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

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

This instructor-led live training, available online or on-site, targets intermediate-level AI engineers and data scientists keen on developing and optimizing neural network models via Huawei's Ascend platform and the CANN toolkit.

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

• Setting up and configuring the CANN development environment.
• Creating AI applications employing MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through custom operators and tiling techniques.
• Deploying models to either edge or cloud environments.

Course Format

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

Course Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to make arrangements.

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 live training session, led by an instructor and available either online or onsite, targets intermediate-level AI developers and engineers. It focuses on efficiently deploying trained AI models onto Huawei Ascend hardware by leveraging the CANN toolkit alongside tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

• Grasp the CANN architecture and its critical role within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into formats compatible with Ascend.
• Utilise tools like ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
• Troubleshoot deployment challenges and optimise performance on Ascend hardware.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory exercises using CANN tools and Ascend simulators or physical devices.
• Practical deployment scenarios grounded in real-world AI models.

Course Customisation Options

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

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit designed to compile, optimise, 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 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 optimisation 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 Customisation Options

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

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

This instructor-led, live training (available online or onsite) targets intermediate-level AI developers and integrators who wish to deploy and optimise models on Ascend edge devices using the CANN toolchain.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and demonstrations.
• Hands-on laboratory work involving edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Customisation Options

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

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

This instructor-led live training session, available online or onsite, is designed for advanced AI developers and system engineers who aim to optimise inference performance through CANN’s sophisticated toolkit, including the Graph Engine, TIK, and custom operator development.

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

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

Course Format

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

Course Customisation Options

• To request a tailored version of this course, please contact us to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools 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 practitioners who want to build, deploy, and optimize vision and language models for production environments using the CANN SDK.

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

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

Course Format

• Interactive lectures and demonstrations.
• Hands-on labs involving model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course 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 the advanced optimisation and customisation of AI model operators for Huawei Ascend hardware.

This instructor-led, live training session (available online or onsite) is designed for advanced-level system developers who wish to construct, deploy, and fine-tune custom operators for AI models using 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 using 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 utilizing TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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