At the core of high-performance artificial intelligence is CANN (Compute Architecture for Neural Networks), the essential software layer that drives Huawei’s Ascend chips and the intelligence they enable.
Through these instructor-led programs, participants gain a deep understanding 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, refining custom operators, or migrating deep learning models to edge environments, these courses provide the practical expertise needed to maximize performance on Ascend processors.
Attend live online sessions using an interactive remote desktop, or participate in onsite workshops in Lyon, available either at your company's site or a NobleProg training center. These sessions include labs designed to simulate production-level acceleration and deployment workflows.
Often referred to as Ascend CANN or Huawei CANN, this training empowers developers, engineers, and AI infrastructure teams to fully harness the capabilities of hardware-aware intelligence.
NobleProg – Your Local Training Provider
Lyon, Swisslife Tower
NobleProg Lyon, 10 Place Charles Béraudier, Lyon, france, 69000
Located 200 meters far from the train station TGV, Swisslife Tower is today the most representative building of this quarter of Lyon. The Business Center offers you a perfect location for your training.
Gares TGV
100meters from Gare TGV Part-Dieu , porte du Rhône Exit
Aéroport
30 minutes from Lyon Saint Exupéry (Satolas)
Rhône Express from Saint Exupéry airport (Terminus Gare part-Dieu)
This instructor-led training in Lyon supports intermediate AI engineers in constructing and optimizing neural network models using the Huawei Ascend platform and CANN toolkit. Attendees will set up environments, build applications with MindSpore, and deploy to edge or cloud configurations.
Huawei’s AI stack, ranging from the low-level CANN SDK to the high-level MindSpore framework, provides a tightly integrated environment for AI development and deployment, optimized specifically for Ascend hardware.
This instructor-led live training, available either online or on-site, targets beginner to intermediate technical professionals seeking to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.
By the end of this training, participants will be able to:
Comprehend the layered architecture of Huawei’s AI compute stack.
Identify how CANN facilitates model optimization and hardware-level deployment.
Evaluate the MindSpore framework and toolchain in comparison to industry alternatives.
Position Huawei's AI stack within enterprise or cloud/on-premises environments.
Format of the Course also allows for the evaluation of participants.
Interactive lectures and discussions.
Live system demonstrations and case-based walkthroughs.
Optional guided labs covering the model flow from MindSpore to CANN.
Course Customization Options
To request customized training for this course, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led training, available both online and onsite, targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.
Upon completion of this training, participants will be able to:
Deploy and optimize CV and NLP models using CANN and AscendCL.
Leverage CANN tools to convert models and integrate them into active pipelines.
Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.
Course Format
Interactive lectures and live demonstrations.
Practical labs focused on model deployment and performance profiling.
Live pipeline design utilizing real-world CV and NLP use cases.
Customization Options
For a customized version of this course, please reach out to us to arrange your training.
CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate the advanced optimization and customization of AI model operators for Huawei Ascend hardware.
This instructor-led, live training session (available online or onsite) is designed for advanced system developers who aim to create, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.
Upon completion of this training, participants will be capable of:
Writing and testing custom AI operators using the TIK DSL for Ascend processors.
Integrating custom operations into the CANN runtime and execution graph.
Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
Debugging and optimizing instruction-level performance for specific computational patterns.
Course Format
Interactive lectures and demonstrations.
Practical coding exercises for operators using TIK and TVM pipelines.
Testing and tuning on Ascend hardware or simulators.
Customization Options for the Course
To request a customized training version of this course, please contact us to make arrangements.
Huawei's Ascend CANN toolkit empowers powerful AI inference on edge devices like the Ascend 310. CANN offers the necessary tools for compiling, optimizing, and deploying models in environments where computing power and memory are limited.
This instructor-led, live training (available online or onsite) is designed for intermediate AI developers and integrators who want to deploy and optimize models on Ascend edge devices using the CANN toolchain.
By the end of this training, participants will be able to:
Prepare and convert AI models for the Ascend 310 using CANN tools.
Build lightweight inference pipelines using MindSpore Lite and AscendCL.
Optimize model performance for environments with limited compute and memory.
Deploy and monitor AI applications in real-world edge scenarios.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and demonstration.
Hands-on lab work with edge-specific models and scenarios.
Live deployment examples on virtual or physical edge hardware.
Course Customization Options
To request a customized training for this course, 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 who want to understand how CANN integrates into the model lifecycle, from training through to deployment, and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.
By the end of this training, participants will be able to:
Understand the purpose and architecture of the CANN toolkit.
Set up a development environment using CANN and MindSpore.
Convert and deploy a simple AI model to Ascend hardware.
Gain foundational knowledge for future CANN optimization or integration projects.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Hands-on labs with simple model deployment.
Step-by-step walkthrough of the CANN toolchain and integration points.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, enabling developers to fine-tune and maximize the performance of neural networks deployed on Ascend AI processors.
This instructor-led live training, available either online or onsite, is designed for advanced AI developers and system engineers seeking to optimize inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.
Upon completion of this training, participants will be equipped to:
Grasp CANN's runtime architecture and its performance lifecycle.
Utilize profiling tools and the Graph Engine to analyze and optimize performance.
Develop and optimize custom operators using TIK and TVM.
Address memory bottlenecks and enhance model throughput.
Course Format
Interactive lectures coupled with discussions.
Practical labs featuring real-time profiling and operator tuning.
Optimization exercises based on edge-case deployment scenarios.
Customization Options
For personalized training arrangements for this course, please get in touch with us.
This live training in Lyon guides intermediate AI developers through deploying models on Ascend processors using the CANN toolkit. Learn to convert frameworks like PyTorch and TensorFlow, optimize performance, and debug issues for efficient edge and cloud inference scenarios.
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