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

Huawei Ascend comprises a series of artificial intelligence processors engineered to deliver high-performance capabilities for both inference and training workloads.

This instructor-led, live training session, available via online or onsite formats, targets intermediate-level AI engineers and data scientists seeking to build and optimize neural network models utilizing the Huawei Ascend platform alongside the CANN toolkit.

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

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

Course Delivery Format

• Engaging lectures and discussions.
• Practical application of Huawei Ascend hardware and the CANN toolkit within sample applications.
• Supervised exercises centered on model construction, training, and deployment processes.

Course Customization Options

• Agencies seeking tailored training aligned with their specific infrastructure or datasets may contact the provider to arrange these services for government use cases.

The Compute Architecture for Neural Networks (CANN) serves as Huawei’s comprehensive AI compute framework designed to facilitate the deployment and optimization of artificial intelligence models on Ascend processors.

This instructor-led session, available via online or onsite delivery, is designed for intermediate-level developers and engineers seeking to efficiently implement trained AI models on Huawei Ascend hardware utilizing the CANN toolkit alongside frameworks such as MindSpore, TensorFlow, or PyTorch. The curriculum emphasizes scalable solutions tailored for government and enterprise environments for government operations.

Upon completion of this training, participants will demonstrate proficiency in:

• Analyzing the CANN architecture and its function within the AI deployment lifecycle.
• Converting and adapting models from widely used frameworks into formats compatible with Ascend processors.
• Utilizing utilities such as ATC, OM model conversion, and MindSpore to support inference tasks across edge and cloud infrastructure.
• Identifying deployment challenges and executing performance optimizations on Ascend hardware platforms.

Course Delivery Structure

• Interactive instruction combined with technical demonstrations.
• Practical laboratory exercises utilizing CANN tools within Ascend simulator or hardware environments.
• Application of deployment strategies based on real-world AI use cases.

Training Adaptation Options

• For agencies requiring a tailored curriculum, please contact our administrative team to coordinate custom arrangements.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors, offering critical resources for government workloads that require scalable intelligence solutions.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like 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 with 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

• 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.

Huawei's Ascend CANN toolkit facilitates advanced artificial intelligence inference on edge hardware, including the Ascend 310 platform. This framework supplies critical capabilities for compiling, optimizing, and deploying models in environments with restricted computational resources and memory capacity, ensuring effective solutions for government systems that require efficient edge processing.

This instructor-led live training program, available online or onsite, targets intermediate-level AI developers and integrators seeking to deploy and optimize machine learning models on Ascend edge devices via the CANN toolchain.

Upon completion of this course, participants will be capable of:

• Preparing and converting artificial intelligence models for the Ascend 310 platform utilizing CANN utilities.
• Developing lightweight inference pipelines through MindSpore Lite and AscendCL integration.
• Enhancing model performance within resource-constrained compute and memory contexts.
• Deploying and monitoring artificial intelligence applications in practical edge scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical laboratory exercises focused on edge-specific models and operational scenarios.
• Real-time deployment examples executed on virtual or physical edge infrastructure.

Course Customization Options

• To request customized training for this course, please contact us to arrange a suitable schedule.

The Compute Architecture for Neural Networks (CANN) SDK serves as Huawei’s foundational platform for AI computing. It enables engineers to refine and enhance the operational efficiency of neural networks deployed on Ascend AI processors.

This instructor-led training program, available through online or onsite delivery methods, is designed for advanced artificial intelligence developers and system engineers seeking to maximize inference capabilities. Participants will utilize CANN’s comprehensive toolset, specifically focusing on the Graph Engine, TIK, and custom operator development frameworks.

Upon completion of this educational module, participants will demonstrate proficiency in:

• Analyzing CANN's runtime architecture and performance lifecycle.
• Leveraging profiling tools and the Graph Engine for detailed performance assessment and optimization.
• Developing and refining custom operators using TIK and TVM technologies.
• Addressing memory constraints and enhancing model throughput efficiency.

Course Delivery Format

• Structured interactive lectures and technical discussions.
• Practical laboratory exercises featuring real-time profiling and operator tuning.
• Targeted optimization workshops utilizing edge-case deployment scenarios.

Customization Options for Government

• To request a customized training curriculum tailored to specific organizational needs, please contact our support team to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time artificial intelligence workloads in computer vision and natural language processing, particularly when utilized with Huawei Ascend hardware.

This instructor-led training program, available via online or onsite delivery, is designed for intermediate-level AI professionals seeking to develop, deploy, and optimize vision and language models using the CANN SDK for government and production-grade applications.

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

• Deploy and optimize computer vision (CV) and natural language processing (NLP) models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and integrate them into operational pipelines.
• Enhance inference performance for critical tasks including detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment architectures.

Course Format

• Interactive lectures coupled with live demonstrations.
• Practical laboratory exercises focused on model deployment and performance profiling.
• Live pipeline development using authentic CV and NLP use cases.

Course Customization Options

• Agencies requiring customized training for government-specific needs should contact us to arrange tailored sessions.

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

This instructor-led, live training (available online or onsite) targets advanced system developers seeking to build, deploy, and tune custom operators for AI models via CANN’s TIK programming model and TVM compiler integration. Designed specifically for government applications, this course supports critical computational needs.

Upon completion of this training, participants will be capable of:

• Writing and validating custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph infrastructure.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking procedures.
• Debugging and optimizing instruction-level performance for specialized computation patterns.

Course Format

• Interactive lectures combined with technical demonstrations.
• Practical coding exercises involving operators through TIK and TVM pipelines.
• Testing and tuning activities performed on Ascend hardware or simulation environments.

Customization Options

• To request tailored training for this curriculum, please contact the program coordinators to make arrangements.