컨퍼런스 워크숍 및 트레이닝

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:

- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging Nsight Systems to guide and check your work

Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. In this training, you'll:

- Use cuDF and Dask to ingest and manipulate massive datasets directly on the GPU
- Apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH, and cuML, to perform data analysis at massive scale
- Perform multiple analysis tasks on massive datasets in an effort to stave off a simulated epidemic outbreak affecting the UK

Upon completion, you'll be able to load, manipulate, and analyze data orders of magnitude faster than before, enabling more iteration cycles and drastically improving productivity.

You’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

- Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
- See how Transformer architecture features, especially self-attention, are used to create language models without RNNs
- Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results
- Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
- Manage inference challenges and deploy refined models for live applications

The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

- Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)
- Build and compare supervised learning with unsupervised learning-based solutions
- Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This course will teach you how to use multiple GPUs to train neural networks. You'll learn:

- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using Horovod.

Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the DRIVE development platform.

You'll learn how to:

- Work with CUDA code, memory management, and GPU acceleration on the DRIVE AGX
- Train a semantic segmentation neural network
- Optimize, validate, and deploy a trained neural network using TensorRT

Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.

Writing CUDA C++ applications that efficiently and correctly utilize all available GPUs on a node drastically improves performance over single-GPU code, and makes the most cost-effective use out of compute nodes with multiple GPUs. In this workshop you will learn to utilize multiple GPUs on a single node by:

- Learning how to launch kernels on multiple GPUs, each working on a subsection of the required work
- Learning how to use concurrent CUDA Streams to overlap memory copy with computation on multiple GPUs

Upon completion, you will be able to build robust and efficient CUDA C++ applications that can leverage all available GPUs on a single node.

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

- Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)
- Build and compare supervised learning with unsupervised learning-based solutions
- Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:

- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging Nsight Systems to guide and check your work

Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

You’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

- Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
- See how Transformer architecture features, especially self-attention, are used to create language models without RNNs
- Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results
- Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
- Manage inference challenges and deploy refined models for live applications

Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the DRIVE development platform.

You'll learn how to:

- Work with CUDA code, memory management, and GPU acceleration on the DRIVE AGX
- Train a semantic segmentation neural network
- Optimize, validate, and deploy a trained neural network using TensorRT

Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.

Writing CUDA C++ applications that efficiently and correctly utilize all available GPUs on a node drastically improves performance over single-GPU code, and makes the most cost-effective use out of compute nodes with multiple GPUs. In this workshop you will learn to utilize multiple GPUs on a single node by:

- Learning how to launch kernels on multiple GPUs, each working on a subsection of the required work
- Learning how to use concurrent CUDA Streams to overlap memory copy with computation on multiple GPUs

Upon completion, you will be able to build robust and efficient CUDA C++ applications that can leverage all available GPUs on a single node.

RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. In this training, you'll:

- Use cuDF and Dask to ingest and manipulate massive datasets directly on the GPU
- Apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH, and cuML, to perform data analysis at massive scale
- Perform multiple analysis tasks on massive datasets in an effort to stave off a simulated epidemic outbreak affecting the UK

Upon completion, you'll be able to load, manipulate, and analyze data orders of magnitude faster than before, enabling more iteration cycles and drastically improving productivity.

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This course will teach you how to use multiple GPUs to train neural networks. You'll learn:

- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using Horovod.

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

With the increase in traffic cameras, growing prospect of autonomous vehicles, and promising outlook of smart cities, there's a rise in demand for faster and more efficient object detection and tracking models. This involves identification, tracking, segmentation and prediction of different types of objects within video frames.

In this workshop, you’ll learn how to:

- Efficiently process and prepare video feeds using hardware accelerated decoding methods
- Train and evaluate deep learning models and leverage ""transfer learning"" techniques to elevate efficiency and accuracy of these models and mitigate data sparsity issues
- Explore the strategies and trade-offs involved in developing high-quality neural network models to track moving objects in large-scale video datasets
- Optimize and deploy video analytics inference engines by acquiring the DeepStream SDK

Upon completion, you'll be able to design, train, test and deploy building blocks of a hardware-accelerated traffic management system based on parking lot camera feeds.

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This course will teach you how to use multiple GPUs to train neural networks. You'll learn:

- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using Horovod.

컴퓨터 비전과 자연어 처리의 실습 예제를 통해 딥 러닝 구조를 학습합니다. 정확도를 높이기 위한 툴이나 방법을 학습해 딥 러닝 모델을 스크래치 레벨부터 단계별로 훈련시키고 목적에 맞게 자유롭게 활용하는 방법도 배우게 됩니다. 또한 무료로 제공되는 최첨단의 사전 훈련된 모델을 활용하여 시간을 절약하고 딥 러닝 애플리케이션을 신속하게 실행하는 방법을 배울 수 있습니다.

워크숍에 참여함으로서, 다음의 내용을 배우게 됩니다.

- 딥 러닝 모델 트레이닝에 필요한 기본 기술과 도구들을 배울 수 있습니다.
- 일반적인 딥 러닝 데이터 유형 및 모델 아키텍처에 대한 경험을 쌓을 수 있습니다.
- 모델 정확도 향상을 위해 데이터 증가를 통해 데이터세트를 강화할 수 있습니다.
- 모델 간 전이 학습(Transfer Learning)을 활용해 더 적은 데이터와 컴퓨팅 성능으로 효율적인 결과를 달성할 수 있습니다.
- 첨단 딥 러닝 프레임워크로 자체 프로젝트를 수행할 수 있는 자신감을 획득할 수 있습니다.

CUDA 컴퓨팅 플랫폼은 세계에서 가장 빠른 대규모 병렬 GPU에서 CPU 전용 애플리케이션을 가속화할 수 있도록 지원합니다. 다음을 통해 C/C++ 애플리케이션 가속화를 경험하십시오.

- GPU에서 잠재 병렬 처리를 실행하기 위해 CPU 전용 애플리케이션 가속화
- 가속화된 애플리케이션을 최적화하기 위해 필수적인 CUDA 메모리 관리 기법 활용
- 가속화된 애플리케이션의 동시 실행에 대한 잠재성을 노출하고 CUDA 스트림으로 이를 활용
- 작업 가이드 및 확인을 위해 Nsight 시스템 활용법

교육 수강 시 가장 필수적인 CUDA 기술과 Nsight 시스템을 사용하여 기존 C/C++ CPU 전용 애플리케이션을 가속화하고 최적화할 수 있습니다. 가속화된 애플리케이션을 빠르게 전송할 수 있는 반복적인 CUDA 개발 방식을 이해할 수 있습니다.

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

You’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

- Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
- See how Transformer architecture features, especially self-attention, are used to create language models without RNNs
- Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results
- Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
- Manage inference challenges and deploy refined models for live applications