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

Huawei Ascend is a family of AI processors designed for high-performance inference and training.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimize neural network models using Huawei’s Ascend platform and the CANN toolkit.

By the end of this training, participants will be able to:

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimize performance on Ascend NPUs using custom operators and tiling.
• Deploy models to edge or cloud environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
• Guided exercises focused on model building, training, and deployment.

Course Customization Options

• To request a customized training for this course based on your infrastructure or datasets, please contact us to arrange.

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

This instructor-led live training, available both online and on-site, targets intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be capable of:

• Understanding the CANN architecture and its function within the AI deployment pipeline.
• Converting and adapting models from popular frameworks into Ascend-compatible formats.
• Utilizing tools like ATC, OM model conversion, and MindSpore for inference on both edge and cloud environments.
• Diagnosing deployment issues and optimizing 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.

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 used to compile, optimize, and deploy AI models on Ascend AI processors.

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 empowers developers to execute high-performance AI inference on edge hardware, including the Ascend 310. This toolkit offers critical capabilities for compiling, optimizing, and deploying models in environments where computational power and memory are limited.

This instructor-led live training, available either online or on-site, is designed for intermediate AI developers and integrators seeking to deploy and optimize models on Ascend edge devices using the CANN ecosystem.

Upon completion of this course, participants will be able to:

• Prepare and convert AI models for the Ascend 310 using CANN utilities.
• Construct efficient inference pipelines utilizing MindSpore Lite and AscendCL.
• Enhance model performance tailored for constrained compute and memory resources.
• Deploy and oversee AI applications in practical edge scenarios.

Course Format

• Engaging lectures combined with live demonstrations.
• Practical laboratory sessions focusing on edge-specific models and use cases.
• Real-world deployment examples executed on virtual or physical edge hardware.

Customization Options

• To request a tailored training session for this course, please reach out to us for arrangements.

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

This instructor-led live training, available online or onsite, targets advanced AI developers and system engineers seeking to maximize inference performance by leveraging CANN’s advanced tools, such as the Graph Engine, TIK, and custom operator development capabilities.

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

• Gaining insight into CANN's runtime architecture and performance lifecycle.
• Utilizing profiling tools and the Graph Engine to analyze and optimize performance.
• Developing and optimizing custom operators using TIK and TVM.
• Addressing memory bottlenecks and increasing model throughput.

Course Format

• Interactive lectures and discussions.
• Practical labs involving real-time profiling and operator tuning.
• Optimization exercises based on real-world edge-case deployment scenarios.

Customization Options

• For customized training arrangements, please contact us directly.

The CANN SDK (Compute Architecture for Neural Networks) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.

By the end of this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks like detection, classification, and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab with model deployment and performance profiling.
• Live pipeline design using real CV and NLP use cases.

Course Customization Options

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

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 (available online or onsite) is designed for advanced-level system developers who want to build, deploy, and fine-tune custom operators for AI models using CANN’s TIK programming model and its integration with the TVM compiler.

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.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computation patterns.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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