July NVIDIA Studio Driver Improves Performance for Chaos V-Ray 6 for 3ds Max

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology accelerates creative workflows. 

Creativity heats up In the NVIDIA Studio as the July NVIDIA Studio Driver, available now, accelerates the recent Chaos V-Ray 6 for 3ds Max release.

Plus, this week’s In the NVIDIA Studio 3D artist, Brian Lai, showcases his development process for Afternoon Coffee and Waffle, a piece that went from concept to completion faster with NVIDIA RTX acceleration in Chaos V-Ray rendering software.

Chaos V-Ray Powered by NVIDIA Studio Offers a July to Remember

Visualization and computer graphics software company Chaos this month released an update to the all-in-one photorealistic rendering software, V-Ray 6 for 3ds Max. The July Studio Driver gives creators on NVIDIA GPUs the best experience.

Add procedural clouds to create beautiful custom skies with the latest V-Ray 6 release.

This release is a major upgrade that gives artists powerful new world-building and workflow tools to quickly distribute 3D objects, generate detailed 3D surfaces and add procedural clouds to create beautiful custom skies.

V-Ray GPU software — which already benefits from high-performance final-frame rendering with RTX-accelerated ray tracing and accelerated interactive rendering with AI-powered denoising — gets a performance bump, in addition to the new features.

Catch some V-Rays with an average of 2x faster GPU Light Cache calculations than V-Ray 5.

3D artists benefit from speedups in multiple ways with V-Ray 6. GPU improvements include support for nearly all new V-Ray 6 features, and enable faster Light Cache and a new Device Selector to assign rendering devices to tasks. By allowing users to specify use of the GPU for the AI denoiser, rendering performance is nearly doubled.

Adaptive dome-light rendering clocks in at up to 3x faster than V-Ray 5.

Additional key new features include:

  • Chaos Scatter — easily populate scenes with millions of 3D objects to produce natural-looking landscapes and environments without adjusting objects by hand.
  • Procedural Clouds — simulate a variety of cloud types and weather conditions, from partly cloudy to overcast.
  • Improved trace-depth workflow — simplifies the setting of trace-depth overrides for reflections and refractions.
  • Shading improvements — includes new energy-preserving GGX shader, thin film rollout in the V-Ray material for bubbles and fabrics, and improved V-Ray dirt functionality.

And there’s much more to explore. Creators can join a free V-Ray 6 for 3ds Max webinar on Thursday, July 28, to see how the new features are already helping 3D artists create fantastic visuals.

Sweet, Rendery Waffles

This week In the NVIDIA Studio, Brian Lai, a computer graphics craftsman, details the sweet, buttery-smooth creative process for Afternoon Coffee and Waffle.

Lai enjoyed the sweet satisfaction of creating with a GeForce RTX 3090 GPU and NVIDIA Studio benefits.

Lai’s journey into 3D art started at an early age, inspired by his photographer father — who Lai worked for until getting accepted into Malaysia’s top art college, The One Academy. He finds inspiration in real-world environments, observing what’s around him.

“I was always obsessed with optics and materials, so I just look at things around me, and then I want to replicate them into 3D form,” Lai said. “It’s satisfying to recreate a thing or environment to confuse my audience about whether it’s a real or 3D rendered image.”

It’s no wonder that Afternoon Coffee and Waffle takes on a picturesque quality, as typically exhibited by a social media image showing off a food adventure.

Before Lai turned on RTX.

After finding a reference shot online, Lai started his creative process with basic 3D model blocking, which helped him set a direction for the final image, as well as find a good focal length and position for the camera. Then, he finalized each 3D model, turning them into high-resolution models and texturing each separately. This allowed him to focus on the props’ details.

Lai called his experience creating with a GeForce RTX 3090 “butter smooth throughout the process.”

RTX-accelerated ray tracing happens lightning quick in V-Ray GPU with AI-powered denoising. Lai prefers V-Ray “mainly because it utilizes the power from RT and Tensor Cores on the graphics card,” he said. “I don’t really feel limited by the software.”

The anticipation of completion is so syrupy thick, one can almost taste it.

The artist also used his GPU for simulation in Autodesk Maya to get the best-looking fabrics possible, noting that “texturing in 4K preview is so satisfying.” He then finalized the models with normal map folds in Adobe Substance Painter.

Once all the models were ready, Lai gathered everything into one scene. In the above tutorial, he shows how each shader was constructed in Autodesk Maya’s Hypershade window.

The very last — and creatively infinite — step was to look dev. Here, Lai had to self-contain his tinkering as he “can do endless improvement in this stage,” he said. Ultimately, he reached the extent of realism he was aiming for and called the piece complete.

3D artist and CG craftsman Brian Lai.

Find more work from Lai, who first made his name discovering invert art, or negative drawing, on his Instagram.

Join the #ExtendtheOmniverse contest, running through Friday, Aug. 19. Perform something akin to magic by making your own NVIDIA Omniverse Extension for a chance to win an NVIDIA RTX GPU. Winners will be announced in September at GTC.

Follow NVIDIA Studio on Instagram, Twitter and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the NVIDIA Studio newsletter.

The post July NVIDIA Studio Driver Improves Performance for Chaos V-Ray 6 for 3ds Max appeared first on NVIDIA Blog.

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Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

This is a guest post co-written with Mutisya Ndunda from Trumid.

Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, increase the efficiency and accuracy of operational workflows, and enhance performance by supporting multiple aspects of the trading process.

Trumid is a financial technology company building tomorrow’s credit trading network—a marketplace for efficient trading, information dissemination, and execution between corporate bond market participants. Trumid is optimizing the credit trading experience by combining leading-edge product design and technology principles with deep market expertise. The result is an integrated trading solution delivering a full ecosystem of protocols and execution tools within one intuitive platform.

The bond trading market has traditionally involved offline buyer/seller matching processes aided by rules-based technology. Trumid has embarked on an initiative to transform this experience. Through its electronic trading platform, traders can access thousands of bonds to buy or sell, a community of engaged users to interact with, and a variety of trading protocols and execution solutions. With an expanding network of users, Trumid’s AI and Data Strategy team partnered with the AWS Machine Learning Solutions Lab. The objective was to develop ML systems that could deliver a more personalized trading experience by modeling the interest and preferences of users for bonds available on Trumid.

These ML models can be used to speed up time to insight and action by personalizing how information is displayed to each user to ensure that the most relevant and actionable information a trader may care about is prioritized and accessible.

To solve this challenge, Trumid and the ML Solutions Lab developed an end-to-end data preparation, model training, and inference process based on a deep neural network model built using the Deep Graph Library for Knowledge Embedding (DGL-KE). An end-to-end solution with Amazon SageMaker was also deployed.

Benefits of graph machine learning

Real-world data is complex and interconnected, and often contains network structures. Examples include molecules in nature, social networks, the internet, roadways, and financial trading platforms.

Graphs provide a natural way to model this complexity by extracting important and rich information that is embedded in the relations between entities.

Traditional ML algorithms require data to be organized as tables or sequences. This generally works well, but some domains are more naturally and effectively represented by graphs (such as a network of objects related to each other, as illustrated later in this post). Instead of coercing these graph datasets into tables or sequences, you can use graph ML algorithms to both represent and learn from the data as presented in its graph form, including information about constituent nodes, edges, and other features.

Considering that bond trading is inherently represented as a network of interactions between buyers and sellers involving various types of bond instruments, an effective solution needs to harness the network effects of the communities of traders that participate in the market. Let’s look at how we leveraged the trading network effects and implemented this vision here.

Solution

Bond trading is characterized by several factors, including trade size, term, issuer, rate, coupon values, bid/ask offer, and type of trading protocol involved. In addition to orders and trades, Trumid also captures “indications of interest” (IOIs). The historical interaction data embodies the trading behavior and the market conditions evolving over time. We used this data to build a graph of timestamped interactions between traders, bonds, and issuers, and used graph ML to predict future interactions.

The recommendation solution comprised four main steps:

  • Preparing the trading data as a graph dataset
  • Training a knowledge graph embedding model
  • Predicting new trades
  • Packaging the solution as a scalable workflow

In the following sections, we discuss each step in more detail.

Preparing the trading data as a graph dataset

There are many ways to represent trading data as a graph. One option is to represent the data exhaustively with nodes, edges, and properties: traders as nodes with properties (such as employer or tenure), bonds as nodes with properties (issuer, amount outstanding, maturity, rate, coupon value), and trades as edges with properties (date, type, size). Another option is to simplify the data and use only nodes and relations (relations are typed edges like traded or issued-by). This latter approach worked better in our case, and we used the graph represented in the following figure.

This graph represents the relations between traders, bonds and issuers

Graph of relations between traders, bonds and bond issuers

Additionally, we removed some of the edges considered obsolete: if a trader interacted with more than 100 different bonds, we kept only the last 100 bonds.

Finally, we saved the graph dataset as a list of edges in TSV format:

t987	trade-old		i55198
t995	trade-old		i55306
t987	trade-recent	i24528
t995	trade-recent	i49181
t987	ioi-recent		i24523
t995	ioi-old 		i49178
…
i49611	issued-by		XXX
i46569	issued-by		YYY
i46507	issued-by		ZZZ

Training a knowledge graph embedding model

For graphs composed only of nodes and relations (often called knowledge graphs), the DGL team developed the knowledge graph embedding framework DGL-KE. KE stands for knowledge embedding, the idea being to represent nodes and relations (knowledge) by coordinates (embeddings) and optimize (train) the coordinates so that the original graph structure can be recovered from the coordinates. In the list of available embedding models, we selected TransE (translational embeddings). TransE trains embeddings with the objective of approximating the following equality:

Source node embedding + relation embedding = target node embedding (1)

We trained the model by invoking the dglke_train command. The output of the training is a model folder containing the trained embeddings.

For more details about TransE, refer to Translating Embeddings for Modeling Multi-relational Data.

Predicting new trades

To predict new trades from a trader with our model, we used the equality (1): add the trader embedding to the trade-recent embedding and looked for bonds closest to the resulting embedding.

We did this in two steps:

  1. Compute scores for all possible trade-recent relations with dglke_predict.
  2. Compute the top 100 highest scores for each trader.

For detailed instructions on how to use the DGL-KE, refer to Training knowledge graph embeddings at scale with the Deep Graph Library and DGL-KE Documentation.

Packaging the solution as a scalable workflow

We used SageMaker notebooks to develop and debug our code. For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing. SageMaker processing allows you to run a script remotely on a chosen instance type and Docker image without having to worry about resource allocation and data transfer. This was simple and cost-effective for us, because the GPU instance is only used and paid for during the 15 minutes needed for the script to run.

For detailed instructions on how to use SageMaker processing, see Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation and Processing.

Results

Our custom graph model performed very well compared to other methods: performance improved by 80%, with more stable results across all trader types. We measured performance by mean recall (percentage of actual trades predicted by the recommender, averaged over all traders). With other standard metrics, the improvement ranged from 50–130%.

This performance enabled us to better match traders and bonds, indicating an enhanced trader experience within the model, with machine learning delivering a big step forward from hard-coded rules, which can be difficult to scale.

Conclusion

Trumid is focused on delivering innovative products and workflow efficiencies to their community of users. Building tomorrow’s credit trading network requires continuous collaboration with peers and industry experts like the AWS ML Solutions Lab, designed to help you innovate faster.

For more information, see the following resources:

About the authors

Marc van Oudheusden is a Senior Data Scientist with the Amazon ML Solutions Lab team at Amazon Web Services. He works with AWS customers to solve business problems with artificial intelligence and machine learning. Outside of work you may find him at the beach, playing with his children, surfing or kitesurfing.

Mutisya Ndunda is the Head of Data Strategy and AI at Trumid. He is a seasoned financial professional with over 20 years of broad institutional experience in capital markets, trading, and financial technology.  Mutisya has a strong quantitative and analytical background with over a decade of experience in artificial intelligence, machine learning and big data analytics. Prior to Trumid, he was the CEO of Alpha Vertex, a financial technology company offering analytical solutions powered by proprietary AI algorithms to financial institutions. Mutisya holds a bachelor’s degree in Electrical Engineering from Cornell University and a master’s degree in Financial Engineering from Cornell University.

Isaac Privitera is a Senior Data Scientist at the Amazon Machine Learning Solutions Lab, where he develops bespoke machine learning and deep learning solutions to address customers’ business problems. He works primarily in the computer vision space, focusing on enabling AWS customers with distributed training and active learning.

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Digital Sculptor Does Heavy Lifting With Lightweight Mobile Workstation

As a professional digital sculptor, Marlon Nuñez is on a mission to make learning 3D art skills easier, smoother and more fun for all. And with the help of an NVIDIA RTX-powered Lenovo mobile workstation, he takes his 3D projects to the next level, wherever he goes.

Nuñez is the art director and co-founder of Art Heroes, a 3D art academy. Based in Spain, Nuñez specializes in creating digital humans and stylized 3D characters, a complex feat. But he tackles his demanding creative workflows from anywhere, thanks to his Lenovo ThinkPad P1 powered by the NVIDIA RTX A5000 Laptop GPU.

The speed and performance of RTX-powered technology enable Nuñez to create stunning characters in real time.

“As an artist, render times and look development are where you spend most of your time,” said Nuñez. “NVIDIA RTX allows you to work with ray tracing on, providing artists with the option to make these creative processes faster and easier.”

Powerful Performance on the Go

Nuñez says there are three main benefits he has experienced with his RTX-powered mobile workstation. First, it’s light — the Lenovo ThinkPad P1 packs the power of the ultra-high-end NVIDIA RTX A5000 laptop GPU into a thin chassis that only weighs around four pounds. Nuñez said he can easily travel with his portable workstation — he doesn’t even need a bag to carry it.

Second, the NVIDIA RTX GPU supports intense real-time ray tracing, which allows Nuñez to make photorealistic graphics and hyper-accurate designs. It also helps him tackle challenging tasks and maintain multiple workflows. From multitasking with several apps to rendering on the fly, RTX technology helps Nuñez easily keep up with creative tasks.

And lastly, the Lenovo ThinkPad P1 has a color-calibrated screen. Nuñez finds this preset feature particularly helpful, as it lets him see vibrant colors in his designs without having to worry about screen reflections.

All of these benefits make the ThinkPad P1 the ideal workstation for working under any scenario, the artist said. With the accelerated workflows it enables, as well as the ability to see his designs running in real time, Nuñez can finalize his 3D character designs faster than ever.

Image courtesy of Marlon Nuñez.

NVIDIA RTX Accelerates Creative Development

Nuñez creates extremely detailed 3D creations and characters, which means rendering and look development take a significant amount of time. RTX graphics cards enable unique ray-tracing capabilities that allow Nuñez to easily speed up his overall development process.

“I decided to test the NVIDIA RTX on my new Lenovo ThinkPad P1, and I was pretty shocked at how well it performed inside Unreal Engine 5 with ray tracing enabled,” said Nuñez. “I created the layout, played with the alembic hair and shaders, and used the sequencer on the scene — it was very responsive all the time.”

Ray tracing also enables artists to access extreme precision when it comes to life-like lighting. Because ray-tracing technology automatically renders light behavior in a physically accurate way, Nuñez doesn’t have to manually adjust render settings or complex setups.

Nuñez believes real-time ray tracing is already making a big difference across industries, especially for virtual productions and game development. With the help of an NVIDIA RTX GPU on a mobile workstation, creators can perform complex tasks in less time, from any location.

Learn more about NVIDIA RTX Laptop GPUs and watch Marlon Nuñez talk about his workflow below:

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