Beneath the surface of high-performance AI resides CANN (Compute Architecture for Neural Networks) — the essential software backbone that drives Huawei’s Ascend chips and the intellectual frameworks behind them.
These instructor-led programs dissect the layers of the Compute Architecture for Neural Networks, examining how CANN connects algorithms with silicon through graph optimization, kernel fusion, and hardware-aware scheduling.
Whether your focus is on developing inference engines, refining custom operators, or migrating deep learning models to edge environments, you will acquire practical insights into achieving peak performance on Ascend processors.
Participate in live online sessions via an interactive remote desktop, or attend in-person classes in Prague — whether at your company's offices or a NobleProg training center — complete with labs that replicate production-grade acceleration and deployment pipelines.
Often referred to as Ascend CANN or Huawei CANN, this training empowers developers, engineers, and AI infrastructure teams to extract maximum value from hardware-aware intelligence.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led session in Prague enables intermediate AI engineers to construct and optimize neural network models using the Huawei Ascend platform and CANN toolkit. Attendees will learn to configure environments, develop applications with MindSpore, and execute deployments to edge or cloud settings.
This live, instructor-led training in Prague examines Huawei's AI stack, bridging the gap between the CANN SDK and the MindSpore framework. It empowers beginner and intermediate professionals to understand how these components interact on Ascend hardware to streamline lifecycle management and deployment.
This instructor-led training on Prague focuses on deploying and optimizing CV and NLP models using the CANN SDK for Ascend hardware. Attendees will master model conversion, integration into live pipelines, and strategies to boost inference performance for real-time detection and analysis.
This live, instructor-led training in Prague empowers advanced developers to construct, deploy, and optimize custom AI operators. Through mastering the integration of CANN TIK and Apache TVM, participants will drive advanced optimization and scheduling on Huawei Ascend hardware, ensuring robust real-world performance.
This instructor-led live course in Prague explores the core concepts and practical fundamentals of deploying AI models on Ascend edge devices via the CANN toolkit, enabling participants to develop essential skills for compiling, optimizing, and managing constrained environments.
This live training in Prague provides an introduction to the CANN toolkit for AI framework developers. It covers setting up environments, converting models, and deploying applications on Ascend hardware using MindSpore, TensorFlow, or PyTorch, addressing the complete workflow from training to inference.
Boost neural network inference performance on Ascend AI processors through this advanced, instructor-led training in Prague. Delve into CANN's runtime architecture and utilize the Graph Engine, TIK, and TVM for profiling, custom operator creation, and resolving memory bottlenecks.
This live training in Prague provides a guided pathway for intermediate AI developers looking to deploy models on Ascend processors via the CANN toolkit. It covers converting frameworks such as PyTorch and TensorFlow, along with performance optimization and debugging techniques for efficient edge and cloud inference.
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