Under the hood of high-performance AI lies CANN (Compute Architecture for Neural Networks) — the software foundation powering Huawei’s Ascend chips and the minds behind them.
These instructor-led courses peel back the layers of the Compute Architecture for Neural Networks, exploring how CANN bridges algorithms and silicon through graph optimization, kernel fusion, and hardware-aware scheduling.
Whether you’re building inference engines, tuning custom operators, or porting deep learning models to run at the edge, you’ll gain practical insight into maximizing performance on Ascend processors.
Train live online via an interactive remote desktop, or join onsite sessions in Zakopane — either at your organization’s premises or a NobleProg training center — featuring labs that simulate production-grade acceleration and deployment pipelines.
Also known as Ascend CANN or Huawei CANN, this training equips developers, engineers, and AI infrastructure teams to get the most from hardware-aware intelligence.
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Zakopane
Dafne, Jagiellońska 30, Zakopane, Polska, 34-500
Sala szkoleniowa jest zlokalizowana zaledwie 350 metrów od dworców PKP i PKS oraz 700 metrów od Krupówek, głównej ulicy handlowej w Zakopanem. Na terenie obiektu znajduje się ogrodzony parking, co zapewnia wygodę dla uczestników szkoleń podróżujących własnym samochodem oraz ułatwia dostęp do obiektu.
Huawei Ascend jest rodziną procesorów AI zaprojektowanych do wysokowydajnej inferencji i szkolenia.
To prowadzone przez instruktora, żywe szkolenie (online lub na miejscu) jest skierowane do inżynierów AI i naukowców danych na poziomie średnim, którzy chcą tworzyć i optymalizować modele sieci neuronowych za pomocą platformy Ascend Huawei i zestawu narzędzi CANN.
Na zakończenie tego szkolenia uczestnicy będą w stanie:
Konfigurować i ustawiać środowisko CANN development.
Tworzyć aplikacje AI za pomocą MindSpore i CloudMatrix workflows.
Optymalizować wydajność na Ascend NPUs za pomocą operatorów niestandardowych i podziału.
Wdrażać modele w środowiskach krawędziowych lub chmurowych.
Format kursu
Interaktywne wykłady i dyskusje.
Ręczne używanie Huawei Ascend i zestawu narzędzi CANN w przykładowych aplikacjach.
Wykonywanie zadań pod kierunkiem, skupionych na budowaniu, szkoleniu i wdrażaniu modeli.
Opcje dostosowywania kursu
Aby zapytać o dostosowane szkolenie dla tego kursu zgodnie z Twoją infrastrukturą lub zbiory danych, skontaktuj się z nami w celu zorganizowania.
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.
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.
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.
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.
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.
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.
