Whether delivered online or at your location, instructor-led live GPU (Graphics Processing Unit) training courses explore the fundamentals of GPUs and programming techniques through engaging discussions and practical, hands-on exercises.
GPU training is offered in two formats: "online live training" and "onsite live training." Online sessions (also known as "remote live training") are conducted interactively via remote desktop. Onsite training can be hosted locally at your premises in Bhutan or at NobleProg's 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.
Huawei Ascend comprises a suite of AI processors engineered for high-performance inference and training workloads.
This instructor-led live training, available in online or onsite formats, targets intermediate AI engineers and data scientists seeking to develop and optimise neural network models leveraging 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.
Construct AI applications utilizing MindSpore and CloudMatrix workflows.
Enhance performance on Ascend NPUs through custom operators and tiling techniques.
Deploy models across edge or cloud infrastructure.
Course Format
Interactive lectures coupled with group discussions.
Practical application of Huawei Ascend and the CANN toolkit within sample projects.
Guided exercises centred on model construction, training, and deployment.
Customisation Options
For tailored training aligned with your specific infrastructure or datasets, please reach out to us to arrange a bespoke session.
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.
The Huawei Ascend CANN toolkit facilitates robust AI inference on edge devices like the Ascend 310. It offers critical tools for compiling, optimizing, and deploying models in environments with limited 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 optimize models on Ascend edge devices using the CANN toolchain.
Upon completing this training, participants will be equipped to:
Prepare and convert AI models for the Ascend 310 using CANN tools.
Construct lightweight inference pipelines leveraging MindSpore Lite and AscendCL.
Enhance model performance in environments with restricted compute and memory.
Deploy and monitor AI applications in practical, real-world edge scenarios.
Format of the Course
Interactive lectures and demonstrations.
Practical lab sessions focusing on edge-specific models and scenarios.
Live deployment examples on virtual or physical edge hardware.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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.
CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed for deploying and optimizing AI models on Ascend AI processors.
This instructor-led, live training (available online or onsite) is tailored for intermediate-level AI developers and engineers who aim to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.
Upon completing this training, participants will be able to:
Gain insight into the CANN architecture and its significance within the AI deployment pipeline.
Convert and adapt models from widely-used frameworks into formats compatible with Ascend.
Utilize tools like ATC, OM model conversion, and MindSpore for inference tasks in both edge and cloud environments.
Troubleshoot deployment challenges and optimize performance on Ascend hardware.
Course Format
Interactive lectures accompanied by live demonstrations.
Practical lab exercises employing CANN tools along with Ascend simulators or physical devices.
Real-world deployment scenarios based on actual AI models.
Customization Options
For a customized version of this training, please contact us to make arrangements.
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.
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize both inference and training tasks in edge computing and data center environments.
This instructor-led live training, available either online or on-site, targets intermediate-level developers who aim to construct and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completion of this training, participants will be able to:
Establish and configure the development environments for BANGPy and Neuware.
Create and optimize models using Python and C++ specifically for Cambricon MLUs.
Deploy models onto edge and data center devices operating on the Neuware runtime.
Integrate machine learning workflows with acceleration capabilities specific to MLUs.
Course Format
Engaging lectures paired with interactive discussions.
Practical, hands-on sessions involving development and deployment with BANGPy and Neuware.
Guided exercises concentrating on optimization, integration, and testing.
Customization Options for the Course
If you require customized training tailored to your specific Cambricon device model or use case, please get in touch with us to make the necessary arrangements.
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 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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