Beneath the surface of high-performance AI lies CANN (Compute Architecture for Neural Networks) — the essential software foundation that drives Huawei’s Ascend chips and the intelligent systems they power.
Through these instructor-led courses, you will delve into the core of the Compute Architecture for Neural Networks, examining how CANN connects algorithms with silicon via graph optimization, kernel fusion, and hardware-aware scheduling.
Whether you are developing inference engines, optimizing custom operators, or migrating deep learning models for edge computing, you will acquire practical insights into achieving peak performance on Ascend processors.
Participate in live online training through an interactive remote desktop, or attend onsite sessions in Gaia — either at your company’s location or a NobleProg training center — which include labs designed to simulate production-grade acceleration and deployment workflows.
Often referred to as Ascend CANN or Huawei CANN, this training empowers developers, engineers, and AI infrastructure teams to fully leverage hardware-aware intelligence.
NobleProg – Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This instructor-led programme on Gaia guides intermediate AI engineers in constructing and optimising neural network models using the Huawei Ascend platform and CANN toolkit. Learners will configure their environments, develop applications with MindSpore, and execute deployments to edge or cloud settings.
This live, instructor-led training on Gaia delves into Huawei's AI stack, covering the spectrum from the CANN SDK to the MindSpore framework. It supports both beginners and intermediate professionals in understanding how these elements integrate on Ascend hardware for lifecycle management and deployment.
This instructor-led session in Gaia focuses on deploying and optimizing CV and NLP models via the CANN SDK on Ascend hardware. Participants will gain the skills to convert models, integrate them into live pipelines, and boost inference performance for real-time detection and analysis.
This live, instructor-led training in Gaia provides advanced developers with the essential skills to construct, deploy, and optimize custom AI operators. Participants will gain mastery over the integration of CANN TIK and Apache TVM, enabling sophisticated optimization and scheduling on Huawei Ascend hardware to achieve real-world performance gains.
This instructor-led live course in Gaia explores the fundamental principles and practical skills required to deploy AI models on Ascend edge devices using the CANN toolkit, enabling participants to master compilation, optimization, and management within constrained environments.
This live training in Gaia introduces the CANN toolkit for AI framework developers. Participants will learn to configure environments, convert models, and deploy applications on Ascend hardware using MindSpore, TensorFlow, or PyTorch, covering the complete workflow from training to inference.
Elevate neural network inference performance on Ascend AI processors through this advanced, instructor-led training in Gaia. Delve into the CANN runtime architecture, utilising the Graph Engine, TIK, and TVM for profiling, custom operator creation, and resolving memory bottlenecks.
This live training in Gaia provides intermediate AI developers with the skills to deploy models on Ascend processors using the CANN toolkit. The course covers converting frameworks such as PyTorch and TensorFlow, optimising performance, and troubleshooting issues to ensure efficient edge and cloud inference.
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