Online Huawei Ascend Training in 대한민국

CloudMatrix는 확장 가능한 생산 준비 AI 추론 파이프라인을 지원하기 위해 설계된 화웨이의 통합 AI 개발 및 배포 플랫폼입니다.

이 강사는 화웨이의 CloudMatrix 플랫폼에 CANN 및 MindSpore 통합을 사용하여 AI 모델을 배포하고 모니터링하고자 하는 초급에서 중급 수준의 AI 전문가를 대상으로 하는 온라인 또는 현장 실습형 라이브 교육입니다.

이 교육을 마친 후 참가자는 다음을 할 수 있게 됩니다:

• CloudMatrix를 사용하여 모델 패키징, 배포 및 제공.
• Ascend 칩셋에 맞게 모델 변환 및 최적화.
• 실시간 및 배치 추론 작업에 대한 파이프라인 설정.
• 생산 환경에서 배포 모니터링 및 성능 조정.

수업 형식

• 상호작용형 강의 및 토론.
• 실제 배포 시나리오에서 CloudMatrix 사용.
• 변환, 최적화 및 확장에 중점을 둔 가이드된 연습.

수업 맞춤화 옵션

• AI 인프라 또는 클라우드 환경에 기반한 이 수업에 대한 맞춤형 교육을 요청하려면, 맞춤화된 교육을 조율하기 위해 연락하세요.

Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimize model inference and training workflows across multiple Chinese AI chip platforms.

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

• Benchmark models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Apply graph-level, kernel-level, and operator-level optimizations.
• Tune deployment pipelines to improve throughput and latency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of profiling and optimization tools on each platform.
• Guided exercises focused on practical tuning scenarios.

Course Customization Options

• To request a customized training for this course based on your performance environment or model type, please contact us to arrange.

Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for local AI and HPC markets.

This instructor-led, live training (online or onsite) is aimed at advanced-level GPU programmers and infrastructure specialists who wish to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

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

• Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance and identify optimization points across platforms.
• Address practical challenges in cross-architecture support and deployment.

Format of the Course

• Interactive lecture and discussion.
• Hands-on code translation and performance comparison labs.
• Guided exercises focused on multi-GPU adaptation strategies.

Course Customization Options

• To request a customized training for this course based on your platform or CUDA project, please contact us to arrange.