Whether delivered online or on-site, instructor-led live GPU (Graphics Processing Unit) training courses illustrate the fundamentals of GPU architecture and programming through interactive discussions and practical, hands-on exercises.
GPU training is offered in two formats: "online live training" or "onsite live training." Online live training (also known as "remote live training") is conducted via an interactive remote desktop. Onsite live training can be delivered locally at the customer’s premises in Nantes or at NobleProg corporate training centers located in Nantes.
NobleProg -- Your Local Training Provider
Nantes, Zenith
NobleProg Nantes, 4 rue Edith Piaf, Saint-Herblain, france, 44821
In the Parc d'Ar Mor zone, near the Zénith.
Car : from the ring road, Porte de Chézine Exit> Boulevard du Zenith > Esplanade Georges Brassens (restaurants) > Rue Edith Piaf on the right. From the N444 road (Nantes > Lorient), Exit #1 > boulevard Marcel Paul > Rue Edith Piaf at the right.
Parking Zénith P1 (free). Once parked, you can recognize the building: it's one of the tree bulding with zinc frontage.
Bicycle: free indoor parking
Public transport :
Tramway R1, Schoelcher station + 10 mn by foot through commercial center Atlantis
Tramway R1, François Mitterrand stop + bus 50, stop at Saulzaie station or bus 71, stop at the Zénith station
Tramway R3, Marcel Paul station + bus 50, Saulzaie station
Chronobus C6, Hermeland station+ bus 71, Zénith station
Bus : lignes 50 (Saulzaie station) or 71 (Zénith station)
Huawei Ascend comprises a series of AI processors engineered to deliver high-performance capabilities for both inference and training tasks.
This instructor-led live training, available online or on-site, is designed for intermediate-level AI engineers and data scientists aiming to develop and optimize neural network models utilizing Huawei’s Ascend platform alongside the CANN toolkit.
Upon completion of this training, participants will be equipped to:
Configure and establish the CANN development environment.
Create AI applications leveraging MindSpore and CloudMatrix workflows.
Enhance performance on Ascend NPUs through the use of tiling and custom operators.
Deploy models across cloud or edge environments.
Course Format
Interactive lectures and discussions.
Practical application of the Huawei Ascend and CANN toolkit within sample applications.
Guided exercises targeting model building, training, and deployment.
Course Customization Options
For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange.
Huawei’s AI stack, ranging from the low-level CANN SDK to the high-level MindSpore framework, provides a tightly integrated environment for AI development and deployment, optimized specifically for Ascend hardware.
This instructor-led live training, available either online or on-site, targets beginner to intermediate technical professionals seeking to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.
By the end of this training, participants will be able to:
Comprehend the layered architecture of Huawei’s AI compute stack.
Identify how CANN facilitates model optimization and hardware-level deployment.
Evaluate the MindSpore framework and toolchain in comparison to industry alternatives.
Position Huawei's AI stack within enterprise or cloud/on-premises environments.
Format of the Course also allows for the evaluation of participants.
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
To request customized training for this course, please contact us to arrange.
This instructor-led, live training in Nantes (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.
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led training, available both online and onsite, targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.
Upon completion of this training, participants will be able to:
Deploy and optimize CV and NLP models using CANN and AscendCL.
Leverage CANN tools to convert models and 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 scenarios.
Course Format
Interactive lectures and live demonstrations.
Practical labs focused on model deployment and performance profiling.
Live pipeline design utilizing real-world CV and NLP use cases.
Customization Options
For a customized version of this course, please reach out to us to arrange your training.
This instructor-led, live training in Nantes (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to learn the basics of GPU programming and the main frameworks and tools for developing GPU applications.
By the end of this training, participants will be able to: Understand the difference between CPU and GPU computing and 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 the advanced optimization and customization of AI model operators for Huawei Ascend hardware.
This instructor-led, live training session (available online or onsite) is designed for advanced system developers who aim to create, deploy, and fine-tune custom operators for AI models utilizing 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 using the TIK DSL for Ascend processors.
Integrating custom operations into the CANN runtime and execution graph.
Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
Debugging and optimizing instruction-level performance for specific 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.
Customization Options for the Course
To request a customized training version of this course, please contact us to make arrangements.
This instructor-led live training in Nantes (available online or onsite) targets beginner to intermediate developers who want to explore different GPU programming frameworks and assess their features, performance, and compatibility.
After this training, participants will be able to:
Configure a development environment that includes the OpenCL SDK, CUDA Toolkit, ROCm Platform, compatible hardware, and Visual Studio Code.
Build a basic GPU program for vector addition using OpenCL, CUDA, and ROCm, and analyze the differences in syntax, structure, and execution.
Use the respective APIs to query device details, manage memory allocation/deallocation, transfer data between host and device, launch kernels, and synchronize threads.
Write device-side kernels using the native languages of each framework to manipulate data.
Utilize framework-specific built-in functions, variables, and libraries for common tasks.
Optimize data transfers and memory access by using specific memory spaces (global, local, constant, private).
Control parallelism by managing threads, blocks, and grids via specific execution models.
Debug and test GPU programs using tools like CodeXL, CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight.
Improve performance through optimization techniques such as coalescing, caching, prefetching, and profiling.
CloudMatrix is Huawei’s comprehensive AI development and deployment platform designed to enable scalable, production-grade inference pipelines.
This instructor-led live training (available online or onsite) targets beginner to intermediate AI professionals seeking to deploy and monitor AI models using the CloudMatrix platform with integrated CANN and MindSpore support.
By the conclusion of this training, participants will be capable of:
Utilizing CloudMatrix for model packaging, deployment, and serving.
Converting and optimizing models specifically for Ascend chipsets.
Establishing pipelines for both real-time and batch inference tasks.
Monitoring deployments and tuning performance in production environments.
Course Format
Interactive lectures and discussions.
Hands-on practice with CloudMatrix using real-world deployment scenarios.
Guided exercises focusing on conversion, optimization, and scaling.
Course Customization Options
To request a customized training session tailored to your specific AI infrastructure or cloud environment, please contact us to arrange it.
Huawei's Ascend CANN toolkit empowers powerful AI inference on edge devices like the Ascend 310. CANN offers the necessary tools for compiling, optimizing, and deploying models in environments where computing power and memory are limited.
This instructor-led, live training (available online or onsite) is designed for intermediate AI developers and integrators who want to deploy and optimize 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.
Optimize model performance for environments with limited compute and memory.
Deploy and monitor AI applications in real-world edge scenarios.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and demonstration.
Hands-on lab work with 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 Nantes (online or onsite) is designed for beginner to intermediate developers who wish to install and use ROCm on Windows to program AMD GPUs and exploit their parallelism.
By the end of this training, participants will be able to:
Set up a development environment that includes ROCm Platform, 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 Nantes (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use ROCm and HIP to program AMD GPUs and exploit their parallelism.
By the end of this training, participants will be able to:
Set up a development environment that includes ROCm Platform, a AMD GPU, and Visual Studio Code.
Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from the GPU memory.
Use ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
Use HIP language to write kernels that execute on the GPU and manipulate data.
Use HIP built-in functions, variables, and libraries to perform common tasks and operations.
Use ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
Use ROCm and HIP execution models to control the threads, blocks, and grids that define the parallelism.
Debug and test ROCm and HIP programs using tools such as ROCm Debugger and ROCm Profiler.
Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.
CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.
This instructor-led live training (available online or onsite) targets beginner-level AI developers who want to understand how CANN integrates into the model lifecycle, from training through to deployment, and how it interacts with frameworks such as MindSpore, TensorFlow, and PyTorch.
By the end of this training, participants will be able to:
Understand the purpose and architecture of the CANN toolkit.
Set up a development environment using CANN and MindSpore.
Convert and deploy a simple AI model to Ascend hardware.
Gain foundational knowledge for future CANN optimization or integration projects.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Hands-on labs with simple model deployment.
Step-by-step walkthrough of the CANN toolchain and integration points.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ascend, Biren, and Cambricon represent the leading AI hardware platforms in China, each providing distinct acceleration and profiling capabilities for enterprise-scale AI workloads.
This instructor-led live training, available online or onsite, is designed for advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across these diverse Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
Benchmark models across Ascend, Biren, and Cambricon platforms.
Identify system bottlenecks and inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Tune deployment pipelines to enhance throughput and reduce latency.
Course Format
Interactive lectures and discussions.
Practical application of profiling and optimization tools on each respective platform.
Guided exercises centered on real-world tuning scenarios.
Customization Options
To request a customized version of this course tailored to your specific performance environment or model architecture, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, enabling developers to fine-tune and maximize the performance of neural networks deployed on Ascend AI processors.
This instructor-led live training, available either online or onsite, is designed for advanced AI developers and system engineers seeking to optimize inference performance through CANN’s sophisticated toolset, which includes the Graph Engine, TIK, and custom operator development capabilities.
Upon completion of this training, participants will be equipped to:
Grasp CANN's runtime architecture and its performance lifecycle.
Utilize profiling tools and the Graph Engine to analyze and optimize performance.
Develop and optimize custom operators using TIK and TVM.
Address memory bottlenecks and enhance model throughput.
Course Format
Interactive lectures coupled with discussions.
Practical labs featuring real-time profiling and operator tuning.
Optimization exercises based on edge-case deployment scenarios.
Customization Options
For personalized training arrangements for this course, please get in touch with us.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide alternatives to CUDA specifically designed for the domestic AI and high-performance computing (HPC) markets.
This instructor-led live training, available either online or onsite, targets advanced GPU developers and infrastructure specialists looking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be able 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.
Address practical challenges related to cross-architecture support and deployment.
Format of the Course also allows for the evaluation of participants.
Interactive lectures and discussions.
Hands-on labs for code translation and performance comparison.
Guided exercises focusing on multi-GPU adaptation strategies.
Course Customization Options
To request customized training tailored to your specific platform or CUDA project, please contact us to arrange it.
This instructor-led, live training in Nantes (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) represents Huawei's AI compute stack designed for the efficient deployment and optimization of AI models on Ascend AI processors.
This instructor-led, live training, available both online and onsite, targets intermediate-level AI developers and engineers aiming to deploy trained AI models efficiently on Huawei Ascend hardware. The course leverages the CANN toolkit alongside established tools such as MindSpore, TensorFlow, or PyTorch.
Upon completing this training, participants will be equipped to:
Grasp the CANN architecture and its critical function within the AI deployment pipeline.
Convert and adapt models from popular frameworks into formats compatible with Ascend.
Utilize tools such as ATC, OM model conversion, and MindSpore for both edge and cloud inference tasks.
Diagnose deployment challenges and optimize performance on Ascend hardware.
Course Format
Interactive lectures combined with live demonstrations.
Hands-on lab exercises utilizing CANN tools and Ascend simulators or devices.
Practical deployment scenarios grounded in real-world AI models.
Course Customization Options
To request a customized training session for this course, please contact us to arrange details.
Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize 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 use Biren’s programming model.
Translate and optimize CUDA-style code for Biren platforms.
Apply performance tuning and debugging techniques.
Format of the Course also allows for the evaluation of participants.
Interactive lecture and discussion.
Hands-on use of Biren SDK in sample GPU workloads.
Guided exercises focused on porting and performance tuning.
Course Customization Options
To request a customized training for this course based on your application stack or integration needs, please contact us to arrange.
Cambricon MLUs (Machine Learning Units) are specialized AI processors designed to optimize both inference and training workloads for edge computing and data center environments.
This instructor-led live training (available online or onsite) targets intermediate developers looking to build and deploy AI models utilizing the BANGPy framework and the Neuware SDK on Cambricon MLU hardware.
Upon completion of this course, participants will be able to:
Set up and configure development environments for BANGPy and Neuware.
Develop and optimize models written in Python and C++ for Cambricon MLUs.
Deploy models to edge and data center devices running the Neuware runtime.
Integrate machine learning workflows with MLU-specific acceleration capabilities.
Course Format
Interactive lectures and discussions.
Practical, hands-on experience with BANGPy and Neuware for development and deployment.
Guided exercises focusing on optimization, integration, and testing.
Customization Options
To arrange a customized version of this course tailored to your specific Cambricon device model or use case, please contact us.
This instructor-led, live training in Nantes (online or onsite) is designed for beginner-level system administrators and IT professionals who want to install, configure, manage, and troubleshoot CUDA environments.
By the end of this training, participants will be able to:
Comprehend the architecture, components, and capabilities of CUDA.
This instructor-led, live training in Nantes (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use OpenCL to program heterogeneous devices and exploit their parallelism.
By the end of this training, participants will be able to:
Set up a development environment that includes OpenCL SDK, a device that supports OpenCL, and Visual Studio Code.
Create a basic OpenCL program that performs vector addition on the device and retrieves the results from the device memory.
Use OpenCL API to query device information, create contexts, command queues, buffers, kernels, and events.
Use OpenCL C language to write kernels that execute on the device and manipulate data.
Use OpenCL built-in functions, extensions, and libraries to perform common tasks and operations.
Use OpenCL host and device memory models to optimize data transfers and memory accesses.
Use OpenCL execution model to control the work-items, work-groups, and ND-ranges.
Debug and test OpenCL programs using tools such as CodeXL, Intel VTune, and NVIDIA Nsight.
Optimize OpenCL programs using techniques such as vectorization, loop unrolling, local memory, and profiling.
This instructor-led, live training course in Nantes 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 optimization techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.
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Testimonials (1)
Trainers energy and humor.
Tadeusz Kaluba - Nokia Solutions and Networks Sp. z o.o.
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