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

Huawei Ascend constitutes a range of AI processors engineered for superior inference and training capabilities.

This instructor-led live training, available both online and onsite, targets intermediate-level AI engineers and data scientists eager to develop and fine-tune neural network models utilizing 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.
• Create AI applications leveraging MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models across edge or cloud infrastructures.

Course Format

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

Customization Options

• To arrange customized training tailored to your specific infrastructure or datasets for this course, please contact us.

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

This instructor-led live training, available either online or onsite, is tailored for intermediate-level AI developers and engineers aiming to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit alongside popular frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completion of this training, participants will gain the ability to:

• Comprehend the CANN architecture and its pivotal role within the AI deployment pipeline.
• Convert and adapt models from leading frameworks into formats compatible with Ascend.
• Utilize tools such as ATC, OM model conversion utilities, and MindSpore for both edge and cloud inference tasks.
• Identify deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on lab exercises employing CANN tools, Ascend simulators, or physical devices.
• Practical deployment scenarios derived from real-world AI models.

Customization Options

• For those wishing to request a customized version of this course, please reach out to us to make the necessary arrangements.

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, is tailored for beginner-level AI developers who want to grasp 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 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

• 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 powerful AI inference on edge devices like the Ascend 310. It offers vital tools for compiling, optimising, and deploying models in environments with constrained 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 optimise models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Construct lightweight inference pipelines employing MindSpore Lite and AscendCL.
• Optimise model performance for environments with limited compute and memory.
• Deploy and monitor AI applications in real-world edge use cases.

Course Format

• Interactive lectures and demonstrations.
• Practical lab sessions focusing on edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

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

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

This instructor-led training session (available online or onsite) targets advanced AI developers and system engineers aiming to enhance inference performance through CANN’s advanced toolset, which includes the Graph Engine, TIK, and capabilities for custom operator development.

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

• Comprehend CANN's runtime architecture and performance lifecycle.
• Utilise profiling tools and the Graph Engine for performance analysis and optimisation.
• Develop and optimise custom operators using TIK and TVM.
• Address memory bottlenecks and boost model throughput.

Course Format

• Interactive lectures and discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimisation exercises based on edge-case deployment examples.

Course Customisation Options

• To arrange a bespoke training session for this course, please contact us.

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

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

Upon completing this training, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Leveraging CANN utilities to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines tailored for edge or cloud-based deployment environments.

Course Format

• Interactive lectures and demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP scenarios.

Customization Options

• For those requiring customized training for this course, please contact us to arrange.

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

Upon completing this training, participants will be able to:

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom operators into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Course Format

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

Customization Options

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