Building Custom AI Operators with CANN TIK and TVM Training Course

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

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. The course is tailored to align with public sector workflows, governance, and accountability for government.

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 techniques.
• Deploy models to edge or cloud environments, ensuring compliance with government standards.

Format of the Course

• Interactive lecture and discussion sessions.
• Hands-on use of Huawei Ascend and the CANN toolkit in sample applications relevant to government operations.
• Guided exercises focused on model building, training, and deployment within a governmental context.

Course Customization Options

• To request a customized training for this course based on your specific infrastructure or datasets, please contact us to arrange. We can tailor the content to meet the unique needs of government agencies.

The Compute Architecture for Neural Networks (CANN) is Huawei’s AI computing stack designed for deploying and optimizing AI models on Ascend AI processors. This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch. By the end of this training, participants will be able to: - Understand the CANN architecture and its role in the AI deployment pipeline. - Convert and adapt models from popular frameworks to Ascend-compatible formats. - Utilize tools like ATC, OM model conversion, and MindSpore for edge and cloud inference. - Diagnose deployment issues and optimize performance on Ascend hardware. **Format of the Course** - Interactive lecture and demonstration. - Hands-on lab work using CANN tools and Ascend simulators or devices. - Practical deployment scenarios based on real-world AI models. **Course Customization Options for Government** To request a customized training for this course, please contact us to arrange.

The Compute Architecture for Neural Networks (CANN) is Huawei’s AI computing toolkit designed for compiling, optimizing, and deploying AI models on Ascend AI processors.

This instructor-led, live training (available 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 integrates 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 with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects, including those for government applications.

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.

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

This instructor-led, live training (online or onsite) is designed for intermediate-level AI developers and integrators who aim 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 deployment on the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines utilizing MindSpore Lite and AscendCL.
• Enhance model performance in compute- and memory-constrained environments.
• Deploy and monitor AI applications in practical edge scenarios.

Format of the Course

• 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 for Government

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

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated environment optimized for Ascend hardware, designed to support efficient AI development and deployment. This instructor-led, live training (online or on-site) is aimed at technical professionals at beginner to intermediate levels who wish to gain a comprehensive understanding of how the CANN and MindSpore components work together to facilitate AI lifecycle management and infrastructure decisions. By the end of this training, participants will be able to: - Understand the layered architecture of Huawei’s AI compute stack for government. - Identify how CANN supports model optimization and hardware-level deployment in various environments. - Evaluate the MindSpore framework and toolchain in comparison to industry alternatives. - Position Huawei's AI stack within enterprise or cloud/on-premises environments, ensuring alignment with public sector workflows and governance. **Format of the Course** - Interactive lecture and discussion. - Live system demonstrations and case-based walkthroughs. - Optional guided labs on model flow from MindSpore to CANN. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI computing foundation designed to enable developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors. This instructor-led, live training (available online or onsite) is targeted at advanced-level AI developers and system engineers who seek to enhance inference performance using CANN’s comprehensive toolset, which includes the Graph Engine, TIK, and custom operator development. By the end of this training, participants will be able to: - Understand the runtime architecture and performance lifecycle of CANN. - Utilize profiling tools and the Graph Engine for performance analysis and optimization. - Develop and optimize custom operators using TIK and TVM. - Address memory bottlenecks and improve model throughput. **Format of the Course:** - Interactive lecture and discussion. - Hands-on labs with real-time profiling and operator tuning. - Optimization exercises using edge-case deployment examples. **Course Customization Options:** - To request a customized training for government or other specific needs, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) provides robust deployment and optimization tools for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware. This instructor-led, live training (online or onsite) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimize vision and language models using the CANN SDK for production use cases for government. By the end of this training, participants will be able to: - Deploy and optimize computer vision and natural language processing models using CANN and AscendCL. - Utilize CANN tools to convert models and integrate them into live pipelines. - Enhance inference performance for tasks such as detection, classification, and sentiment analysis. - Construct real-time computer vision and natural language processing pipelines suitable for edge or cloud-based deployment scenarios. **Format of the Course** - Interactive lecture and demonstration. - Hands-on laboratory sessions with model deployment and performance profiling. - Live pipeline design using real-world computer vision and natural language processing use cases. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.