Graphic techniques (Adobe Photoshop, Adobe Illustrator) Training Course

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

This instructor-led live training, available online or on-site, targets intermediate-level AI engineers and data scientists aiming to create and refine neural network models using Huawei’s Ascend platform alongside the CANN toolkit.

Upon completing this course, participants will be capable of:

• Configuring and establishing the CANN development environment.
• Creating AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through tiling and custom operators.
• Deploying models across cloud or edge environments.

Course Format

• Interactive discussions and lectures.
• Practical application of Huawei Ascend and CANN toolkit within sample projects.
• Guided exercises centered on model building, training, and deployment.

Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange a session.

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

This instructor-led, live training session (available online or onsite) is designed for intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit. The curriculum covers integration with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

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

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

Course Format

• Interactive lectures and live demonstrations.
• Hands-on laboratory exercises utilizing CANN tools, Ascend simulators, or physical devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

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

This instructor-led training session, available both online and onsite, is designed for AI professionals ranging from beginner to intermediate levels who aim to deploy and manage AI models by leveraging the CloudMatrix platform integrated with CANN and MindSpore.

Upon completing this training, participants will be equipped to:

• Utilize CloudMatrix for model packaging, deployment, and serving.
• Convert and optimize models specifically for Ascend chipsets.
• Establish pipelines for both real-time and batch inference operations.
• Monitor deployments and fine-tune performance within production environments.

Course Format

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

Customization Options

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

Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.

By the end of 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 engineered for optimized inference and training across both edge computing and datacenter environments.

This instructor-led live training, available online or onsite, is designed for intermediate developers seeking 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 able to:

• Install and configure the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge and datacenter devices operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Hands-on practice with BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

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 toolkit for AI computing, designed to compile, optimize, and deploy AI models on Ascend processors.

This guided, live training (available online or in-person) targets beginner AI developers looking to understand how CANN integrates into the model lifecycle, from training through deployment, and its interaction with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment using CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge for future CANN optimization and integration projects.

Course Format

• Interactive lectures and discussions.
• Practical labs involving simple model deployment.
• Step-by-step walkthroughs of the CANN toolchain and integration points.

Customization Options

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

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

This instructor-led, live training (available online or onsite) targets intermediate AI developers and integrators who aim to deploy and optimize 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 utilizing MindSpore Lite and AscendCL.
• Optimize model performance for environments with constrained compute and memory.
• Deploy and monitor AI applications in real-world edge scenarios.

Format of the Course

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

Course Customization Options

• To request 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 — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together 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 optimization 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-prem 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 computing framework, empowering developers to fine-tune and maximize the performance of neural networks deployed on Ascend AI processors.

This instructor-led training, available in online or onsite formats, is designed for advanced AI developers and system engineers seeking to boost inference performance through CANN's sophisticated toolset. Key components covered include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Grasp CANN's runtime architecture and performance lifecycle.
• Leverage 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 enhance model throughput.

Training Format

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

Customization Options

• For personalized training arrangements, please contact us directly.

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

This instructor-led live training, available online or onsite, targets intermediate AI professionals seeking to build, 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 utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Hands-on labs focused on model deployment and performance profiling.
• Practical pipeline design exercises using real-world CV and NLP use cases.

Customization Options

• For customized training arrangements, please reach out to us.

By leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM, advanced optimization and customization of AI model operators tailored for Huawei Ascend hardware become achievable.

This instructor-led, live training session, available either online or on-site, is specifically designed for advanced-level system developers aiming to develop, deploy, and fine-tune custom operators for AI models by utilizing CANN’s TIK programming model and integrating with the TVM compiler.

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

• Write and evaluate custom AI operators using the TIK DSL for Ascend processors.
• Seamlessly integrate custom operators into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Debug and optimize instruction-level performance to address specific custom computation patterns.

Course Format

• Engaging lectures combined with live demonstrations.
• Practical coding exercises focused on implementing operators via TIK and TVM pipelines.
• Testing and tuning procedures conducted on Ascend hardware or simulators.

Customization Options

• To arrange a customized version of this course, please reach out to us for further details.

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for local AI and high-performance computing (HPC) markets.

This instructor-led live training, available online or on-site, targets advanced GPU programmers 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 equipped to:

• Assess the compatibility of existing CUDA workloads with Chinese chip alternatives.
• Translate CUDA codebases into Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Analyze performance metrics and identify optimization opportunities across different platforms.
• Tackle practical challenges related to cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Practical labs involving code translation and performance comparisons.
• Guided exercises focusing on 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 stand out as premier AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale AI workloads.

Designed for advanced AI infrastructure and performance engineers, this instructor-led live training (available online or onsite) focuses on optimizing model inference and training workflows across multiple Chinese AI chip ecosystems.

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

• Conduct benchmarking on Ascend, Biren, and Cambricon platforms.
• Pinpoint system bottlenecks and identify inefficiencies in 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.

Customization Options

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