A digital twin is a continuously updated virtual representation — a true-to-reality simulation of physics and materials — of a real-world physical asset or system.
The post What Is a Digital Twin? appeared first on The Official NVIDIA Blog.
A digital twin is a continuously updated virtual representation — a true-to-reality simulation of physics and materials — of a real-world physical asset or system.
The post What Is a Digital Twin? appeared first on The Official NVIDIA Blog.
It was the kind of message Connor McCluskey loves to find in his inbox. As a member of the product innovation team at FirstEnergy Corp. — an electric utility serving 6 million customers from central Ohio to the New Jersey coast — his job is to find technologies that open new revenue streams or cut Read article >
The post Startup Surge: Utility Feels Power of Computer Vision to Track its Lines appeared first on The Official NVIDIA Blog.
I’m following a tutorial that user `ndim`, for example:
scalar = tf.constant(7) scalar.ndim
However, I can’t find `ndim` in the attribute section of the API docs for `tf.Tensor`
Where should I be looking for this?
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I am training an ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8 model from the Tensorflow Model Zoo. I used the default steps of 25000 but my total_loss metric is 0.852. Do I let the model train until all the step are complete or do I stop the training? Also how would I go about stopping the training? Thanks.
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2021 trends are charging into 2022, heralding a new era of autonomous transportation and opening up business models and services never before dreamed of. In the next year, software-defined compute architectures, electric powertrains, high-fidelity simulation, AI assistants and autonomous trucking solutions are set to transform the transportation industry. This past year, key technologies saw significant Read article >
The post Sensing What’s Ahead in 2022: Latest Breakthroughs Pave Way for Year of Autonomous Vehicle Innovation appeared first on The Official NVIDIA Blog.
The latest NVIDIA HPC SDK includes a variety of tools to maximize developer productivity, as well as the performance, and portability of HPC applications.
At the Supercomputing Conference (SC21) NVIDIA preannounced the next update to the HPC SDK. Today, the HPC SDK 21.11 release was posted for free download to Developer Program members.
The NVIDIA HPC SDK is a comprehensive suite of compilers and libraries for high performance computing development. It includes a wide variety of tools proven to maximize developer productivity, as well as the performance and portability of HPC applications.
The HPC SDK and its components are updated numerous times per year with new features, performance advancements, and other enhancements.
This 21.11 release will include updates to HPC C++/Fortran compiler support and the developer environment, as well as new multi-node mulitGPU library capabilities.
Multi-horizon forecasting, i.e. predicting variables-of-interest at multiple future time steps, is a crucial challenge in time series machine learning. Most real-world datasets have a time component, and forecasting the future can unlock great value. For example, retailers can use future sales to optimize their supply chain and promotions, investment managers are interested in forecasting the future prices of financial assets to maximize their performance, and healthcare institutions can use the number of future patient admissions to have sufficient personnel and equipment.
Deep neural networks (DNNs) have increasingly been used in multi-horizon forecasting, demonstrating strong performance improvements over traditional time series models. While many models (e.g., DeepAR, MQRNN) have focused on variants of recurrent neural networks (RNNs), recent improvements, including Transformer-based models, have used attention-based layers to enhance the selection of relevant time steps in the past beyond the inductive bias of RNNs – sequential ordered processing of information including. However, these often do not consider the different inputs commonly present in multi-horizon forecasting and either assume that all exogenous inputs are known into the future or neglect important static covariates.
Multi-horizon forecasting with static covariates and various time-dependent inputs. |
Additionally, conventional time series models are controlled by complex nonlinear interactions between many parameters, making it difficult to explain how such models arrive at their predictions. Unfortunately, common methods to explain the behavior of DNNs have limitations. For example, post-hoc methods (e.g., LIME and SHAP) do not consider the order of input features. Some attention-based models are proposed with inherent interpretability for sequential data, primarily language or speech, but multi-horizon forecasting has many different types of inputs, not just language or speech. Attention-based models can provide insights into relevant time steps, but they cannot distinguish the importance of different features at a given time step. New methods are needed to tackle the heterogeneity of data in multi-horizon forecasting for high performance and to render these forecasts interpretable.
To that end, we announce “Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting”, published in the International Journal of Forecasting, where we propose the Temporal Fusion Transformer (TFT), an attention-based DNN model for multi-horizon forecasting. TFT is designed to explicitly align the model with the general multi-horizon forecasting task for both superior accuracy and interpretability, which we demonstrate across various use cases.
Temporal Fusion Transformer
We design TFT to efficiently build feature representations for each input type (i.e., static, known, or observed inputs) for high forecasting performance. The major constituents of TFT (shown below) are:
TFT inputs static metadata, time-varying past inputs and time-varying a priori known future inputs. Variable Selection is used for judicious selection of the most salient features based on the input. Gated information is added as a residual input, followed by normalization. Gated residual network (GRN) blocks enable efficient information flow with skip connections and gating layers. Time-dependent processing is based on LSTMs for local processing, and multi-head attention for integrating information from any time step. |
Forecasting Performance
We compare TFT to a wide range of models for multi-horizon forecasting, including various deep learning models with iterative methods (e.g., DeepAR, DeepSSM, ConvTrans) and direct methods (e.g., LSTM Seq2Seq, MQRNN), as well as traditional models such as ARIMA, ETS, and TRMF. Below is a comparison to a truncated list of models.
Model | Electricity | Traffic | Volatility | Retail |
ARIMA | 0.154 (+180%) | 0.223 (+135%) | – | – |
ETS | 0.102 (+85%) | 0.236 (+148%) | – | – |
DeepAR | 0.075 (+36%) | 0.161 (+69%) | 0.050 (+28%) | 0.574 (+62%) |
Seq2Seq | 0.067 (+22%) | 0.105 (+11%) | 0.042 (+7%) | 0.411 (+16%) |
MQRNN | 0.077 (+40%) | 0.117 (+23%) | 0.042 (+7%) | 0.379 (+7%) |
TFT | 0.055 | 0.095 | 0.039 | 0.354 |
P50 quantile losses (lower is better) for TFT vs. alternative models. |
As shown above, TFT outperforms all benchmarks over a variety of datasets. This applies to both point forecasts and uncertainty estimates, with TFT yielding an average 7% lower P50 and 9% lower P90 losses, respectively, compared to the next best model.
Interpretability Use Cases
We demonstrate how TFT’s design allows for analysis of its individual components for enhanced interpretability with three use cases.
Variable importance for the retail dataset. The 10th, 50th, and 90th percentiles of the variable selection weights are shown, with values larger than 0.1 in bold purple. |
The above shows the attention weight patterns across time, indicating how TFT learns persistent temporal patterns without any hard-coding. Such capability can help build trust with users because the output confirms expected known patterns. Model developers can also use these towards model improvements, e.g., via specific feature engineering or data collection.
Event identification for S&P 500 realized volatility from 2002 through 2014. |
Significant deviations in attention patterns can be observed above around periods of high volatility, corresponding to the peaks observed in dist(t), distance between attention patterns (red line). We use a threshold to denote significant events, as highlighted in purple.
Focusing on periods around the 2008 financial crisis, the bottom plot below zooms on midway through the significant event (evident from the increased attention on sharp trend changes), compared to the normal event in the top plot (where attention is equal over low volatility periods).
Event identification for S&P 500 realized volatility, a zoom of the above on a period from 2004 and 2005. |
Event identification for S&P 500 realized volatility, a zoom of the above on a period from 2008 and 2009. |
Real-World Impact
Finally, TFT has been used to help retail and logistics companies with demand forecasting by both improving forecasting accuracy and providing interpretability capabilities.
Additionally, TFT has potential applications for climate-related challenges: for example, reducing greenhouse gas emissions by balancing electricity supply and demand in real time, and improving the accuracy and interpretability of rainfall forecasting results.
Conclusion
We present a novel attention-based model for high-performance multi-horizon forecasting. In addition to improved performance across a range of datasets, TFT also contains specialized components for inherent interpretability — i.e., variable selection networks and interpretable multi-head attention. With three interpretability use-cases, we also demonstrate how these components can be used to extract insights on feature importance and temporal dynamics.
Acknowledgements
We gratefully acknowledge contributions of Bryan Lim, Nicolas Loeff, Minho Jin, Yaguang Li, and Andrew Moore.
Creators and developers can now view their 3D content as it was meant to be experienced, in full immersive detail with NVIDIA Omniverse XR Remote for iPad.
Content creators and developers can now view their 3D content in full-immersive detail with NVIDIA Omniverse XR Remote for iPad. The app is available now from the Apple Store for iPad iOS 14.5 and higher.
Visualizing complex 3D models is critical to industries, such as architecture and manufacturing, where context is everything. Minor design decisions can trigger changes that lead to higher costs and time-consuming adjustments.
Omniverse XR Remote addresses this challenge by enabling users to interact with full-fidelity, real-time NVIDIA RTX ray-traced content in Omniverse. This can be streamed directly from a desktop to an iPad using NVIDIA CloudXR. Content is viewed in AR, where users bring virtual assets into their world, or as a VR virtual camera that gives users a “window” to navigate a 3D scene or experience.
For developers, this provides a new means to distribute content built in Omniverse, without compromising on quality or mobility.
Kohn Pedersen Fox Associates (KPF), one of the world’s preeminent architecture firms, is leveraging NVIDIA technologies to make the design process more intuitive for designers, engineers, and clients.
“We see a future where we can bring our design to the table and the computer helps us make it real,” said Applied Research Director at KPF, Cobus Bothma.
Bothma is using XR Remote to visualize at-scale architectural models overlaid with complex data sets—like diagrammatic flow lines of wind around a building, creating a virtual wind tunnel. Currently, KPF is working to simulate multiple buildings in the same scene, viewed on a tablet device.
“With XR Remote, we can reduce review cycles from days to hours,” said Bothma.
Historically, it would have taken the KPF design team 4 to 6 weeks to develop a custom app for every design change. “Now we can simply stream it to them and they will immediately have the latest view,” Bothma said.
The application delivers an immersive view of Universal Scene Description content from Omniverse to any supported iOS or Android device through the use of the NVIDIA CloudXR streaming solution. XR Remote is one of the first instances where users can leverage CloudXR streaming to reach back through a remote agent and harness extra compute power. This helps users run a simulation in Omniverse and stream full-fidelity graphics to an iPad in real time.
The result is a fully immersive interaction with 3D content, which enables easier collaboration to speed up design processes. “This is a much more intuitive way to interact with 3D content than a mouse and keyboard,” said Greg Jones, Director of Global Business Development and Product Management for XR at NVIDIA.
“With XR Remote, users can grab the iPad and literally walk through their data. This changes the game for industries like AEC, manufacturing, and M&E, where flat digital tools have required designers to translate 2D renderings into 3D results,” Jones said.
The Omniverse XR Remote application is available now through the Apple Store and Android devices.
Load a 3D model and enable AR settings in Omniverse Create on a PC to get started. Then input the corresponding IP address into the Omniverse XR Remote on their iPad. Check out the XR Remote documentation for detailed instructions.
Android tablet users can follow these steps and connect a device using NVIDIA Omniverse XR Remote.
Expand the design process and view 3D content as it was meant to be experienced, in full immersive detail. Download NVIDIA Omniverse XR Remote for iPad today from the Apple Store.
In an evaluation of enterprise AI infrastructure providers, Forrester Research Monday recognized NVIDIA as a leader in AI infrastructure. The “Forrester Wave™: AI Infrastructure, Q4 2021” report states that “NVIDIA’s DNA is in every other AI infrastructure solution we evaluated. It’s an understatement to say that NVIDIA GPUs are synonymous with AI infrastructure.” “Reference customers Read article >
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‘Tis the season for all content creators, especially 3D artists, this month on NVIDIA Studio. Blender, the world’s most popular open-source 3D creative application, launched a highly anticipated 3.0 release, delivering extraordinary performance gains powered by NVIDIA RTX GPUs, with added Universal Scene Description (USD) support for NVIDIA Omniverse. Faster 3D creative workflows are made Read article >
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