Imagine achieving faster execution, smarter training, and seamless deployment of your AI models from edge to cloud. Huawei Ascend makes this vision a reality.
Our instructor-led programmes are designed to unlock the full capabilities of Ascend AI processors, guiding engineers through practical projects involving MindSpore, model compression, and chip-level optimization.
These courses are available through online live training via an interactive remote desktop, or as onsite live training in Varna featuring labs centered on real production workloads.
Onsite sessions can be conducted at your premises in Varna or at a NobleProg corporate training center in Varna, promoting collaborative, team-based learning.
Recognized also as Ascend AI, Huawei Ascend 910, or Ascend Computing, this training enables you to move beyond generic AI hardware and embrace purpose-built performance.
The "Central Point" complex offers quick access to main roads leading to the airport, the northern and southern resorts and the Varna - Sofia and Varna - Burgas highways.
This instructor-led course in Varna assists intermediate AI engineers in constructing and optimizing neural network models via the Huawei Ascend platform and CANN toolkit. Learners will set up environments, build applications using MindSpore, and manage deployment to edge or cloud settings.
This instructor-led live training in Varna explores Huawei's AI stack, from the CANN SDK to the MindSpore framework. It helps beginners and intermediate professionals understand how these components integrate on Ascend hardware for lifecycle management and deployment.
This instructor-led training in Varna covers deploying and optimizing CV and NLP models using the CANN SDK for Ascend hardware. Participants will learn to convert models, integrate them into live pipelines, and enhance inference performance for real-time detection and analysis.
This instructor-led, live training in Varna equips advanced developers with the skills to build, deploy, and tune custom AI operators. Participants will master CANN TIK and Apache TVM integration, enabling advanced optimization and scheduling on Huawei Ascend hardware for real-world performance.
This live training session in Varna provides a comprehensive overview of CloudMatrix for scalable AI inference. Discover how to deploy, optimize, and monitor models using CANN and MindSpore. Practical exercises will guide you through packaging, conversion, serving, and performance tuning for both real-time and batch workloads.
This instructor-led, live training in Varna covers the core concepts and hands-on fundamentals of deploying AI models on Ascend edge devices using the CANN toolkit, helping participants build practical skills for compiling, optimizing, and managing constrained environments.
This live training in Varna introduces the CANN toolkit for AI framework developers. Learn to set up environments, convert models, and deploy applications on Ascend hardware using MindSpore, TensorFlow, or PyTorch, covering the full workflow from training to inference.
Optimize AI workloads on Ascend, Biren, and Cambricon with this hands-on training in Varna. Learn to benchmark models, identify bottlenecks, and apply graph, kernel, and operator-level optimizations. Tune deployment pipelines to enhance throughput and latency across these leading platforms.
Optimize neural network inference performance on Ascend AI processors with this advanced, instructor-led training in Varna. Explore CANN's runtime architecture, leveraging the Graph Engine, TIK, and TVM for profiling, custom operator development, and memory bottleneck resolution.
Migrate CUDA applications to Chinese GPU architectures like Huawei Ascend and Biren in Varna. This instructor-led course guides advanced programmers through code translation and performance optimization, covering hands-on labs for porting CUDA codebases to new SDKs.
This live training in Varna assists intermediate AI developers in deploying models on Ascend processors through the CANN toolkit. You will learn to convert frameworks like PyTorch and TensorFlow, optimize performance, and troubleshoot issues to achieve efficient edge and cloud inference.
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