GPU Training in Tashkent

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 — from the low-level CANN SDK to the high-level MindSpore framework — provides a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) targets beginner-level to intermediate-level technical professionals who want 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.

This instructor-led, live training in Tashkent (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 powerful deployment and optimisation tools for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

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

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

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

Course Format

• Interactive lectures and demonstrations.
• Practical labs covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange the details.

This instructor-led, live training in Tashkent (online or onsite) is designed for beginner to intermediate developers who wish to learn the fundamentals of GPU programming and the key frameworks and tools for developing 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 GPU application.
• Develop a basic GPU program that performs vector addition using one or more of the frameworks and tools.
• Utilize the relevant APIs, languages, and libraries to retrieve device information, manage device memory, transfer data between host and device, initiate kernels, and synchronize threads.
• Employ various memory spaces, including global, local, constant, and private, to optimize data transfer and memory access.
• Apply execution models, such as work-items, work-groups, threads, blocks, and grids, to manage parallelism.
• Debug and test GPU programs using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs through techniques such as coalescing, caching, prefetching, and profiling.

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

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

Upon completing this training, participants will be capable of:

• Writing and testing custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operators into the CANN runtime and execution graph.
• Utilizing TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computational patterns.

Course Format

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

Customization Options

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

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

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

• Set up a development environment equipped with OpenCL SDK, CUDA Toolkit, ROCm Platform, a compatible device, and Visual Studio Code.
• Develop a basic GPU program for vector addition using OpenCL, CUDA, and ROCm, comparing their syntax, structure, and execution.
• Utilize APIs to query device information, manage device memory, transfer data between host and device, launch kernels, and synchronize threads.
• Write kernels in the respective languages to execute on the device and manipulate data.
• Leverage built-in functions, variables, and libraries to perform common tasks and operations.
• Optimize data transfers and memory accesses using memory spaces such as global, local, constant, and private.
• Control parallelism by managing threads, blocks, and grids using the respective execution models.
• Debug and test GPU programs using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize GPU programs through techniques such as coalescing, caching, prefetching, and profiling.

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

This instructor-led live training, available either online or onsite, targets beginner to intermediate-level AI professionals looking to deploy and monitor AI models using the CloudMatrix platform, integrated with CANN and MindSpore.

Upon completing 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 fine-tuning performance within production environments.

Course Format

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

Customization Options

• To request tailored training for this course based on your specific AI infrastructure or cloud environment, please reach out to us to arrange.

Huawei’s Ascend CANN toolkit enables powerful AI inference on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (delivered online or on-site) is designed for intermediate-level AI developers and integrators who aim 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 scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on lab sessions using edge-specific models and practical scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for beginner to intermediate-level developers who want to install and use ROCm on Windows to program AMD GPUs and utilize their parallel processing 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 on Windows.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves the 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 synchronize threads.
• Use the HIP language to write kernels that execute on the GPU and manipulate data.
• Utilize HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Leverage ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Apply ROCm and HIP execution models to control threads, blocks, and grids that define parallelism.
• Debug and test ROCm and HIP programs using tools like the 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 Tashkent (online or onsite) is designed for beginner to intermediate developers who wish to use ROCm and HIP for AMD GPU programming and leverage their parallel processing 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.
• Develop a basic ROCm program to perform vector addition on the GPU and retrieve results from GPU memory.
• Use the ROCm API to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronize threads.
• Write GPU-executable kernels and manipulate data using the HIP language.
• Utilize HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Optimize data transfers and memory accesses using ROCm and HIP memory spaces such as global, shared, constant, and local.
• Control parallelism by managing threads, blocks, and grids using ROCm and HIP execution models.
• Debug and test ROCm and HIP programs using tools like ROCm Debugger and ROCm Profiler.
• Optimize ROCm and HIP programs through techniques such as coalescing, caching, prefetching, and profiling.

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 training, available online or onsite, is tailored for beginner-level AI developers seeking to understand how CANN integrates into the model lifecycle from training to deployment, and how it works alongside frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing 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 simple AI model to Ascend hardware.
• Acquire foundational knowledge for future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and integration points.

Course Customization Options

• For customized training requests, please contact us to arrange details.

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 optimize 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 optimizations.
• Tune deployment pipelines to improve throughput and latency.

Course Format

• Interactive lectures and discussions.
• Hands-on experience with profiling and optimization tools on each platform.
• Guided exercises focused on practical tuning scenarios.

Course Customization Options

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

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

This instructor-led live training, available both online and on-site, is designed for advanced AI developers and system engineers looking to enhance inference performance using CANN’s sophisticated tools, such as the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be able to:

• Comprehend CANN’s runtime architecture and its performance lifecycle.
• Leverage profiling tools and the Graph Engine for performance analysis and optimization.
• Develop and optimize custom operators utilizing 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 request a tailored version of this course, please contact us to arrange your specific needs.

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

This instructor-led live training, available online or on-site, is designed for 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 current CUDA workloads with Chinese 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.
• 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.

Customization Options

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

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

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

• Set up a development environment including the CUDA Toolkit, an NVIDIA GPU, and Visual Studio Code.
• Develop a basic CUDA program to perform vector addition on the GPU and retrieve results from GPU memory.
• Utilize the CUDA API to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronize threads.
• Write CUDA C/C++ kernels that execute on the GPU and manipulate data.
• Employ CUDA built-in functions, variables, and libraries to perform common tasks and operations.
• Optimize data transfers and memory accesses using CUDA memory spaces such as global, shared, constant, and local.
• Control parallelism using the CUDA execution model, defining threads, blocks, and grids.
• Debug and test CUDA programs using tools like CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
• Optimize 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 optimizing AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at 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 optimize 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 Customization Options

• To request a customized training for this course, 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 tasks.

This instructor-led, live training session (available online or on-site) is designed for intermediate to advanced developers seeking to program and optimise applications using Biren's proprietary GPU stack, with practical comparisons to CUDA-based environments.

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

• Comprehend the Biren GPU architecture and its memory hierarchy.
• Configure the development environment and effectively utilise Biren's programming model.
• Translate and optimise CUDA-style code for deployment on Biren platforms.
• Apply advanced performance tuning and debugging techniques.

Course Format

• Interactive lectures and guided discussions.
• Hands-on application of the Biren SDK in real-world GPU workloads.
• Structured exercises focused on code porting and performance optimisation.

Course Customisation Options

• To request a customised training programme tailored to your specific application stack or integration requirements, please contact us to arrange a session.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to accelerate inference and training processes in both edge computing and datacenter environments.

This instructor-led training session, available online or on-site, is designed for intermediate-level developers interested in creating and deploying AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Configure and establish the development environments for BANGPy and Neuware.
• Create and optimize Python and C++ models tailored for Cambricon MLUs.
• Deploy models onto edge and data center devices operating on the Neuware runtime.
• Enhance ML workflows by integrating MLU-specific acceleration capabilities.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical, hands-on experience using BANGPy and Neuware for development and deployment.
• Guided exercises targeting optimization, integration, and testing techniques.

Customization Options

• If you require a tailored training session aligned with your specific Cambricon device model or use case, please reach out to us to arrange details.

This instructor-led, live training in Tashkent (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 optimize CUDA resources.
• Debug and troubleshoot common CUDA issues.

This instructor-led, live training in Uzbekistan (online or onsite) is tailored for beginner to intermediate developers looking to use OpenCL for programming heterogeneous devices and maximizing their parallel computing capabilities.

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

• Set up a development environment with OpenCL SDK, an OpenCL-compatible device, and Visual Studio Code.
• Develop a basic OpenCL program to perform vector addition on the device and retrieve results from device memory.
• Utilize OpenCL API to query device information, create contexts, command queues, buffers, kernels, and events.
• Write kernels in OpenCL C to execute on the device and manipulate data.
• Employ OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
• Optimize data transfers and memory accesses using OpenCL host and device memory models.
• Control work-items, work-groups, and ND-ranges using the OpenCL execution model.
• Debug and test OpenCL programs using tools like CodeXL, Intel VTune, and NVIDIA Nsight.
• Enhance OpenCL programs using techniques such as vectorization, loop unrolling, local memory, and profiling.

This instructor-led, live training course in Tashkent offers a comprehensive exploration of GPU programming for parallel computing. Participants will gain hands-on experience with various platforms, the CUDA platform, and its features, along with learning optimization techniques using CUDA. Applications include deep learning, analytics, image processing, and engineering solutions.