GPU Training in Christchurch

Huawei Ascend is a family of AI processors designed for high-performance inference and training.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimise neural network models using Huawei's Ascend platform and the CANN toolkit.

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

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimise performance on Ascend NPUs using custom operators and tiling.
• Deploy models to edge or cloud environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
• Guided exercises focused on model building, training, and deployment.

Course Customisation Options

• To request a customised training for this course based on your infrastructure or datasets, please contact us to arrange.

Huawei's AI stack — spanning from the low-level CANN SDK to the high-level MindSpore framework — delivers a tightly integrated AI development and deployment environment optimised for Ascend hardware.

This instructor-led, live training (available online or on-site) is designed for beginner to intermediate-level technical professionals seeking to understand how CANN and MindSpore components work together to support AI lifecycle management and inform 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.

Course Format

• Interactive lecture and discussion.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs exploring model flow from MindSpore to CANN.

Course Customisation Options

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

This instructor-led, live training in Christchurch (available online or on-site) is designed for beginner to intermediate-level developers who wish to leverage OpenACC to program heterogeneous devices and harness their parallelism. 

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

• Set up an OpenACC development environment.
• Write and execute a basic OpenACC program.
• Annotate code using OpenACC directives and clauses.
• Utilise the OpenACC API and associated libraries.
• Profile, debug, and optimise OpenACC programs.

The CANN SDK (Compute Architecture for Neural Networks) delivers powerful deployment and optimisation tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (online or on-site) is designed for intermediate-level AI practitioners who wish to build, deploy and optimise vision and language models using the CANN SDK for production use cases.

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

• Deploy and optimise CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimise inference performance for tasks such as detection, classification and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Course Format

• Interactive lecture and demonstration.
• Hands-on lab covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

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

This instructor-led, live training in Christchurch (available online or on-site) is designed for beginner to intermediate-level developers who wish to learn the fundamentals of GPU programming and the primary frameworks and tools used to develop GPU applications.

• By the end of this training, participants will be able to:
  Understand the differences between CPU and GPU computing, as well as the benefits and challenges of GPU programming.
• Select the appropriate framework and tool for their specific GPU application.
• Develop a basic GPU program that performs vector addition using one or more of the available frameworks and tools.
• Utilise the relevant APIs, languages, and libraries to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronise threads.
• Employ the respective memory spaces, including global, local, constant, and private, to optimise data transfers and memory accesses.
• Apply the corresponding execution models, such as work-items, work-groups, threads, blocks, and grids, to control parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimise GPU programs through techniques such as coalescing, caching, prefetching, and profiling.

CANN TIK (Tensor Instruction Kernel) and Apache TVM enable advanced optimisation and customisation of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at advanced-level system developers who wish to build, deploy, and tune custom operators for AI models using CANN's TIK programming model and TVM compiler integration.

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

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

Format of the Course

• Interactive lecture and demonstration.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customisation Options

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

This instructor-led, live training in Christchurch (online or on-site) is designed for beginner to intermediate-level developers seeking to explore different GPU programming frameworks and compare their features, performance, and compatibility.

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

• Configure a development environment comprising the OpenCL SDK, CUDA Toolkit, ROCm Platform, a compatible device supporting OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Develop a basic GPU program that performs vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution characteristics of each framework.
• Leverage respective APIs to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronise threads.
• Utilise framework-specific languages to write kernels that execute on the device and manipulate data.
• Employ built-in functions, variables, and libraries to perform common tasks and operations.
• Apply appropriate memory spaces, such as global, local, constant, and private, to optimise data transfers and memory accesses.
• Implement execution models to manage threads, blocks, and grids that define parallelism.
• Debug and test GPU programs using tools such as CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimise GPU programs through techniques including coalescing, caching, prefetching, and profiling.

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

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

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

• Leverage CloudMatrix for model packaging, deployment, and serving.
• Convert and optimise models for Ascend chipsets.
• Establish pipelines for both real-time and batch inference tasks.
• Monitor deployments and fine-tune performance in production environments.

Course Format

• Interactive lectures and group discussions.
• Practical, hands-on experience with CloudMatrix using real-world deployment scenarios.
• Guided exercises focused on model conversion, optimisation, and scaling.

Course Customisation Options

• To request a customised training session tailored to your AI infrastructure or cloud environment, please contact us to arrange.

Huawei's Ascend CANN toolkit delivers powerful AI inference capabilities on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimising, and deploying models in environments where compute and memory resources are limited.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI developers and integrators who wish to deploy and optimise models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Build lightweight inference pipelines using MindSpore Lite and AscendCL.
• Optimise model performance for constrained compute and memory environments.
• Deploy and monitor AI applications in real-world edge use cases.

Format of the Course

• Interactive lectures and live demonstrations.
• Hands-on laboratory work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

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

This instructor-led, live training in Christchurch (online or on-site) is designed for beginner to intermediate-level developers who wish to install and use ROCm on Windows to program AMD GPUs and leverage their parallel processing capabilities.

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

• Set up a development environment comprising the 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 results from GPU memory.
• Use the ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronise threads.
• Use the HIP language to write kernels that execute on the GPU and manipulate data.
• Utilise HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Apply ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimise data transfers and memory accesses.
• Leverage ROCm and HIP execution models to control the threads, blocks, and grids that define parallelism.
• Debug and test ROCm and HIP programs using tools such as the ROCm Debugger and ROCm Profiler.
• Optimise ROCm and HIP programs using techniques including coalescing, caching, prefetching, and profiling.

This instructor-led, live training in Christchurch (available online or on-site) is designed for beginner to intermediate-level developers who wish to use ROCm and HIP to program AMD GPUs and harness their parallel capabilities.

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

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

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

This instructor-led, live training (online or on-site) 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 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 with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimisation 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 Customisation Options

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

Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimise model inference and training workflows across multiple Chinese AI chip platforms.

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

• Benchmark models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Apply graph-level, kernel-level, and operator-level optimisations.
• Tune deployment pipelines to improve throughput and latency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of profiling and optimisation tools on each platform.
• Guided exercises focused on practical tuning scenarios.

Course Customisation Options

• To request a customised training for this course based on your performance environment or model type, please contact us to arrange.

CANN SDK (Compute Architecture for Neural Networks) is Huawei's AI compute foundation that enables developers to fine-tune and optimise the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (available online or on-site) is designed for advanced-level AI developers and system engineers who wish to enhance inference performance using CANN's advanced toolset, including the Graph Engine, TIK, and custom operator development.

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

• Understand CANN's runtime architecture and performance lifecycle.
• Use profiling tools and the Graph Engine for performance analysis and optimisation.
• Create and optimise custom operators using TIK and TVM.
• Resolve memory bottlenecks and improve model throughput.

Course Format

• Interactive lectures and discussions.
• Hands-on labs featuring real-time profiling and operator tuning.
• Optimisation exercises using edge-case deployment scenarios.

Course Customisation Options

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

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) is tailored for advanced-level GPU programmers and infrastructure specialists seeking to migrate and optimise existing CUDA applications for deployment on Chinese hardware platforms.

By the end 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 and identify optimisation opportunities across platforms.
• Address practical challenges related to cross-architecture support and deployment.

Course Format

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

Course Customisation Options

• To request a customised training session for this course based on your platform or CUDA project, please contact us to arrange.

This instructor-led, live training in Christchurch (online or on-site) is designed for beginner to intermediate-level developers who wish to use CUDA to program NVIDIA GPUs and harness their parallel processing capabilities.

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

• Set up a development environment that includes the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
• Create a basic CUDA program that performs vector addition on the GPU and retrieves results from GPU memory.
• Use the CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronise 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.
• Leverage CUDA memory spaces, such as global, shared, constant, and local, to optimise data transfers and memory accesses.
• Apply the CUDA execution model to control threads, blocks, and grids that define parallelism.
• Debug and test CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimise CUDA programs using techniques such as coalescing, caching, prefetching, and profiling.

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CANN (Compute Architecture for Neural Networks) is Huawei's AI compute stack for deploying and optimising AI models on Ascend AI processors.

This instructor-led, live training (online or on-site) is designed for intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch.

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

• Understand the CANN architecture and its role in the AI deployment pipeline.
• Convert and adapt models from popular frameworks to Ascend-compatible formats.
• Use tools like ATC, OM model conversion, and MindSpore for edge and cloud inference.
• Diagnose deployment issues and optimise performance on Ascend hardware.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customisation Options

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

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

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced developers who aim to program and optimise 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 utilise Biren’s programming model.
• Translate and optimise CUDA-style code for Biren platforms.
• Apply performance tuning and debugging techniques.

Format of the Course

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

Course Customisation Options

• To request a customised training session based on your application stack or integration needs, please contact us to arrange.

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

This instructor-led, live training (online or on-site) 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 datacentre devices running the Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Course Format

• Interactive lectures and discussions.
• Hands-on application of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimisation, integration, and testing.

Course Customisation Options

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

This instructor-led, live training in Christchurch (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:

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

This instructor-led, live training in Christchurch (online or on-site) is designed for beginner to intermediate-level developers who wish to use OpenCL to program heterogeneous devices and harness their parallel capabilities.

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

• Set up a development environment that includes the OpenCL SDK, a device supporting OpenCL, and Visual Studio Code.
• Create a basic OpenCL program that performs vector addition on the device and retrieves results from device memory.
• Use the OpenCL API to query device information and create contexts, command queues, buffers, kernels, and events.
• Use the 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.
• Apply OpenCL host and device memory models to optimise data transfers and memory accesses.
• Leverage the OpenCL execution model to control work-items, work-groups, and ND-ranges.
• Debug and test OpenCL programs using tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
• Optimise OpenCL programs using techniques such as vectorisation, loop unrolling, local memory usage, and profiling.

This instructor-led, live training in Christchurch (online or onsite) is aimed at intermediate-level developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.

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

• Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
• Create, compile and launch custom CUDA kernels.
• Manage GPU memory.
• Convert a CPU-based application into a GPU-accelerated application.

This instructor-led, live training course in Christchurch covers how to program GPUs for parallel computing, how to use various platforms, how to work with the CUDA platform and its features, and how to perform various optimisation techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.