Delivered either online or onsite, these instructor-led live training courses on GPU (Graphics Processing Unit) utilize interactive discussions and hands-on practice to impart the core principles of GPU technology and programming techniques.
GPU training is offered in two formats: "online live training" and "onsite live training." Online live training (also known as "remote live training") is conducted through an interactive remote desktop. Onsite live training can be provided directly at your premises in Bhutan or at NobleProg corporate training centers in Bhutan.
NobleProg -- Your Local Training Provider
Bhutan, Thimphu - Classroom
near Le Méridien , Chorten Lam, Thimphu, Bhutan, 11001
Set in Thimphu, this classroom is well located in Chorten Lam with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Bhutan, Paro - Classroom
near Le Méridien Riverfront, thimphu hwy, Shaba, Paro, Bhutan, 12001
Set in Paro, this classroom is well located near Paro-Thimphu Highway around 4 km from the airport, and 7 km from Rinpung Dzong, and possess all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This live, instructor-led course in Bhutan empowers intermediate AI engineers to construct and refine neural network models via the Huawei Ascend platform and CANN toolkit. Learners will configure environments, develop applications using MindSpore, and deploy solutions to edge or cloud environments.
Huawei's AI stack, spanning from the low-level CANN SDK to the high-level MindSpore framework, provides a closely integrated environment for developing and deploying AI solutions, specifically optimized for Ascend hardware.
This instructor-led, live training (available online or onsite) is designed for technical professionals at beginner to intermediate levels who wish to comprehend how the CANN and MindSpore components collaborate to facilitate AI lifecycle management and inform infrastructure decisions.
Upon completion of this training, participants will be equipped to:
Grasp the layered architecture of Huawei's AI compute stack.
Recognise how CANN aids in model optimization and hardware-level deployment.
Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
Place Huawei's AI stack within the context of enterprise or cloud/on-premises environments.
Course Format
Interactive lectures and discussions.
Live system demonstrations and case-based walkthroughs.
Optional guided labs covering the model flow from MindSpore to CANN.
Course Customization Options
For those seeking a customized training version of this course, please reach out to us to make arrangements.
This instructor-led, live training in Bhutan (online or onsite) targets beginner to intermediate developers who wish to utilize 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.
The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.
This instructor-led, live training (available online or onsite) targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.
Upon completion of this training, participants will be equipped to:
Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
Utilize CANN utilities to convert models and seamlessly integrate them into active pipelines.
Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment environments.
Course Format
Interactive lectures combined with practical demonstrations.
Hands-on labs focusing on model deployment and performance profiling.
Live pipeline design exercises utilizing real-world CV and NLP use cases.
Customization Options
To request a customized training session for this course, please contact us to make arrangements.
This instructor-led live training in Bhutan (online or onsite) targets beginner to intermediate developers keen on learning the fundamentals of GPU programming and the key frameworks and tools for developing GPU applications.
Upon completing this training, participants will be able to: Understand the differences between CPU and GPU computing, including the benefits and challenges of GPU programming.
Choose the right framework and tool for their GPU application.
Create a basic GPU program that performs vector addition using one or more of the frameworks and tools.
Use the respective APIs, languages, and libraries to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
Use the respective execution models, such as work-items, work-groups, threads, blocks, and grids, to control 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.
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 both online and onsite, is designed for advanced-level system developers looking to build, deploy, and tune custom operators for AI models by leveraging CANN’s TIK programming model and TVM compiler integration.
Upon completion of this training, participants will be capable of:
Writing and testing custom AI operators utilizing the TIK DSL for Ascend processors.
Integrating custom operations into the CANN runtime and execution graph.
Employing TVM for operator scheduling, auto-tuning, and benchmarking.
Debugging and optimizing instruction-level performance for custom computational patterns.
Course Format
Interactive lectures and demonstrations.
Practical coding exercises for operators using TIK and TVM pipelines.
Testing and tuning on Ascend hardware or simulators.
Course Customization Options
For inquiries regarding customized training for this course, please get in touch with us to arrange it.
This instructor-led, live training in Bhutan (online or onsite) targets beginner to intermediate developers who want to utilise diverse frameworks for GPU programming and compare their features, performance, and compatibility.
Upon completing this training, participants will be able to:
Set up a development environment featuring the OpenCL SDK, CUDA Toolkit, ROCm Platform, a device supporting OpenCL, CUDA, or ROCm, and Visual Studio Code.
Develop a basic GPU program performing vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution of each framework.
Employ the respective APIs to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
Utilise the respective languages to write kernels that execute on the device and manipulate data.
Use the respective built-in functions, variables, and libraries to carry out common tasks and operations.
Leverage the respective memory spaces, such as global, local, constant, and private, to optimise data transfers and memory accesses.
Apply the respective execution models to manage the threads, blocks, and grids that define parallelism.
Debug and test GPU programs using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
Optimise GPU programs employing techniques such as coalescing, caching, prefetching, and profiling.
CloudMatrix, Huawei’s unified platform for AI development and deployment, enables scalable, production-grade inference pipelines.
This instructor-led live training, available online or on-site, targets beginner to intermediate AI professionals seeking to deploy and monitor AI models via CloudMatrix, integrating CANN and MindSpore.
Upon completing this training, participants will be able to:
Leverage CloudMatrix for model packaging, deployment, and serving.
Convert and optimize models for Ascend chipsets.
Establish pipelines for both real-time and batch inference tasks.
Monitor deployments and tune performance in production environments.
Course Format
Interactive lectures and discussions.
Practical application of CloudMatrix with real-world deployment scenarios.
Guided exercises focusing on conversion, optimization, and scaling.
Customization Options
For customized training aligned with your specific AI infrastructure or cloud environment, please reach out to arrange a session.
This instructor-led, live training in Bhutan covers the core concepts and hands-on fundamentals of deploying AI models on Ascend edge devices using the CANN toolkit, helping participants build practical skills for compiling, optimizing, and managing constrained environments.
This instructor-led, live training in Bhutan (online or onsite) is aimed at beginner-level to intermediate-level 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, a 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 Bhutan (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, an 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.
CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.
This instructor-led live training, available either online or onsite, targets beginner-level AI developers keen on understanding CANN's role within the model lifecycle—from training to deployment—and its interaction with 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 integrating CANN and MindSpore.
Convert and deploy a basic AI model onto Ascend hardware.
Build foundational knowledge for future CANN optimization or integration initiatives.
Course Format
Interactive lectures and discussions.
Practical 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 get in touch with us.
Ascend, Biren, and Cambricon represent the forefront of AI hardware platforms in China, each providing distinct acceleration and profiling capabilities designed for large-scale AI workloads.
This instructor-led live training session (available online or onsite) is tailored for advanced-level AI infrastructure and performance engineers looking to enhance model inference and training workflows across various Chinese AI chip architectures.
Upon completion of this training, participants will be equipped to:
Evaluate and benchmark models across Ascend, Biren, and Cambricon environments.
Pinpoint system bottlenecks and identify inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Optimize deployment pipelines to achieve superior throughput and reduced latency.
Training Format
Interactive lectures coupled with open discussions.
Practical application of profiling and optimization tools on each specific platform.
Guided exercises centered on real-world tuning scenarios.
Customization Options
For tailored training sessions based on your specific performance environment or model requirements, please reach out to us to coordinate.
The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute framework, empowering developers to fine-tune and maximise the performance of neural networks deployed on Ascend AI processors.
This instructor-led training session, available both online and onsite, is designed for advanced-level AI developers and system engineers who aim to enhance inference performance by leveraging CANN’s sophisticated toolset. Key components include the Graph Engine, TIK, and custom operator development.
Upon completing this training, participants will be equipped to:
Gain a comprehensive understanding of CANN’s runtime architecture and performance lifecycle.
Utilise profiling tools and the Graph Engine for thorough performance analysis and optimisation.
Develop and optimise custom operators using TIK and TVM.
Address memory bottlenecks and significantly improve model throughput.
Course Format
Engaging interactive lectures and discussions.
Practical hands-on labs featuring real-time profiling and operator tuning.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and High-Performance Computing (HPC) markets.
This instructor-led live training, available online or onsite, targets advanced GPU programmers and infrastructure specialists looking to migrate and optimize their 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 key optimization points across different platforms.
Address practical challenges related to cross-architecture support and deployment.
Format of the Course
Interactive lectures and discussions.
Hands-on labs focused on code translation and performance comparison.
This instructor-led, live training in Bhutan (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, an 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.
This live training in Bhutan equips intermediate AI developers with the expertise to deploy models on Ascend processors using the CANN toolkit. Participants will learn to convert models from frameworks like PyTorch and TensorFlow, optimize performance, and troubleshoot issues to ensure efficient edge and cloud inference.
Biren AI Accelerators are high-performance GPUs engineered for AI and HPC workloads, enabling large-scale training and inference.
This instructor-led live training (available online or onsite) targets intermediate to advanced developers who want to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.
Upon completing this training, participants will be able to:
Grasp the Biren GPU architecture and its 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 strategies.
Course Format
Interactive lectures and discussions.
Hands-on exploration of the Biren SDK using sample GPU workloads.
Guided exercises concentrating on porting and performance tuning.
Customization Options
For customized training tailored to your specific application stack or integration requirements, please contact us to arrange a session.
This instructor-led live training in Bhutan equips developers with the skills to build and deploy AI models using BANGPy and Neuware on Cambricon MLUs. Participants will configure environments, develop optimized models, and integrate MLU acceleration into edge and data center applications.
This instructor-led, live training in Bhutan (online or onsite) is designed for beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.
Upon completing this training, participants will be able to:
Comprehend the architecture, components, and capabilities of CUDA.
This instructor-led, live training in Bhutan (online or onsite) is designed for beginner to intermediate-level developers who want to use OpenCL to program heterogeneous devices and leverage their parallelism.
Upon completion of this training, participants will be able to:
Set up a development environment comprising the OpenCL SDK, an OpenCL-compatible device, and Visual Studio Code.
Develop a fundamental OpenCL program that performs vector addition on the device and retrieves results from device memory.
Utilize the OpenCL API to query device information, and create contexts, command queues, buffers, kernels, and events.
Write kernels using the OpenCL C language to execute on the device and manipulate data.
Employ OpenCL built-in functions, extensions, and libraries to carry out common tasks and operations.
Leverage OpenCL host and device memory models to optimize data transfers and memory access.
Use the OpenCL execution model to manage work-items, work-groups, and ND-ranges.
Debug and test OpenCL programs using tools like CodeXL, Intel VTune, and NVIDIA Nsight.
Optimize OpenCL programs through techniques such as vectorization, loop unrolling, local memory usage, and profiling.
This instructor-led, live training in Bhutan (online or onsite) is designed for C++ developers who intend to use CUDA to expedite applications, develop high-performance GPU kernels, and exploit parallel algorithm libraries for scientific computing, data processing, and machine learning tasks.
This instructor-led, live training in Bhutan (online or onsite) is designed for C/C++ developers who wish to utilize CUDA to accelerate compute-intensive applications, including data processing, scientific simulations, machine learning workloads, and image processing pipelines.
This instructor-led, live training in Bhutan (online or onsite) targets software developers, data analysts, and technical professionals who want to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.
This instructor-led, live training programme conducted in Bhutan explores GPU programming for parallel computing, the use of various platforms, hands-on work with CUDA features, and optimization strategies using CUDA. Relevant applications include deep learning, analytics, image processing, and engineering use cases.
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Testimonials (1)
Trainers energy and humor.
Tadeusz Kaluba - Nokia Solutions and Networks Sp. z o.o.
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