NVIDIA GPU Programming - Extended 교육 과정

이 강사는 중간 수준의 AI 엔지니어와 데이터 과학자를 대상으로, 화웨이의 Ascend 플랫폼과 CANN 툴킷을 사용하여 신경망 모델을 개발하고 최적화하는 방법을 학습합니다. (온라인 또는 현장)

이 교육을 마친 후 참가자는 다음을 수행할 수 있습니다:

강좌 형식

• 상호작용형 강의 및 토론.
• 샘플 애플리케이션에서 Huawei Ascend와 CANN 툴킷을 직접 사용합니다.
• 모델 구축, 학습 및 배포에 중점을 둔 가이드 연습.

강좌 맞춤화 옵션

• 인프라 또는 데이터셋을 기반으로 이 강좌에 맞춘 맞춤형 교육을 요청하려면, 맞춤형 교육을 요청하기 위해 연락해 주세요.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI compute stack for deploying and optimizing AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch.

By the end of this training, participants will be able to:

• Understand the CANN architecture and its role in the AI deployment pipeline.
• Convert and adapt models from popular frameworks to Ascend-compatible formats.
• Use tools like ATC, OM model conversion, and MindSpore for edge and cloud inference.
• Diagnose deployment issues and optimize performance on Ascend hardware.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.

By the end of this training, participants will be able to:

• Understand Biren GPU architecture and memory hierarchy.
• Set up the development environment and use Biren’s programming model.
• Translate and optimize CUDA-style code for Biren platforms.
• Apply performance tuning and debugging techniques.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Biren SDK in sample GPU workloads.
• Guided exercises focused on porting and performance tuning.

Course Customization Options

• To request a customized training for this course based on your application stack or integration needs, please contact us to arrange.

Cambricon MLUs (Machine Learning 유닛)은 엣지 및 데이터 센터 시나리오에서 추론 및 학습을 위한 최적화된 전문 AI 칩입니다.

이 강사는 BANGPy 프레임워크와 Neuware SDK를 사용하여 Cambricon MLU 하드웨어에서 AI 모델을 구축하고 배포하고자 하는 중급 개발자를 대상으로 하는 온라인 또는 오프라인에서 진행되는 강의입니다.

이 강의가 끝나면 참가자들은 다음과 같은 작업을 할 수 있게 됩니다:

• BANGPy와 Neuware 개발 환경을 설정하고 구성합니다.
• Python 및 C++ 기반 모델을 Cambricon MLU에 개발하고 최적화합니다.
• Neuware 런타임을 실행하는 엣지 및 데이터 센터 장치에 모델을 배포합니다.
• MLU 특화 가속 기능을 사용한 ML 워크플로를 통합합니다.

강의 형식

• 상호작용 강의 및 토론.
• BANGPy와 Neuware를 개발 및 배포에 직접 사용하는 실습.
• 최적화, 통합 및 테스트에 중점을 둔 지도 연습.

강의 맞춤화 옵션

• 강의를 Cambricon 장치 모델 또는 사용 사례에 맞게 맞춤화한 교육을 요청하려면, 연락하여 조정을 신청하십시오.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

By the end of this training, participants will be able to:

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Huawei's Ascend CANN toolkit enables powerful AI inference on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimizing, and deploying models where compute and memory are constrained.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and integrators who wish to deploy and optimize models on Ascend edge devices using the CANN toolchain.

By the end of this training, participants will be able to:

• Prepare and convert AI models for Ascend 310 using CANN tools.
• Build lightweight inference pipelines using MindSpore Lite and AscendCL.
• Optimize model performance for limited compute and memory environments.
• Deploy and monitor AI applications in real-world edge use cases.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together to support AI lifecycle management and infrastructure decisions.

By the end of this training, participants will be able to:

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN supports model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-prem environments.

Format of the Course

• Interactive lecture and discussion.
• Live system demos and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI compute foundation that allows developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers and system engineers who wish to optimize inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development.

By the end of this training, participants will be able to:

• Understand CANN's runtime architecture and performance lifecycle.
• Use profiling tools and Graph Engine for performance analysis and optimization.
• Create and optimize custom operators using TIK and TVM.
• Resolve memory bottlenecks and improve model throughput.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with real-time profiling and operator tuning.
• Optimization exercises using edge-case deployment examples.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.

By the end of this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks like detection, classification, and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab with model deployment and performance profiling.
• Live pipeline design using real CV and NLP use cases.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

CANN TIK (Tensor Instruction Kernel) and Apache TVM enable advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at advanced-level system developers who wish to build, deploy, and tune custom operators for AI models using CANN’s TIK programming model and TVM compiler integration.

By the end of this training, participants will be able to:

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom ops into the CANN runtime and execution graph.
• Use TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

중국 GPU 아키텍처인 Huawei Ascend, Biren, Cambricon MLU는 현지 AI 및 HPC 시장을 위한 CUDA 대안으로 제공됩니다.

이 강사는 온라인 또는 현장에서 진행되는 실시간 강의로, CUDA 애플리케이션을 중국 하드웨어 플랫폼에 배포하기 위해 기존 CUDA 애플리케이션을 마이그레이션하고 최적화하려는 고급 수준의 GPU 프로그래머와 인프라 전문가를 대상으로 합니다.

이 교육을 마치면 참가자들은 다음과 같은 작업을 수행할 수 있게 됩니다:

• 기존 CUDA 작업 부하가 중국 칩 대안과의 호환성을 평가합니다.
• CUDA 코드베이스를 Huawei CANN, Biren SDK, Cambricon BANGPy 환경으로 포팅합니다.
• 플랫폼 간 성능을 비교하고 최적화 지점을 식별합니다.
• 다양한 아키텍처를 지원하고 배포하는 데 있어 실질적인 도전 과제를 해결합니다.

강좌 형식

• 상호작용 강의 및 토론
• 코드 변환 및 성능 비교 실습
• 다중 GPU 적응 전략에 중점을 둔 유도 연습

강좌 맞춤화 옵션

• 플랫폼이나 CUDA 프로젝트에 맞춘 이 강좌에 대한 맞춤형 교육을 요청하려면, 연락을 취하여 조율하십시오.

• Ascend, Biren, Cambricon 플랫폼에서 모델을 벤치마크할 수 있습니다.
• 시스템 병목 현상 및 메모리/계산 비효율성을 식별할 수 있습니다.
• 그래프 수준, 커널 수준, 그리고 연산자 수준 최적화를 적용할 수 있습니다.
• 처리량과 지연 시간을 향상시키기 위해 배포 파이프라인을 조정할 수 있습니다.

교육 형식

• 상호작용적인 강의와 토론.
• 각 플랫폼에서 프로파일링 및 최적화 도구를 직접 사용할 수 있습니다.
• 실제 튜닝 시나리오에 초점을 맞춘 안내된 연습.

교육 커스터마이징 옵션

• 성과 환경 또는 모델 유형에 따라 이 교육을 맞춤형으로 요청하려면, 커스터마이징을 조정하기 위해 문의하세요.