Understanding Huawei’s AI Compute Stack: From CANN to MindSpore Training Course

Huawei Ascend comprises a suite of AI processors engineered for high-performance inference and training workloads.

This instructor-led live training, available in online or onsite formats, targets intermediate AI engineers and data scientists seeking to develop and optimise neural network models leveraging Huawei’s Ascend platform alongside the CANN toolkit.

Upon completion of this training, participants will be equipped to:

• Establish and configure the CANN development environment.
• Construct AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models across edge or cloud infrastructure.

Course Format

• Interactive lectures coupled with group discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Guided exercises centred on model construction, training, and deployment.

Customisation Options

• For tailored training aligned with your specific infrastructure or datasets, please reach out to us to arrange a bespoke session.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed for deploying and optimizing AI models on Ascend AI processors.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

• Gain insight into the CANN architecture and its significance within the AI deployment pipeline.
• Convert and adapt models from widely-used frameworks into formats compatible with Ascend.
• Utilize tools like ATC, OM model conversion, and MindSpore for inference tasks in both edge and cloud environments.
• Troubleshoot deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures accompanied by live demonstrations.
• Practical lab exercises employing CANN tools along with Ascend simulators or physical devices.
• Real-world deployment scenarios based on actual AI models.

Customization Options

• For a customized version of this training, please contact us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led live training, available either online or onsite, targets beginner-level AI developers keen on understanding CANN's role within the model lifecycle—from training to deployment—and its interaction with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment integrating CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Build foundational knowledge for future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical hands-on labs focused on simple model deployment.
• Step-by-step walkthroughs of the CANN toolchain and integration points.

Course Customization Options

• To arrange customized training for this course, please get in touch with us.

The Huawei Ascend CANN toolkit facilitates robust AI inference on edge devices like the Ascend 310. It offers critical tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led live training, available online or onsite, is designed for intermediate-level AI developers and integrators looking to deploy and optimize models on Ascend edge devices using the CANN toolchain.

Upon completing this training, participants will be equipped to:

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines leveraging MindSpore Lite and AscendCL.
• Enhance model performance in environments with restricted compute and memory.
• Deploy and monitor AI applications in practical, real-world edge scenarios.

Format of the Course

• Interactive lectures and demonstrations.
• Practical lab sessions focusing on 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.

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute framework, empowering developers to fine-tune and maximise the performance of neural networks deployed on Ascend AI processors.

This instructor-led training session, available both online and onsite, is designed for advanced-level AI developers and system engineers who aim to enhance inference performance by leveraging CANN’s sophisticated toolset. Key components include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Gain a comprehensive understanding of CANN’s runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for thorough performance analysis and optimisation.
• Develop and optimise custom operators using TIK and TVM.
• Address memory bottlenecks and significantly improve model throughput.

Course Format

• Engaging interactive lectures and discussions.
• Practical hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises utilising edge-case deployment examples.

Customisation Options

• To arrange a bespoke training session for this course, please reach out to us for further assistance.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.

Upon completion of this training, participants will be equipped to:

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilize CANN utilities to convert models and seamlessly 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 environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises utilizing real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate advanced optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led live training, available both online and onsite, is designed for advanced-level system developers looking to build, deploy, and tune custom operators for AI models by leveraging 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 utilizing the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph.
• Employing TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom 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.

Course Customization Options

• For inquiries regarding customized training for this course, please get in touch with us to arrange it.