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

This instructor-led, live training (available online or onsite) targets intermediate-level AI engineers and data scientists who aim to develop and optimize neural network models utilizing 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 using 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 in sample applications.
• Guided exercises centered on model construction, training, and deployment.

Customization Options

• To request a customized version of this course tailored to your specific infrastructure or datasets, please contact us to make arrangements.

CANN (Compute Architecture for Neural Networks) serves as Huawei's dedicated AI compute stack, designed for deploying and optimizing artificial intelligence models on Ascend AI processors.

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

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

• Grasp the CANN architecture and its pivotal role within the AI deployment pipeline.
• Convert and adapt models from widely used frameworks into Ascend-compatible formats.
• Utilize tools such as ATC, OM model conversion, and MindSpore to facilitate edge and cloud inference.
• Identify deployment challenges and optimize performance metrics on Ascend hardware.

Format of the Course

• Interactive lectures combined with live demonstrations.
• Practical lab exercises using CANN tools, along with Ascend simulators or physical devices.
• Real-world deployment scenarios grounded in practical AI models.

Course Customization Options

• For tailored training arrangements, please contact us to discuss your specific needs.

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

This instructor-led live training, available both online and onsite, targets beginner to intermediate-level AI professionals aiming to deploy and monitor AI models using CloudMatrix with integrated CANN and MindSpore capabilities.

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

• Leverage CloudMatrix for packaging, deploying, and serving models.
• Convert and optimize models specifically for Ascend chipsets.
• Establish pipelines tailored for both real-time and batch inference tasks.
• Monitor deployments and fine-tune performance within production environments.

Course Format

• Interactive lectures combined with group discussions.
• Practical, hands-on experience using CloudMatrix in real-world deployment scenarios.
• Guided exercises concentrating on model conversion, optimization, and scalability.

Course Customization Options

• For customized training tailored to your specific AI infrastructure or cloud environment, please contact us to make arrangements.

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 looking 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.

Format of the Course

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

Course Customization Options

• To request a 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 AI accelerators designed to optimize both inference and training tasks in edge computing and data center environments.

This instructor-led, live training session (available online or on-site) targets intermediate-level developers who aim to construct and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Establish and configure development environments for BANGPy and Neuware.
• Construct and optimize Python and C++ based models for Cambricon MLUs.
• Deploy models to edge and data center devices operating with the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

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

Course Customization Options

• To arrange customized training tailored to your specific Cambricon device model or use case, please contact us.

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 facilitates high-performance AI inference on edge devices, such as the Ascend 310. It provides critical tools for compiling, optimizing, and deploying models in environments with limited compute power and memory.

This instructor-led live training, available online or onsite, is designed for intermediate-level AI developers and integrators looking to deploy and optimize models on Ascend edge devices using the CANN toolchain.

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

• Preparing and converting AI models for the Ascend 310 using CANN tools.
• Constructing lightweight inference pipelines with MindSpore Lite and AscendCL.
• Enhancing model performance in resource-constrained settings.
• Deploying and monitoring AI applications in real-world edge scenarios.

Course Format

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

Customization Options

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

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

This instructor-led, live training (available online or on-site) targets beginner to intermediate technical professionals seeking to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decision-making.

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

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and its toolchain in the context of industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-premise environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs focusing on the model flow from MindSpore to CANN.

Course Customization Options

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

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

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

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

• Comprehend the runtime architecture and performance lifecycle of CANN.
• Utilize profiling tools and the Graph Engine for detailed performance analysis and optimization.
• Develop and optimize 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.
• Optimization exercises based on edge-case deployment scenarios.

Customization Options

• To arrange tailored training for this course, please get in touch with us.

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, particularly when leveraging Huawei Ascend hardware.

This instructor-led, live training session (available online or onsite) targets intermediate-level AI professionals aiming to build, deploy, and optimize vision and language models using the CANN SDK for practical production scenarios.

Upon completing this course, participants will be capable of:

• Deploying and optimizing CV and NLP models using CANN and AscendCL.
• Utilizing CANN tools to convert models and seamlessly integrate them into operational pipelines.
• Enhancing inference performance for applications such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on laboratory exercises focusing on model deployment and performance profiling.
• Real-time pipeline design utilizing actual CV and NLP use cases.

Customization Options

• For customized training arrangements for this course, please contact us to discuss your needs.

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

This instructor-led, live training (available online or onsite) targets advanced-level system developers aiming to create, deploy, and tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

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

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

Course Format

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

Course Customization Options

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

Domestic GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA-compatible alternatives specifically designed for the local AI and HPC sectors.

This instructor-led live training, available online or onsite, is intended for advanced GPU developers and infrastructure experts seeking to migrate and optimize their existing CUDA applications for deployment on Chinese hardware.

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

• Assess the compatibility of current CUDA workloads with domestic chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance metrics and identify key optimization opportunities across different platforms.
• Resolve practical challenges related to cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on code translation and performance benchmarking.
• Guided exercises targeting multi-GPU adaptation strategies.

Customization Options

• For tailored training aligned with your specific platform or CUDA project, please contact us to arrange a session.

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

This instructor-led live training (available online or onsite) targets advanced AI infrastructure and performance engineers seeking to optimize both model inference and training workflows across these diverse Chinese AI chip ecosystems.

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

• Conduct benchmarks for models across Ascend, Biren, and Cambricon platforms.
• Pinpoint system bottlenecks and inefficiencies related to memory and compute resources.
• Implement optimizations at the graph, kernel, and operator levels.
• Refine deployment pipelines to enhance both throughput and latency.

Course Format

• Engaging interactive lectures and discussions.
• Practical application of profiling and optimization tools on each respective platform.
• Guided exercises centered on real-world tuning scenarios.

Course Customization Options

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