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 live, instructor-led training in Varna delves into Huawei's AI stack, covering the spectrum from the CANN SDK to the MindSpore framework. It is designed to assist beginner and intermediate professionals in understanding how these components synergize on Ascend hardware to streamline lifecycle management and deployment.
This instructor-led training on Varna focuses on deploying and optimizing CV and NLP models using the CANN SDK for Ascend hardware. Learners will master model conversion, integration into live pipelines, and enhancing inference performance for real-time detection and analysis.
This instructor-led live training in Varna empowers advanced developers to build, deploy, and optimize custom AI operators. Learners will master the integration of CANN TIK and Apache TVM, enabling sophisticated optimization and scheduling on Huawei Ascend hardware for superior 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 live, instructor-led training in Varna delves into the core principles 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 resource-constrained environments.
This live training in Varna introduces the CANN toolkit to 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.
Enhance AI workload performance on Ascend, Biren, and Cambricon through this practical training in Varna. Master model benchmarking, pinpoint bottlenecks, and deploy graph, kernel, and operator-level optimizations. Refine deployment pipelines to optimize throughput and latency across these top-tier platforms.
Enhance neural network inference performance on Ascend AI processors through this advanced, instructor-led training in Varna. Dive into CANN's runtime architecture, leveraging the Graph Engine, TIK, and TVM to master profiling, develop custom operators, and resolve critical memory bottlenecks.
Transition CUDA applications to Chinese GPU ecosystems, such as Huawei Ascend and Biren, within Varna. This instructor-led course assists advanced programmers in code translation and performance refinement, featuring practical labs for porting CUDA codebases to emerging 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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