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

This instructor-led live training session, available online or on-site, is tailored for intermediate-level AI engineers and data scientists eager to develop and optimize neural network models using 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 utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the use of custom operators and tiling techniques.
• Deploy models across edge or cloud environments.

Course Format

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

Customization Options for the Course

• To arrange a customized training session tailored to your specific infrastructure or datasets, please get in touch with us.

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

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

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures complemented by demonstrations.
• Practical hands-on lab sessions using CANN tools alongside Ascend simulators or physical devices.
• Real-world deployment scenarios based on authentic AI models.

Course Customisation Options

• For a bespoke training experience, please reach out to us to arrange your requirements.

CloudMatrix serves as Huawei's integrated platform for AI development and deployment, engineered to facilitate scalable, production-ready inference pipelines.

This instructor-led training session, available both online and on-site, targets AI professionals ranging from beginner to intermediate levels who aim to deploy and oversee AI models using CloudMatrix, leveraging its integration with CANN and MindSpore.

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

• Utilize CloudMatrix for packaging, deploying, and serving models.
• Convert and optimize models specifically for Ascend hardware.
• Configure pipelines to handle both real-time and batch inference workloads.
• Oversee deployments and fine-tune performance within production environments.

Course Format

• Engaging lectures paired with interactive discussions.
• Practical application of CloudMatrix through real-world deployment scenarios.
• Guided exercises centred on model conversion, optimization, and scaling.

Customization Options

• For tailored training aligned with your specific AI infrastructure or cloud environment, please reach out to us to arrange a session.

Biren AI Accelerators are high-performance GPUs engineered for AI and HPC workloads, supporting large-scale training and inference.

This instructor-led live training, available online or onsite, targets intermediate to advanced developers who want to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.

Upon completing this training, participants will be able to:

• Understand Biren GPU architecture and memory hierarchy.
• Set up the development environment and use Biren’s programming model.
• Translate and optimize CUDA-style code for Biren platforms.
• Apply performance tuning and debugging techniques.

Course Format

• Interactive lecture and discussion.
• Hands-on use of Biren SDK in sample GPU workloads.
• Guided exercises focused on porting and performance tuning.

Customization Options

• To request customized training for this course based on your application stack or integration needs, please contact us to arrange.

Cambricon MLUs (Machine Learning Units) are specialized artificial intelligence chips designed to optimize inference and training tasks for both edge computing and data center environments.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers aiming to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Setting up and configuring development environments for BANGPy and Neuware.
• Creating and optimizing Python and C++ based models for Cambricon MLUs.
• Deploying models to edge and data center devices operating on the Neuware runtime.
• Integrating machine learning workflows with acceleration features specific to MLU.

Course Format

• Interactive lectures and discussions.
• Practical application of BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Customization Options

• For a customized training session tailored to your specific Cambricon device model or use case, please contact us to make arrangements.

CANN (Compute Architecture for Neural Networks) is Huawei’s toolkit designed for AI computing, enabling developers to compile, optimise, and deploy AI models on Ascend AI processors.

This instructor-led training session, available either online or at your office, targets beginner-level AI developers who want to grasp how CANN integrates into the model lifecycle—from training to deployment—and how it collaborates with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be equipped to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment utilising CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimisation or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and its integration points.

Customisation Options

• For those seeking a tailored version of this course, please reach out to us to make arrangements.

Huawei's Ascend CANN toolkit empowers robust AI inference on edge devices like the Ascend 310. It offers crucial tools for compiling, optimizing, and deploying models in environments where computational power and memory are limited.

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

Upon completion, participants will be able to:

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Create lightweight inference pipelines using MindSpore Lite and AscendCL.
• Enhance model performance in resource-constrained settings.
• Deploy and monitor AI applications in practical edge scenarios.

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.

Customization Options

• For tailored training requests, please contact us to arrange.

Huawei’s AI stack — ranging from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated environment for AI development and deployment, specifically optimised for Ascend hardware.

This instructor-led, live training (available online or onsite) is designed for technical professionals at beginner to intermediate levels who wish to understand how CANN and MindSpore components collaborate to support 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.
• Identify how CANN supports model optimisation and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-premises environments.

Format of the Course

• Interactive lecture and discussion.
• Live system demos 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) serves as Huawei’s foundational AI compute platform, empowering developers to fine-tune and enhance the performance of deployed neural networks on Ascend AI processors.

This instructor-led live training, available either online or onsite, is designed for advanced AI developers and system engineers who aim to maximise inference performance by leveraging CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

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 effective 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.
• Hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises incorporating edge-case deployment examples.

Customisation Options

• For tailored training arrangements for this course, please contact us directly.

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

This instructor-led live training, available online or onsite, targets intermediate-level AI practitioners looking to develop, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completion 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 seamlessly integrate them into live pipelines.
• Enhance inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud deployment scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on lab sessions focused on model deployment and performance profiling.
• Live pipeline design exercises using practical CV and NLP use cases.

Course Customization Options

• For tailored training requirements, please contact us to arrange a customized session.

The combination of CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitates advanced optimization and customization of AI model operators specifically for Huawei Ascend hardware.

This instructor-led live training, available either online or onsite, is designed for advanced system developers who aim to construct, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model alongside TVM compiler integration.

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

• Compose and test custom AI operators by employing the TIK DSL for Ascend processors.
• Embed custom operators into the CANN runtime and execution graph.
• Leverage TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and enhance instruction-level performance for bespoke computation patterns.

Course Format

• Engaging lectures complemented by practical demonstrations.
• Practical coding exercises involving operators through TIK and TVM pipelines.
• Testing and tuning procedures performed on Ascend hardware or within simulators.

Customization Options for the Course

• For personalized training requirements, please reach out to us to make arrangements.

Local GPU solutions such as Huawei Ascend, Biren, and Cambricon MLUs provide viable alternatives to CUDA, specifically designed to serve domestic AI and High-Performance Computing (HPC) markets.

This instructor-led, live training (available online or onsite) targets advanced GPU developers and infrastructure specialists seeking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

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

• Evaluate how well existing CUDA workloads align with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Analyze performance metrics and pinpoint optimization opportunities across different platforms.
• Resolve practical challenges related to cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on code translation and performance comparisons.
• Guided exercises emphasizing multi-GPU adaptation strategies.

Customization Options

• To arrange customized training tailored to your specific platform or CUDA project, please contact us.

Ascend, Biren, and Cambricon represent the forefront of AI hardware platforms in China, each providing distinct acceleration and profiling tools designed for production-scale AI workloads.

This instructor-led live training, available both online and onsite, is designed for advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across various Chinese AI chip platforms.

Upon completing this training, participants will be equipped to:

• Conduct model benchmarking on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Implement graph-level, kernel-level, and operator-level optimizations.
• Tune deployment pipelines to enhance throughput and reduce latency.

Course Format

• Interactive lectures and discussions.
• Hands-on practice using profiling and optimization tools on each platform.
• Guided exercises centered on practical tuning scenarios.

Course Customization Options

• To arrange customized training tailored to your specific performance environment or model type, please contact us.