At the core of high-performance AI lies CANN (Compute Architecture for Neural Networks) — the software foundation that powers Huawei’s Ascend chips and supports the teams behind them.
These instructor-led courses delve into the intricacies of the Compute Architecture for Neural Networks, examining how CANN connects algorithms and silicon through graph optimization, kernel fusion, and hardware-aware scheduling.
Whether you are developing inference engines, fine-tuning custom operators, or migrating deep learning models to run at the edge, you will acquire practical insights into maximizing performance on Ascend processors.
Participate in live online training via an interactive remote desktop, or attend onsite sessions in Plovdiv — either at your company’s location or a NobleProg training center — which include labs simulating production-grade acceleration and deployment pipelines.
Also 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
Business Center Plovdiv
Han Kubrat St 1, Plovdiv, Bulgaria, 4017
This is the most modern business center in the city, with all the necessary functionalities, while being located in a green part of the city.
It is about 20 minutes by bus from the main train station as well as the city center.
Huawei Ascend comprises a series of AI processors engineered for high-efficiency inference and training tasks.
This instructor-led live training, available either online or at your location, targets intermediate-level AI engineers and data scientists seeking to create and optimize neural network models via Huawei’s Ascend platform and the CANN toolkit.
Upon completion of this training, participants will be capable of:
Establishing and configuring the CANN development environment.
Creating AI applications utilizing MindSpore and CloudMatrix workflows.
Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
Deploying models into cloud or edge computing environments.
Course Format
Engaging lectures combined with interactive discussions.
Practical application of Huawei Ascend and the CANN toolkit within sample projects.
Supervised exercises centered on model construction, training, and deployment.
Customization Opportunities
For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange a session.
Huawei’s AI ecosystem — spanning from the low-level CANN SDK to the high-level MindSpore framework — delivers a cohesive environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.
This instructor-led live training (available online or onsite) is designed for technical professionals ranging from beginner to intermediate skill levels who want to understand how CANN and MindSpore interact to facilitate AI lifecycle management and inform infrastructure choices.
Upon completion of this training, participants will be able to:
Comprehend the layered structure of Huawei’s AI compute architecture.
Recognize how CANN facilitates model optimization and hardware-level implementation.
Assess the MindSpore framework and its toolchain against industry standards.
Place Huawei's AI stack within the context of enterprise or cloud/on-premises environments.
Course Format
Interactive lectures and discussions.
Live system demonstrations and case-based walkthroughs.
Optional guided labs focusing on the model flow from MindSpore to CANN.
Course Customization Options
To arrange customized training for this course, please contact us.
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools designed for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led live training, available online or onsite, targets intermediate-level AI professionals looking to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.
Upon completion of this training, participants will be capable of:
Deploying and optimizing CV and NLP models via CANN and AscendCL.
Utilizing CANN tools to convert models and integrate them into active pipelines.
Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
Developing real-time CV/NLP pipelines suitable for edge or cloud deployment environments.
Course Format
Interactive lectures paired with live demonstrations.
Practical labs focused on model deployment and performance profiling.
Designing live pipelines using real-world CV and NLP use cases.
Customization Options
To arrange customized training for this course, please contact us.
CANN TIK (Tensor Instruction Kernel) and Apache TVM provide powerful tools for the advanced optimization and customization of AI model operators tailored for Huawei Ascend hardware.
This instructor-led, live training session, available online or on-site, is designed for experienced system developers who aim to create, deploy, and refine custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.
Upon completing this training, participants will be capable of:
Writing and testing custom AI operators using the TIK DSL for Ascend processors.
Integrating custom operators into the CANN runtime and execution graph.
Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
Debugging and optimizing instruction-level performance for specific computation patterns.
Course Format
Interactive lectures and demonstrations.
Practical coding exercises involving operators within TIK and TVM pipelines.
Testing and tuning on Ascend hardware or in simulator environments.
Customization Options for the Course
For customized training requests for this course, please reach out to us to arrange details.
Huawei's Ascend CANN toolkit facilitates robust AI inference on edge devices like the Ascend 310. This suite offers vital tools for compiling, optimizing, and deploying models in environments where computational power and memory are limited.
This instructor-led, live training (available online or onsite) targets intermediate-level AI developers and integrators seeking to deploy and optimize models on Ascend edge devices using the CANN toolchain.
Upon completion of this training, participants will be capable of:
Preparing and converting AI models for the Ascend 310 using CANN tools.
Constructing lightweight inference pipelines with MindSpore Lite and AscendCL.
Enhancing model performance within constrained compute and memory settings.
Deploying and monitoring AI applications in real-world edge scenarios.
Course Format
Interactive lectures and demonstrations.
Practical lab exercises focusing on edge-specific models and scenarios.
Live deployment examples on either virtual or physical edge hardware.
Course Customization Options
For customized training options, please contact us to arrange.
CANN (Compute Architecture for Neural Networks) is Huawei's AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.
This instructor-led live training (available online or onsite) targets beginner-level AI developers seeking to understand the role of CANN within the model lifecycle, from training through deployment, and its integration with frameworks such as MindSpore, TensorFlow, and PyTorch.
Upon completion of this training, participants will be able to:
Comprehend the purpose and architectural design of the CANN toolkit.
Configure a development environment utilizing CANN and MindSpore.
Convert and deploy a basic AI model onto Ascend hardware.
Acquire foundational knowledge to support future CANN optimization or integration initiatives.
Course Format
Interactive lectures and discussions.
Practical hands-on labs featuring simple model deployment.
Step-by-step guidance through the CANN toolchain and integration points.
Customization Options
To arrange customized training for this course, please contact us.
The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, empowering developers to refine and optimize the performance of neural networks deployed on Ascend AI processors.
This instructor-led training session, available online or onsite, targets advanced AI developers and system engineers eager to maximize inference performance utilizing CANN’s sophisticated toolkit. Key components include the Graph Engine, TIK, and custom operator development.
Upon completing this training, participants will be equipped to:
Comprehend CANN's runtime architecture and its performance lifecycle.
Leverage profiling tools and the Graph Engine for thorough performance analysis and optimization.
Develop and optimize custom operators utilizing TIK and TVM.
Address memory bottlenecks and enhance model throughput.
Course Format
Engaging lectures paired with interactive discussions.
Practical labs featuring real-time profiling and operator tuning.
Optimization exercises based on edge-case deployment scenarios.
Customization Options
To request a customized version of this course, please contact us to make arrangements.
CANN (Compute Architecture for Neural Networks) represents Huawei's AI compute stack, designed for deploying and optimizing AI models on Ascend AI processors.
This instructor-led live training, available online or on-site, targets intermediate-level AI developers and engineers seeking to efficiently deploy trained AI models to Huawei Ascend hardware. The course focuses on utilizing the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.
Upon completion, participants will be able to:
Grasp the CANN architecture and its function within the AI deployment pipeline.
Convert and adapt models from popular frameworks into Ascend-compatible formats.
Utilize tools like ATC, OM model conversion, and MindSpore for both cloud and edge inference.
Troubleshoot deployment issues and optimize performance on Ascend hardware.
Course Format
Interactive lectures and demonstrations.
Hands-on lab exercises using CANN tools and Ascend simulators or devices.
Practical deployment scenarios based on real-world AI models.
Customization Options
For a customized training version of this course, please contact us to make arrangements.
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