Adobe Illustrator Training Course

Huawei Ascend comprises a range of AI processors engineered for superior performance in both inference and training tasks.

This live, instructor-led training session (available online or at your location) targets intermediate-level data scientists and AI engineers who aim to create and enhance neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.

Upon completing 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 tiling and custom operators.
• Deploy models across cloud or edge environments.

Course Format

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

Customization Options

• For tailored training aligned with your specific datasets or infrastructure, please get in touch with us to make arrangements.

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, targets intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit alongside tools like MindSpore, TensorFlow, or PyTorch.

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

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

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on lab sessions using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

CloudMatrix is Huawei’s unified AI development and deployment platform designed to support scalable, production-grade inference pipelines.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level AI professionals who wish to deploy and monitor AI models using the CloudMatrix platform with CANN and MindSpore integration.

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

• Use CloudMatrix for model packaging, deployment, and serving.
• Convert and optimize models for Ascend chipsets.
• Set up pipelines for real-time and batch inference tasks.
• Monitor deployments and tune performance in production settings.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of CloudMatrix with real deployment scenarios.
• Guided exercises focused on conversion, optimization, and scaling.

Course Customization Options

• To request a customized training for this course based on your AI infrastructure or cloud environment, please contact us to arrange.

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

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced developers who want 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 chips designed to optimize inference and training for both edge devices and data center environments.

This instructor-led live training, available either online or onsite, targets intermediate developers who want to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Set up and configure the development environments for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models tailored for Cambricon MLUs.
• Deploy models to edge and data center devices operating on the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLU.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on development and deployment using BANGPy and Neuware.
• Guided exercises emphasizing optimization, integration, and testing.

Customization Options

• To request a tailored training session based on your specific Cambricon device model or use case, please contact us to arrange.

CANN (Compute Architecture for Neural Networks) is Huawei’s toolkit for AI computing, 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 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.

Upon completing this training, participants will be able to:

• Comprehend the purpose and architecture of the CANN toolkit.
• Establish a development environment featuring CANN and MindSpore.
• Convert and deploy a basic AI model onto Ascend hardware.
• Acquire foundational knowledge to support future CANN optimization or integration initiatives.

Format of the Course

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

Course Customization Options

• To arrange customized training for this course, please contact us to make the necessary 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 computing power and memory are limited.

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI developers and integrators who aim to deploy and optimize 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.
• Construct lightweight inference pipelines with MindSpore Lite and AscendCL.
• Enhance model performance within constrained compute and memory settings.
• Deploy and monitor AI applications in real-world edge scenarios.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on lab exercises featuring 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 get in touch to arrange it.

Huawei’s AI stack—spanning from the low-level CANN SDK to the high-level MindSpore framework—delivers 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 beginner to intermediate technical professionals seeking to grasp how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.

Upon completing this training, participants will be able to:

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN facilitates 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.

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 Customisation Options

• For bespoke training requests, please contact us to arrange accordingly.

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 both online and onsite, targets advanced AI developers and system engineers looking to enhance inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be equipped to:

• Grasp CANN's runtime architecture and its performance lifecycle.
• 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 hands-on labs featuring real-time profiling and operator tuning.
• Optimization exercises centered on edge-case deployment scenarios.

Customization Options

• To request a tailored training program for this course, please reach out to us to make arrangements.

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

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

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN utilities 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 tailored for edge or cloud-based deployment.

Course Format

• Interactive lectures accompanied by demonstrations.
• Hands-on labs featuring model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Customization Options

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

Leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM allows for sophisticated optimization and customization of AI model operators tailored to Huawei Ascend hardware.

This guided, live training session (available online or in-person) is designed for experienced system developers aiming to create, implement, and refine custom operators for AI models utilizing CANN’s TIK programming framework and TVM compiler integration.

Upon completing this training, participants will be equipped to:

• Craft and evaluate custom AI operators employing the TIK DSL for Ascend processors.
• Seamlessly embed custom operators into the CANN runtime and execution graphs.
• Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Troubleshoot and enhance instruction-level performance for unique computational patterns.

Course Structure

• Engaging lectures paired with live demonstrations.
• Practical coding exercises involving operator development using TIK and TVM pipelines.
• Testing and tuning operations on Ascend hardware or simulated environments.

Customization Opportunities

• To arrange a tailored training session for this course, please reach out to us for arrangements.

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and HPC markets.

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

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

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

Course Format

• Interactive lectures and discussions.
• Hands-on labs for code translation and performance comparison.
• Guided exercises focusing on multi-GPU adaptation strategies.

Course Customization Options

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

Ascend, Biren, and Cambricon stand as premier AI hardware platforms in China, providing distinct acceleration and profiling capabilities designed for large-scale AI production workloads.

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

Upon completing this training, participants will be equipped to:

• Conduct benchmarking of models on Ascend, Biren, and Cambricon platforms.
• Pinpoint system bottlenecks and identify inefficiencies in memory and compute usage.
• Implement optimisations at the graph, kernel, and operator levels.
• Refine deployment pipelines to enhance throughput and reduce latency.

Course Format

• Interactive lectures coupled with group discussions.
• Practical application of profiling and optimisation tools on each specific platform.
• Guided exercises centred on real-world tuning scenarios.

Customisation Options

• To arrange customised training tailored to your specific performance environment or model architecture, please reach out to us for arrangement.