multiWorkerMirroredStrategy and data sharding

I understand the simple tf.distribute.mirroredStrategy(), it’s actually pretty simple. I’m hoping to scale a large problem across multiple computers so I’m trying to learn how to use multiWorkerMirrorredStrategy() but I have not found a good example yet.

My understanding is that I would write one python script and distribute it across the machines. Then I define the roles of each machine via the environment variable TF_CONFIG. I create the strategy and then do something like: 

mystrategy = tf.distribute.multiWorkerMirroredStrategy() with mystrategy.scope(): model = buildModel() model.compile() model.fit(x_train, y_train)

That’s all very straightforward. My question is about the data. This code is executed on all nodes. Is each node supposed to parse TF_CONFIG and load its own subset of data? Does just the chief load all the data and then the scope block parses out shards? Or does each node load all the data?

submitted by /u/Simusid
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Is Tensor Flow Compilable to a binary?

If I install all the external dependencies of Tensor Flow such as the Bazel, make, on my linux machine, and build Tensor Flow from source, would I be able to arrive at a binary code I can feed into some server machine to just run?

I am also asking whether the python API calls a bunch of dynamically linked libraries, and then somehow the have a model.build() command or a model.compile() command that produces a binary in the directory in my linux machine?

submitted by /u/GingerGengar123
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Tutorial: Reducing Temporal Noise on Images with NVIDIA VPI on NVIDIA Jetson Embedded Computers

The NVIDIA Vision Programming Interface (VPI) is a software library that provides a set of computer-vision and image-processing algorithms.

In this post, we show you how to run the Temporal Noise Reduction (TNR) sample application on the Jetson product family. For more information, see the VPI – Vision Programming Interface documentation.

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Webinar: Limitless Extended Reality with NVIDIA CloudXR 2.0

Learn how NVIDIA CloudXR can be used to deliver limitless virtual and augmented reality over networks (including 5G) to low cost, low-powered headsets and devices—while maintaining the high-quality experience traditionally reserved for high-end headsets that are plugged into high-performance computers.

Many people believed delivering extended reality (XR) experiences from cloud computing systems was impossible until now. Join our webinar to learn how NVIDIA CloudXR can be used to deliver limitless virtual and augmented reality over networks (including 5G) to low cost, low-powered headsets and devices—while maintaining the high-quality experience traditionally reserved for high-end headsets that are plugged into high-performance computers. CloudXR lifts the limits on developers, enabling them to focus their imagination on content, rather than spending huge amounts of time optimizing the application for low cost and low-powered headsets.

Date: Thursday, February 18, 2021
Time: 8:00am – 9:00am PT | 4:00pm – 5:00pm GMT
Duration: 1 hour

By joining this webinar, you will:

  • Learn more about delivering CloudXR
  • Get a deeper understanding of the new experiences enabled by CloudXR 2.0
  • See how CloudXR frees developers to focus on building better experiences
  • Get your questions answered in a live Q&A session

Register >

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Misc

Tutorial: Building Image Segmentation Faster Using Jupyter Notebooks from NGC

The AI containers and models on the NGC Catalog are tuned, tested, and optimized to extract maximum performance from your existing GPU infrastructure.

Image segmentation is the process of partitioning a digital image into multiple segments by changing the representation of an image into something that is more meaningful and easier to analyze. Image segmentation can be used in a variety of domains such as manufacturing to identify defective parts, in medical imaging to detect early onset of diseases, in autonomous driving to detect pedestrians, and more.

However, building, training, and optimizing these models can be complex and quite time consuming. To achieve a state-of-the-art model, you need to set up the right environment, train with the correct hyperparameters, and optimize it to achieve the desired accuracy. Data scientists and developers usually end up spending a considerable amount of looking for the right tools and setting up the environments for their models, which is why we built the NGC Catalog.

A hub for cloud-native, GPU-optimized AI and HPC applications and tools that provides faster access to performance-optimized containers, shortens time-to-solution with pretrained models and provides industry specific software development kits to build end-to-end AI solutions. The catalog hosts a diverse set of assets that can be used for a variety of applications and use cases ranging from computer vision and speech recognition to recommendation systems.

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Tutorial: Accelerating IO in the Modern Data Center: Computing and IO Management

This is the third post in the Explaining Magnum IO series, which has the goal of describing the architecture, components, and benefits of Magnum IO, the IO subsystem of the modern data center.

This is the third post in the Explaining Magnum IO series, which has the goal of describing the architecture, components, and benefits of Magnum IO, the IO subsystem of the modern data center.

The first post in this series introduced the Magnum IO architecture; positioned it in the broader context of CUDA, CUDA-X, and vertical application domains; and listed the four major components of the architecture. The second post delved deep into the Network IO components of Magnum IO. This third post covers two shorter areas: computing that occurs in the network adapter or switch and IO management. Whether your interests are in InfiniBand or Ethernet, NVIDIA Mellanox solutions have you covered.

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Misc

NVIDIA DLSS Plugin and Reflex Now Available for Unreal Engine 4.26

Unreal Engine 4 (UE4) developers can now access DLSS as a plugin for Unreal Engine 4.26. Additionally, NVIDIA Reflex is now available as a feature in mainline Unreal Engine 4.26. The NVIDIA RTX UE4 4.25 and 4.26 branches have also received updates.

Leveling up your games with the cutting-edge technologies found in the biggest blockbusters just got a lot simpler. As of today, Unreal Engine 4 (UE4) developers can now access DLSS as a plugin for Unreal Engine 4.26. Additionally, NVIDIA Reflex is now available as a feature in mainline Unreal Engine 4.26. The NVIDIA RTX UE4 4.25 and 4.26 branches have also received updates. 

NVIDIA DLSS Plugin for UE4

DLSS is a deep learning super resolution network that boosts frame rates by rendering fewer pixels and then using AI to construct sharp, higher resolution images. Dedicated computational units on NVIDIA RTX GPUs called Tensor Cores accelerate the AI calculations, allowing the algorithm to run in real-time. DLSS pairs perfectly with computationally intensive rendering algorithms such as real-time ray tracing. The technology has been used to increase performance in a broad range of games, including Fortnite, Cyperpunk 2077, Minecraft, Call of Duty: Black Ops Cold War, and Death Stranding.

DLSS is now available for the first time for mainline UE4 as a plugin, compatible with UE4.26.  Enjoy great scaling across all GeForce RTX GPUs and resolutions, including the new ultra performance mode for 8K gaming.

Access the NVIDIA DLSS plugin for UE4 here.

NVIDIA Reflex

NVIDIA Reflex is a toolkit to measure, debug and improve CPU+GPU latency in competitive multiplayer games.  It is now available to all developers through UE4 mainline.   

In addition to UE4 titles such as Fortnite, developers such as Activision Blizzard, Ubisoft, Riot, Bungie and Respawn are using Reflex now. Pull the latest change list from UE4 mainline today to improve system latency in your game.

Updates to NVIDIA RTX UE 4.25 and 4.26.1

The new NVIDIA UE 4.25 and 4.26.1 Branches offer all of the benefits of mainline UE4.25 and UE4.26.1, while providing some additional features: 

  • Fixes for instanced static mesh culling (including foliage)
  • Option to mark ray tracing data as high-priority to the memory manager to avoid poor placement of data in memory overflow conditions
  • Introduced threshold for recomputing ray tracing representation for landscape tiles
  • Compatibility fixes for building certain targets
  • Inexact occlusion test support for shadows and ambient occlusion

Download the latest branch here to benefit from all of NVIDIA’s cutting-edge features.

Pushing PC Gaming to New Levels

With our work with ray tracing, DLSS and Reflex, NVIDIA is revolutionizing what is possible from a GPU.  

  • Ray tracing and DLSS are supported in the biggest PC game launch ever – Cyberpunk 2077.
  • Ray tracing and DLSS is supported  in the most popular game – Minecraft.
  • Reflex has been adopted by 7 of the Top 10 competitive shooters – Call of Duty: Warzone, Call of Duty: Black Ops Cold War, Valorant, Fortnite, Apex Legends, Overwatch and Rainbow Six: Siege

Now all Unreal Engine developers can easily use these same technologies in your games, thanks to UE4 integration.

Learn more about other NVIDIA developer tools and SDKs available for game developers here.