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Misc

GTC 21: Top 5 Data Center Networking Sessions

Attend GTC to learn more about breakthroughs in data center and cloud networking, including optimized modern workloads and programmable data center infrastructure.

NVIDIA GTC is starting on April 12 with a special focus this year on breakthroughs in data center and cloud networking, including optimized modern workloads and programmable data center infrastructure. Join us to explore advanced technologies and strategies for maximizing your data center networking performance and improve ROI. 

  1. Program Data Center Infrastructure Acceleration with the release of DOCA and the latest DPU software

    NVIDIA is releasing the first version of DOCA, a set of libraries, SDKs, and tools for programming the NVIDIA BlueField DPU, as well as the new version 3.6 of the data-processing unit (DPU) software. Together, these enable new infrastructure acceleration and management features in BlueField and simplify programming and application integration. DPU developers can offload and accelerate networking, virtualization, security, and storage features including VirtIO for NFV/VNFs, NVMe SNAP for storage virtualization, regular expression matching for malware detection, and deep packet inspection to enable sophisticated routing, firewall, and load-balancing applications.

    Ariel Kit, Director of Product Marketing for Networking, NVIDIA
    Ami Badani, Vice President of Marketing, NVIDIA

  1. How to Optimize Modern Workloads Efficiency over Next Generation Hybrid Cloud Solution Architecture

    Modern hybrid cloud solutions are shifting to software-defined networking and software-defined storage, which, combined with the traditional server virtualization, put a heavy load on the CPU as it needs to process more demanding storage, networking, and security infrastructure applications and often competes with revenue-generating workloads for CPU, memory, and I/O resources. This drives the need for a new, more efficient, data center architecture that’s easy to deploy, operate, and scale. Learn how VMware’s Project Monterey over NVIDIA’s BlueField-2 DPU enables IT personnel to deploy hybrid cloud clusters that can deliver on its goals while meeting organizational business objectives in the most efficient way.

    Sudhanshu (Suds) Jain, Director of Product Management, VMware
    Motti Beck, Senior Director, Enterprise Market Development, NVIDIA

  1. Turbocharge Red Hat OpenShift Container Platform with High Performance and Efficient Networking

    Cloud-native applications based on Kubernetes, containers, and microservices are rapidly growing in popularity. These modern workloads are distributed, data-intensive, and latency-sensitive by design. Therein lies the need for fast and super-efficient networking to achieve a predictable and consistent user experience and performance while using cloud-native applications. Learn how NVIDIA Mellanox Networking turbocharges Red Hat’s OpenShift cloud platform with hardware-accelerated, software-defined cloud-native networking. NVIDIA and Red Hat work together to boost the performance and efficiency of modern cloud infrastructure, delivering a delightful customer experience to enterprises and cloud operators alike.

    Erez Cohen, Mellanox VP CloudX Program, NVIDIA
    Marc Curry, Senior Principal Product Manager, OpenShift, Cloud Platform BU, Red Hat

  1. NVIDIA DGX Ethernet Fabric Design and Automated Deployment

    In order for a DGX pod to deliver the highest levels of AI performance, it needs a network configured to deliver the bandwidth, latency, and lossless characteristics necessary to feed the GPUs and high-speed storage devices attached to it. We’ll describe the requirements and design for an all-Ethernet DGX deployment. We’ll also demo how to automate and validate the deployment of NVIDIA DGX servers and NVIDIA networking.

    Pete Lumbis, Director Technical Marketing and Documentation, NVIDIA

  1. Apache Spark Acceleration over VMware’s Tanzu with NVIDIA’s GPU and Networking Solutions

    Apache Spark is an open-source project that’s achieved wide popularity in the analytical space. It’s used by well-known big data and machine learning workloads such as streaming, processing a wide array of datasets, and ETL, to name a few. Kubernetes is now a native option for Spark resource manager. By packaging Spark application as a container, you can reap the benefits of containers because you package your dependencies along with your application as a single entity.

    Boris Kovalev, Staff Solutions Architect, NVIDIA
    Mohan Potheri, Staff Solutions Architect, VMware

Visit the GTC website to register for GTC (free) and to learn more about our Data Center Networking track.

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Misc

GTC 21: Top 5 NVIDIA AI/DL Technical Sessions

With more than 1,400 sessions including the latest deep learning technologies in conversational AI, recommender systems, computer vision, and video streaming, here is a preview of some of the top AI/DL sessions.

NVIDIA GTC is coming up starting on April 12th with more than 1,400 sessions including the latest deep learning technologies in conversational AI, recommender systems, computer vision, and video streaming. 

Here’s a preview of some of the top AI/DL sessions at GTC. 

  1. Building & Deploying a Custom Conversational AI App with NVIDIA Transfer Learning Toolkit and Jarvis
    Tailoring the deep learning models in a Conversational AI pipeline to the needs of your enterprise is time-consuming. Deployment for a domain-specific application typically requires several cycles of re-training, fine-tuning, and deploying the model until it satisfies the requirements. In this session, we will walk you through the process of customizing ASR and NLP pipelines to build a truly customized production-ready Conversational AI application that is fine-tuned to your domain.

    Arun Venkatesan, Product Manager, Deep Learning Software, NVIDIA
    Nikhil Srihari, Deep Learning Software Technical Marketing Engineer, NVIDIA

  1. Accelerated ETL, Training and Inference of Recommender Systems on the GPU with Merlin, HugeCTR, NVTabular, and Triton
    Learn how the Merlin framework, consisting of NVTabular for ETL, HugeCTR for training, and Triton for inference serving. Merlin accelerates recommender systems on GPU, speeding up common ETL tasks, training of models, and inference serving by ~10x over commonly used methods. Beyond providing better performance, these libraries are also designed to be easy to use and integrate with existing recommendation pipelines.

    Even Oldridge, Senior Manager, Recommender Systems Framework Team, NVIDIA

  1. Accelerating AI Workflows with NGC
    This session will walk through building a conversational AI solution using the artifacts from the NGC catalog, including a Jupyter notebook, so the process can be repeated offline. It will also cover the benefits of using NGC software throughout AI development journeys.

    Adel El Hallak, Director of Product Management for NGC, NVIDIA
    Chris Parsons, Product Manager, NGC, NVIDIA

  1.  NVIDIA Maxine: An Accelerated Platform SDK for Developers of Video Conferencing Services
    NVIDIA Maxine, such as how applications based on Maxine can reduce video bandwidth usage down to one-tenth of H.264 using AI video compression. Also see the latest innovations from NVIDIA research, such as face alignment, gaze correction, face re-lighting and real-time translation, in addition to capabilities such as super-resolution, noise removal, closed captioning and virtual assistants.

    Davide Onofrio, Technical Marketing Engineer Lead, NVIDIA
    Abhijit Patait, Director, System Software, NVIDIA
    Abhishek Sawarkar, Deep Learning Software Technical Marketing Engineer, NVIDIA
    Tanay Varshney, Technical Marketing Engineer, Deep Learning, NVIDIA
    Alex Qi, Product Manager, AI Software, NVIDIA

  1. Easily Deploy AI Deep Learning Models at Scale with Triton Inference Server
    Triton Inference Server is a model serving software that simplifies the deployment of AI models at scale in production. It’s an open-source serving software that lets teams deploy trained AI models from any framework on any GPU- or CPU-based infrastructure. Learn about high performance inference serving with Triton’s concurrent execution, dynamic batching, and integrations with Kubernetes and other tools.

    Mahan Salehi, Deep Learning Software Product Manager, NVIDIA

Register today for GTC or explore more deep learning sessions to learn about the latest breakthroughs in AI applications for computer vision, conversational AI, and recommender systems.

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Misc

Accelerating Analytics and AI with Alluxio and NVIDIA GPUs

In this tutorial, we take a look at using Alluxio Data Orchestration across multiple stages of a data processing pipeline, from ETL to analytics and AI/ML.

Categories
Misc

Beginner’s Guide to GPU Accelerated Graph Analytics in Python

This tutorial is the sixth installment of introductions to the RAPIDS ecosystem.

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Misc

GTC 21: Top 5 Automotive Technical Sessions

Register for the free conference to hear talks from Audi Board Member Hildegard Wortmann, Zoox CTO Jesse Levinson, University of Toronto Professor Raquel Urtasun, and Cruise SVP of Engineering Mo ElShenawy.

NVIDIA GTC is returning with a special focus on autonomous vehicles, including talks from Audi Board Member Hildegard Wortmann, Zoox CTO Jesse Levinson, University of Toronto Professor Raquel Urtasun, and Cruise SVP of Engineering Mo ElShenawy. GTC is free to attend – you and your team can register here. 

This free five-day digital conference kicks off April 12 with NVIDIA CEO Jensen Huang’s keynote followed by 1,400+ talks ranging from technical deep dives to business-focused talks by C-level leaders. You won’t want to miss it.

Here are some session highlights for autonomous vehicle development:

  1. Deep-Sensor Fusion of Thermal and Visible Cameras for Autonomous Driving

    See the latest research on fusing visual and thermal sensors for state-of-the-art segmentation accuracy in autonomous driving.

    Vijay John, Assistant Professor, Toyota Technological Institute

  1. Understanding Safety and Security Standards for Autonomous Vehicles: An NVIDIA DRIVE Approach

    Learn how to more easily integrate the NVIDIA DRIVE platform into your safe and secure AV designs with NVIDIA’s safety approach.

    Karl Greb, Safety Engineering Director, NVIDIA
    Riccardo Mariani, VP of Industry Safety, NVIDIA

  1. Autonomous Valet Parking Powered by NVIDIA DRIVE

    This session will cover how Apex.AI developed a modular approach to production autonomous parking.

    Dejan Pangercic, CTO, Apex.AI

  1. Human-Guided Autonomous Convoys

    This presentation highlights the operations and challenges of deploying an autonomous convoy system for long-haul trucking.

    Cetin Mericli, CEO, Locomation

  1. Deflating Dataset Bias Using Synthetic Data Augmentation

    This session will show how targeted synthetic data augmentation can help fill gaps in static datasets for vision tasks.

    Nikita Jaipuria, Research Scientist, Ford Motor Company

Additionally, from April 20-22, be sure to check out NVIDIA DRIVE Developer Days, which will consist of deep dive sessions on safe and robust AV development. These are also FREE and will be available via the GTC session catalog.

Categories
Misc

NVIDIA Releases Updates to CUDA-X AI Software

NVIDIA CUDA-X AI are deep learning libraries for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision.

NVIDIA CUDA-X AI are deep learning libraries for researchers and software developers to build high performance GPU-accelerated applications for conversational AI, recommendation systems and computer vision.

Learn what’s new in the latest releases of CUDA-X AI libraries.

Refer to each package’s release notes in documentation for additional information.

NVIDIA Jarvis Open Beta 

NVIDIA Jarvis is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs. This version of Jarvis includes:

  • ASR, NLU, and TTS models trained on thousands of hours of speech data.
  • Transfer Learning Toolkit with zero coding approach to re-train on custom data.
  • Fully accelerated deep learning pipelines optimized to run as scalable services.
  • End-to-end workflow and tools to deploy services using one line of code.

Transfer Learning Toolkit 3.0 Developer Preview

NVIDIA released new pre-trained models for computer vision and conversational AI that can be easily fine-tuned with Transfer Learning Toolkit (TLT) 3.0 with a zero-coding approach. 

Key highlights: 

  • New vision AI pre-trained models: license plate detection and recognition, heart rate monitoring, gesture recognition, gaze estimation, emotion recognition, face detection, and facial landmark estimation 
  • Newly added support for automatic speech recognition (ASR) and natural language processing (NLP) 
  • Choice of training with popular network architectures such as EfficientNet, YoloV4, and UNET
  • Support for NVIDIA Ampere GPUs with third-generation tensor cores for performance boost 

Triton Inference Server 2.7 

Triton Inference Server is an open source multi-framework, cross platform inference serving software designed to simplify model production deployment. Version 2.7 includes:

  • Model Analyzer – automatically finds best model configuration to maximize performance based on user-specified requirements 
  • Model Repo Agent API  – enables custom operations to be performed to models being loaded (such as decrypting, checksumming, applying TF-TRT optimization, etc)
  • Added support for ONNX Runtime backend in Triton Windows build
  • Added an example Java and Scala client based on GRPC-generated API

Read full release notes here. 

TensorRT 7.2 is Now Available

NVIDIA TensorRT is a platform for high-performance deep learning inference. This version of TensorRT includes:

  • New Polygraphy toolkit, assists in prototyping and debugging deep learning models in various frameworks
  • Support for Python 3.8

Merlin Open Beta

Merlin is an application framework and ecosystem that enables end-to-end development of recommender systems, accelerated on NVIDIA GPUs. Merlin Open Beta highlights include:

  • NVTabular and HugeCTR inference support in Triton Inference Server
  • Cloud configurations and cloud support (AWS/GCP)
  • Dataset analysis and generation tools
  • New PythonAPI for HugeCTR similar to Keras with no JSON configuration anymore

DeepStream SDK 5.1

NVIDIA DeepStream SDK is a streaming analytics toolkit for AI-based multi-sensor processing. 

Key highlights for DeepStream SDK 5.1 (General Availability) 

  • New Python apps for using optical flow, segmentation networks, and analytics using ROI and line crossing
  • Support for audio analytics with a sample application highlighting audio classifier usage
  • Support for NVIDIA Ampere GPUs with third-generation tensor cores and various performance optimizations

nvJPEG2000 0.2 

nvJPEG2000 is a new library for GPU-accelerated JPEG2000 image decoding. This version of nvJPEG2000 includes:

NVIDIA NeMo 1.0.0b4

NVIDIA NeMo is a toolkit to build, train and fine-tune state-of-the-art speech and language models easily. Highlights of this version include:

  • Compatible with Jarvis 1.0.0b2 public beta and TLT 3.0 releases

Deep Learning Examples

Deep Learning Examples provide state-of-the-art reference examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X AI software stack running on NVIDIA Volta, Turing, and Ampere GPUs.

New Model Scripts available from the NGC Catalog:

  • nnUNet/PyT: A Self-adapting Framework for U-Net for state-of-the-art Segmentation across distinct entities, image modalities, image geometries, and dataset sizes, with no manual adjustments between datasets. 
  • Wide and Deep/TF2: Wide & Deep refers to a class of networks that use the output of two parts working in parallel – wide model and deep model – to make a binary prediction of CTR. 
  • EfficientNet PyT & TF2: A model that scales the depth, width, and resolution to achieve better performance across different datasets. EfficientNet B4 achieves state-of-the-art 82.78% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet.
  • Electra: A novel pre-training method for language representations which outperforms existing techniques, given the same compute budget on a wide array of Natural Language Processing (NLP) tasks.
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Offsites

Constructing Transformers For Longer Sequences with Sparse Attention Methods

Posted by Avinava Dubey, Research Scientist, Google Research

Natural language processing (NLP) models based on Transformers, such as BERT, RoBERTa, T5, or GPT3, are successful for a wide variety of tasks and a mainstay of modern NLP research. The versatility and robustness of Transformers are the primary drivers behind their wide-scale adoption, leading them to be easily adapted for a diverse range of sequence-based tasks — as a seq2seq model for translation, summarization, generation, and others, or as a standalone encoder for sentiment analysis, POS tagging, machine reading comprehension, etc. The key innovation in Transformers is the introduction of a self-attention mechanism, which computes similarity scores for all pairs of positions in an input sequence, and can be evaluated in parallel for each token of the input sequence, avoiding the sequential dependency of recurrent neural networks, and enabling Transformers to vastly outperform previous sequence models like LSTM.

A limitation of existing Transformer models and their derivatives, however, is that the full self-attention mechanism has computational and memory requirements that are quadratic with the input sequence length. With commonly available current hardware and model sizes, this typically limits the input sequence to roughly 512 tokens, and prevents Transformers from being directly applicable to tasks that require larger context, like question answering, document summarization or genome fragment classification. Two natural questions arise: 1) Can we achieve the empirical benefits of quadratic full Transformers using sparse models with computational and memory requirements that scale linearly with the input sequence length? 2) Is it possible to show theoretically that these linear Transformers preserve the expressivity and flexibility of the quadratic full Transformers?

We address both of these questions in a recent pair of papers. In “ETC: Encoding Long and Structured Inputs in Transformers”, presented at EMNLP 2020, we present the Extended Transformer Construction (ETC), which is a novel method for sparse attention, in which one uses structural information to limit the number of computed pairs of similarity scores. This reduces the quadratic dependency on input length to linear and yields strong empirical results in the NLP domain. Then, in “Big Bird: Transformers for Longer Sequences”, presented at NeurIPS 2020, we introduce another sparse attention method, called BigBird that extends ETC to more generic scenarios where prerequisite domain knowledge about structure present in the source data may be unavailable. Moreover, we also show that theoretically our proposed sparse attention mechanism preserves the expressivity and flexibility of the quadratic full Transformers. Our proposed methods achieve a new state of the art on challenging long-sequence tasks, including question answering, document summarization and genome fragment classification.

Attention as a Graph
The attention module used in Transformer models computes similarity scores for all pairs of positions in an input sequence. It is useful to think of the attention mechanism as a directed graph, with tokens represented by nodes and the similarity score computed between a pair of tokens represented by an edge. In this view, the full attention model is a complete graph. The core idea behind our approach is to carefully design sparse graphs, such that one only computes a linear number of similarity scores.

Full attention can be viewed as a complete graph.

Extended Transformer Construction (ETC)
On NLP tasks that require long and structured inputs, we propose a structured sparse attention mechanism, which we call Extended Transformer Construction (ETC). To achieve structured sparsification of self attention, we developed the global-local attention mechanism. Here the input to the Transformer is split into two parts: a global input where tokens have unrestricted attention, and a long input where tokens can only attend to either the global input or to a local neighborhood. This achieves linear scaling of attention, which allows ETC to significantly scale input length.

In order to further exploit the structure of long documents, ETC combines additional ideas: representing the positional information of the tokens in a relative way, rather than using their absolute position in the sequence; using an additional training objective beyond the usual masked language model (MLM) used in models like BERT; and flexible masking of tokens to control which tokens can attend to which other tokens. For example, given a long selection of text, a global token is applied to each sentence, which connects to all tokens within the sentence, and a global token is also applied to each paragraph, which connects to all tokens within the same paragraph.

An example of document structure based sparse attention of ETC model. The global variables are denoted by C (in blue) for paragraph, S (yellow) for sentence while the local variables are denoted by X (grey) for tokens corresponding to the long input.

With this approach, we report state-of-the-art results in five challenging NLP datasets requiring long or structured inputs: TriviaQA, Natural Questions (NQ), HotpotQA, WikiHop, and OpenKP.

Test set result on Question Answering. For both verified TriviaQA and WikiHop, using ETC achieved a new state of the art.

BigBird
Extending the work of ETC, we propose BigBird — a sparse attention mechanism that is also linear in the number of tokens and is a generic replacement for the attention mechanism used in Transformers. In contrast to ETC, BigBird doesn’t require any prerequisite knowledge about structure present in the source data. Sparse attention in the BigBird model consists of three main parts:

  • A set of global tokens attending to all parts of the input sequence
  • All tokens attending to a set of local neighboring tokens
  • All tokens attending to a set of random tokens
BigBird sparse attention can be seen as adding few global tokens on Watts-Strogatz graph.

In the BigBird paper, we explain why sparse attention is sufficient to approximate quadratic attention, partially explaining why ETC was successful. A crucial observation is that there is an inherent tension between how few similarity scores one computes and the flow of information between different nodes (i.e., the ability of one token to influence each other). Global tokens serve as a conduit for information flow and we prove that sparse attention mechanisms with global tokens can be as powerful as the full attention model. In particular, we show that BigBird is as expressive as the original Transformer, is computationally universal (following the work of Yun et al. and Perez et al.), and is a universal approximator of continuous functions. Furthermore, our proof suggests that the use of random graphs can further help ease the flow of information — motivating the use of the random attention component.

This design scales to much longer sequence lengths for both structured and unstructured tasks. Further scaling can be achieved by using gradient checkpointing by trading off training time for sequence length. This lets us extend our efficient sparse transformers to include generative tasks that require an encoder and a decoder, such as long document summarization, on which we achieve a new state of the art.

Summarization ROUGE score for long documents. Both for BigPatent and ArXiv datasets, we achieve a new state of the art result.

Moreover, the fact that BigBird is a generic replacement also allows it to be extended to new domains without pre-existing domain knowledge. In particular, we introduce a novel application of Transformer-based models where long contexts are beneficial — extracting contextual representations of genomic sequences (DNA). With longer masked language model pre-training, BigBird achieves state-of-the-art performance on downstream tasks, such as promoter-region prediction and chromatin profile prediction.

On multiple genomics tasks, such as promoter region prediction (PRP), chromatin-profile prediction including transcription factors (TF), histone-mark (HM) and DNase I hypersensitive (DHS) detection, we outperform baselines. Moreover our results show that Transformer models can be applied to multiple genomics tasks that are currently underexplored.

Main Implementation Idea
One of the main impediments to the large scale adoption of sparse attention is the fact that sparse operations are quite inefficient in modern hardware. Behind both ETC and BigBird, one of our key innovations is to make an efficient implementation of the sparse attention mechanism. As modern hardware accelerators like GPUs and TPUs excel using coalesced memory operations, which load blocks of contiguous bytes at once, it is not efficient to have small sporadic look-ups caused by a sliding window (for local attention) or random element queries (random attention). Instead we transform the sparse local and random attention into dense tensor operations to take full advantage of modern single instruction, multiple data (SIMD) hardware.

To do this, we first “blockify” the attention mechanism to better leverage GPUs/TPUs, which are designed to operate on blocks. Then we convert the sparse attention mechanism computation into a dense tensor product through a series of simple matrix operations such as reshape, roll, and gather, as illustrated in the animation below.

Illustration of how sparse window attention is efficiently computed using roll and reshape, and without small sporadic look-ups.

Recently, “Long Range Arena: A Benchmark for Efficient Transformers“ provided a benchmark of six tasks that require longer context, and performed experiments to benchmark all existing long range transformers. The results show that the BigBird model, unlike its counterparts, clearly reduces memory consumption without sacrificing performance.

Conclusion
We show that carefully designed sparse attention can be as expressive and flexible as the original full attention model. Along with theoretical guarantees, we provide a very efficient implementation which allows us to scale to much longer inputs. As a consequence, we achieve state-of-the-art results for question answering, document summarization and genome fragment classification. Given the generic nature of our sparse attention, the approach should be applicable to many other tasks like program synthesis and long form open domain question answering. We have open sourced the code for both ETC (github) and BigBird (github), both of which run efficiently for long sequences on both GPUs and TPUs.

Acknowledgements
This research resulted as a collaboration with Amr Ahmed, Joshua Ainslie, Chris Alberti, Vaclav Cvicek, Avinava Dubey, Zachary Fisher, Guru Guruganesh, Santiago Ontañón, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang, Manzil Zaheer, who co-authored EMNLP and NeurIPS papers.

Categories
Misc

GTC 21: Top 5 Professional Visualization Sessions

Learn how you can take advantage of the latest NVIDIA technology to enable the creation of beautiful worlds quicker and easier than ever before.

This year at GTC we have several sessions for professional content creators looking to take advantage of the latest NVIDIA technology to enable the creation of beautiful worlds quicker and easier than ever before. Find out how to add life-like realism to your projects with new ray tracing features and faster real-time volumetric rendering and simulation. Also, learn how to collaborate with creators around the world seamlessly and effortlessly no matter what software you use. We have hundreds of sessions on graphics, simulation, and design to choose from. Registration is free.

These are the five graphics sessions you can’t miss: 

Image courtesy of Adobe Substance and Zhelongxu

What’s New in OptiX
Catch up with the latest additions to the OptiX SDK and learn tips and tricks on how best to implement them into your products.

NanoVDB: A GPU-Friendly and Portable VDB Data Structure for Real-Time Rendering and Simulation
Learn how NanoVDB accelerates real-time rendering and simulation of the most graphically intensive volumetric effects on NVIDIA GPUs.

An Overview of NVIDIA CloudXR
Learn all about NVIDIA CloudXR, a groundbreaking innovation for streaming VR and AR from any OpenVR application on a remote server to a client device. Get details on the architecture of the CloudXR software stack and explore the use cases.

Building Omniverse Kit Apps and Extensions
Learn how to leverage Omniverse Kit to build amazing applications and extensions.

Making a Connector for Omniverse
Learn how to connect with the Omniverse platform, send data to it, and establish a live sync session. There will also be a USD 101 overview to get you started.

Register for free and check out GTC sessions that dive into the latest technologies for graphics and simulation. A quick registration is required to view the GTC catalog with over 1,400 free sessions covering XR, graphics, simulation, design, and more.

Categories
Misc

Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2

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Categories
Misc

AI wakeword dataset single word audiofile chess

Hi, I would like to make simple AI, which can detect hotword/wakeword and I need a lot of short audiofiles with names of chess figures. How to get the dataset like that or is there another opensource hotword/wakeword detection? (STT would be too weak and common wakeword mechanisms are deprecated or commercial ;( )

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