Computer Graphics Training in Maia

Huawei Ascend constitutes a range of AI processors engineered for superior inference and training capabilities.

This instructor-led live training, available both online and onsite, targets intermediate-level AI engineers and data scientists eager to develop and fine-tune neural network models utilizing 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 leveraging MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models across edge or cloud infrastructures.

Course Format

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

Customization Options

• To arrange customized training tailored to your specific infrastructure or datasets for this course, please contact us.

Huawei's comprehensive AI stack — spanning from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.

This instructor-led training, available either online or onsite, is designed for technical professionals at beginner to intermediate levels who want to understand how CANN and MindSpore collaborate to manage the AI lifecycle and support infrastructure decisions.

Upon completing this training, participants will be equipped to:

• Comprehend the layered architecture of Huawei’s AI compute stack.
• Identify the role of CANN in model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and its toolchain in comparison to industry alternatives.
• Position Huawei's AI stack effectively 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.

Customization Options

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

This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenACC to program heterogeneous devices and exploit their parallelism.

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

• Set up an OpenACC development environment.
• Write and run a basic OpenACC program.
• Annotate code with OpenACC directives and clauses.
• Use OpenACC API and libraries.
• Profile, debug, and optimize OpenACC programs.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization utilities 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 professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.

Upon completing this training, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Leveraging CANN utilities to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines tailored for edge or cloud-based deployment environments.

Course Format

• Interactive lectures and demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP scenarios.

Customization Options

• For those requiring customized training for this course, please contact us to arrange.

This instructor-led, live training in Maia (online or onsite) is designed for beginner to intermediate developers wishing to learn the essentials of GPU programming and the main frameworks and tools for developing GPU applications.

• By the conclusion of this training, participants will be able to:
  Grasp the distinctions between CPU and GPU computing, as well as the advantages and challenges associated with GPU programming.
• Select the most appropriate framework and tool for their specific GPU application.
• Develop a fundamental GPU program that executes vector addition using one or more of the discussed frameworks and tools.
• Utilise the relevant APIs, languages, and libraries to query device details, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronise threads.
• Leverage the corresponding memory spaces—such as global, local, constant, and private—to optimise data transfers and memory access patterns.
• Apply the respective execution models, including work-items, work-groups, threads, blocks, and grids, to manage parallelism effectively.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimise GPU programs employing techniques like coalescing, caching, prefetching, and profiling.

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

This instructor-led live training (available online or onsite) is designed for advanced system developers who want to create, deploy, and fine-tune custom operators for AI models using CANN’s TIK programming model and TVM compiler integration.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and demonstrations.
• Practical coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Customization Options

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

This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use different frameworks for GPU programming and compare their features, performance, and compatibility.

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

• Set up a development environment that includes OpenCL SDK, CUDA Toolkit, ROCm Platform, a device that supports OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Create a basic GPU program that performs vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution of each framework.
• Use the respective APIs to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective languages to write kernels that execute on the device and manipulate data.
• Use the respective built-in functions, variables, and libraries to perform common tasks and operations.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs using techniques such as coalescing, caching, prefetching, and profiling.

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

This instructor-led live training, available online or onsite, targets beginner to intermediate AI professionals seeking to deploy and monitor AI models using CloudMatrix with CANN and MindSpore integration.

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

• Utilizing CloudMatrix for model packaging, deployment, and serving.
• Converting and optimizing models for Ascend chipsets.
• Establishing pipelines for both real-time and batch inference tasks.
• Monitoring deployments and tuning performance within production environments.

Course Format

• Interactive lectures and discussions.
• Practical application of CloudMatrix in real-world deployment scenarios.
• Guided exercises focusing on conversion, optimization, and scaling.

Course Customization Options

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

This instructor-led, live training in Maia (online or onsite) is aimed at artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, etc.

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

• Learn how to create animations and visual effects with Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D motions.
• Introduction to non-destructive modelling and animation.
• Export 3D models and assets to a game engine, 3D printer, or other software.

Huawei's Ascend CANN toolkit empowers powerful AI inference on edge devices like the Ascend 310. It offers vital tools for compiling, optimising, and deploying models in environments with constrained compute and memory resources.

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI developers and integrators looking to deploy and optimise 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 employing MindSpore Lite and AscendCL.
• Optimise model performance for environments with limited compute and memory.
• Deploy and monitor AI applications in real-world edge use cases.

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.

Course Customisation Options

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

This instructor-led live training in Maia (online or onsite) is designed for beginner to intermediate developers who wish to install and use ROCm on Windows to program AMD GPUs and exploit their parallelism.

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

• Set up a development environment that includes ROCm Platform, an AMD GPU, and Visual Studio Code on Windows.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use HIP language to write kernels that execute on the GPU and manipulate data.
• Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use ROCm and HIP to program AMD GPUs and exploit their parallelism.

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

• Set up a development environment that includes ROCm Platform, a AMD GPU, and Visual Studio Code.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use HIP language to write kernels that execute on the GPU and manipulate data.
• Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
• Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.

This instructor-led live training in Maia (online or in-person) is aimed at artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, etc.

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

• Understand the principles and core concepts of 3D modeling.
• Explore a variety of modes and tools for modeling and editing 3D meshes.
• Use the tools for UV mapping/unwrapping, sculpting, and painting 3D models renderring.

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

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 using 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.

Ascend, Biren, and Cambricon are prominent AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale AI workloads.

This instructor-led, live training session (available online or onsite) is designed for advanced AI infrastructure and performance engineers seeking to enhance model inference and training workflows across various Chinese AI chip ecosystems.

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

• Benchmark models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory or compute inefficiencies.
• Implement optimizations at the graph, kernel, and operator levels.
• Tune deployment pipelines to enhance throughput and reduce latency.

Format of the Course

• Interactive lectures and discussions.
• Practical application of profiling and optimization tools on each platform.
• Guided exercises focused on real-world tuning scenarios.

Course Customization Options

• To request a customized training version based on your specific performance environment or model type, please contact us to make arrangements.

This instructor-led, live training in Maia (available online or onsite) is designed for beginner to intermediate graphic designers and animators who wish to learn how to create stunning animations, interactive media, and engaging web content using Adobe Animate.

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

• Navigating the Adobe Animate interface and utilizing its tools effectively.
• Creating and editing animations through keyframes, motion tweens, and shape tweens.
• Designing interactive animations and applications using ActionScript and JavaScript.
• Integrating audio and video elements into projects.
• Exporting animations for web, video, and mobile platforms.

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

This instructor-led training session (available online or onsite) targets advanced AI developers and system engineers aiming to enhance inference performance through CANN’s advanced toolset, which includes the Graph Engine, TIK, and capabilities for custom operator development.

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 performance analysis and optimisation.
• Develop and optimise 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.
• Optimisation exercises based on edge-case deployment examples.

Course Customisation Options

• To arrange a bespoke training session for this course, please contact us.

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

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

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

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

Course Format

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

Course Customization Options

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

This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use CUDA to program NVIDIA GPUs and exploit their parallelism.

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

• Set up a development environment that includes CUDA Toolkit, a NVIDIA GPU, and Visual Studio Code.
• Create a basic CUDA program that performs vector addition on the GPU and retrieves the results from the GPU memory.
• Use the CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the CUDA C/C++ language to write kernels that execute on the GPU and manipulate data.
• Use CUDA built-in functions, variables, and libraries to perform common tasks and operations.
• Use CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use the CUDA execution model to control the threads, blocks, and grids that define the parallelism.
• Debug and test CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize CUDA programs using techniques such as coalescing, caching, prefetching, and profiling.

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Psdto3D101 is a specialist software tool engineered to generate lenticular files featuring 3D, flip, morph, and motion effects. This course equips participants with the necessary knowledge and practical expertise to harness Psdto3D101 for producing high-quality lenticular outputs, addressing both the technical management and creative application of the software.

Delivered as an instructor-led live training (available online or onsite), this programme is tailored for beginner to intermediate graphic and print professionals eager to create, manage, and optimise lenticular files using Psdto3D101.

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

• Grasp the core principles of lenticular printing and design.
• Navigate and configure Psdto3D101 for various project requirements.
• Generate 3D, flip, and motion effects using layers and sequences.
• Export and prepare lenticular files ready for professional printing.

Course Format

• Interactive lectures and discussions.
• Practical exercises using Psdto3D101 software.
• Hands-on implementation within a live-lab environment.

Customisation Options

• For bespoke training arrangements, please contact us.

CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI compute stack, designed to facilitate the deployment and optimization of AI models on Ascend AI processors.

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

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

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

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on lab exercises employing CANN tools, Ascend simulators, or physical devices.
• Practical deployment scenarios derived from real-world AI models.

Customization Options

• For those wishing to request a customized version of this course, please reach out to us to make the necessary 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 completion of this training, participants will be able to:

• Grasp Biren GPU architecture and memory hierarchy.
• Configure the development environment and utilize Biren’s programming model.
• Translate and optimize CUDA-style code for Biren platforms.
• Implement performance tuning and debugging techniques.

Course Format

• Interactive lectures and discussions.
• Practical application of the Biren SDK in sample GPU workloads.
• Guided exercises focused on porting and performance tuning.

Customization Options

• To request customized training tailored to your application stack or integration requirements, please contact us to arrange.

This instructor-led, live training in Maia (online or onsite) is tailored for artists, game developers, or complete beginners who wish to utilise Blender to create 3D models for interactive applications, video games, animated films, and other projects.

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

• Grasp the principles and core concepts underpinning 3D modelling.
• Explore a wide range of modes and tools for modelling and editing 3D meshes.
• Learn how to produce animations and visual effects using Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D movements.
• Utilise tools for UV mapping/unwrapping, sculpting, and painting 3D models.
• Export 3D models and assets to game engines, 3D printers, or other software.

Cambricon MLUs (Machine Learning Units) are specialised AI chips optimised for inference and training in edge and datacentre environments.

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

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

• Set up and configure the BANGPy and Neuware development environments.
• Develop and optimise Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and data centre devices running the Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimisation, integration, and testing.

Course Customisation Options

• To request a customised training for this course based on your Cambricon device model or use case, please contact us to arrange.

This instructor-led live training in Maia (online or onsite) is designed for beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.

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

• Comprehend the architecture, components, and capabilities of CUDA.
• Install and configure CUDA environments.
• Manage and optimize CUDA resources.
• Debug and troubleshoot common CUDA issues.

This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenCL to program heterogeneous devices and exploit their parallelism.

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

• Set up a development environment that includes OpenCL SDK, a device that supports OpenCL, and Visual Studio Code.
• Create a basic OpenCL program that performs vector addition on the device and retrieves the results from the device memory.
• Use OpenCL API to query device information, create contexts, command queues, buffers, kernels, and events.
• Use OpenCL C language to write kernels that execute on the device and manipulate data.
• Use OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
• Use OpenCL host and device memory models to optimize data transfers and memory accesses.
• Use OpenCL execution model to control the work-items, work-groups, and ND-ranges.
• Debug and test OpenCL programs using tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
• Optimize OpenCL programs using techniques such as vectorization, loop unrolling, local memory, and profiling.

This instructor-led live training in Maia (online or onsite) is aimed at beginner-level to advanced-level 3D design and 3D printing enthusiasts who wish to use Fusion 360 to design, simulate, and prepare models for 3D printing.

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

• Install and configure Fusion 360 for optimal performance.
• Design, model, and simulate 3D objects in a unified environment.
• Optimise and prepare designs for the 3D printing process.
• Collaborate and share their designs using Fusion 360's cloud capabilities.

FreeCAD is an open-source, customizable, multi-platform, scriptable and extensible parametric 3D modeler for designing real-life objects.

In this instructor-led, live training, participants will learn how to model an object for later construction or 3D printing.

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

• Use FreeCAD's tools and workflows to replace commercial CAD or BIM modeling software
• Write to many formats such as STEP, IGES, STL, SVG, DXF, OBJ, IFC, DAE, etc.
• Use Python to extend FreeCAD's functionality
• Automate FreeCAD, build modules and embed FreeCAD into an existing application

Audience

• Designers
• Developers
• Mechanical engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training course in Maia explains how to program GPUs for parallel computing, utilize diverse platforms, navigate the CUDA platform and its capabilities, and execute various optimization techniques with CUDA. Key application areas include deep learning, analytics, image processing, and engineering solutions.