GPU Training in Italy

Huawei Ascend comprises a family of AI processors engineered for high-performance inference and training tasks.

This instructor-led live training, available online or onsite, targets intermediate-level AI engineers and data scientists eager to develop and optimize 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.
• Create AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through custom operators and tiling techniques.
• Deploy models within either edge or cloud environments.

Course Format

• Interactive lectures and discussions.
• Practical application of Huawei Ascend and the CANN toolkit within sample projects.
• Guided exercises concentrating on model construction, training, and deployment.

Course Customization Options

• To arrange a tailored training session aligned with your specific infrastructure or datasets, please reach out to us.

Huawei’s AI stack—spanning from the low-level CANN SDK to the high-level MindSpore framework—provides a tightly integrated environment for AI development and deployment, specifically optimized for Ascend hardware.

This instructor-led live training (available online or onsite) targets technical professionals at the beginner to intermediate level who aim to grasp how CANN and MindSpore components collaborate to facilitate AI lifecycle management and infrastructure planning.

Upon completing this training, participants will be able to:

• Comprehend the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain against industry alternatives.
• Position Huawei's AI stack effectively within enterprise or cloud/on-premise environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs exploring the model workflow from MindSpore to CANN.

Customization Options

• To arrange customized training for this course, please contact us.

This instructor-led, live training in Italy (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 robust tools for deploying and optimizing real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available either online or on-site, is designed for intermediate-level AI professionals looking to construct, deploy, and refine vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize computer vision (CV) and NLP models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and embed them into live processing pipelines.
• Enhance inference performance for applications such as object detection, classification, and sentiment analysis.
• Create real-time CV/NLP pipelines suitable for both edge and cloud deployment scenarios.

Course Format

• Engaging lectures combined with practical demonstrations.
• Practical hands-on labs focused on model deployment and performance profiling.
• Real-time pipeline design using authentic CV and NLP use cases.

Customization Options

• For personalized training needs regarding this course, please reach out to us to arrange.

This instructor-led, live training in Italy (online or onsite) is designed for beginner to intermediate-level developers who want to learn the fundamentals of GPU programming as well as the primary 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, along with the benefits and challenges of GPU programming.
• Select 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 (available online or onsite) is designed for advanced-level system developers who want to build, deploy, and fine-tune custom operators for AI models using CANN’s TIK programming model and its integration with the TVM compiler.

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.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computation patterns.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Italy (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use different frameworks for GPU programming and compare their features, performance, and compatibility.

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

• Set up a development environment that includes OpenCL SDK, CUDA Toolkit, ROCm Platform, a device that supports OpenCL, CUDA, or ROCm, and Visual Studio Code.
• Create a basic GPU program that performs vector addition using OpenCL, CUDA, and ROCm, and compare the syntax, structure, and execution of each framework.
• Use the respective APIs to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use the respective languages to write kernels that execute on the device and manipulate data.
• Use the respective built-in functions, variables, and libraries to perform common tasks and operations.
• Use the respective memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses.
• Use the respective execution models to control the threads, blocks, and grids that define 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.

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

This instructor-led live training, available both online and onsite, targets beginner to intermediate AI professionals aiming to deploy and oversee AI models leveraging the CloudMatrix platform with CANN and MindSpore integration.

Upon completion of this training, participants will possess the ability to:

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

Course Format

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

Customization Options

• For tailored training based on your specific AI infrastructure or cloud environment, please contact us to arrange.

Huawei's Ascend CANN toolkit empowers developers to execute high-performance AI inference on edge hardware, including the Ascend 310. This toolkit offers critical capabilities for compiling, optimizing, and deploying models in environments where computational power and memory are limited.

This instructor-led live training, available either online or on-site, is designed for intermediate AI developers and integrators seeking to deploy and optimize models on Ascend edge devices using the CANN ecosystem.

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

• Prepare and convert AI models for the Ascend 310 using CANN utilities.
• Construct efficient inference pipelines utilizing MindSpore Lite and AscendCL.
• Enhance model performance tailored for constrained compute and memory resources.
• Deploy and oversee AI applications in practical edge scenarios.

Course Format

• Engaging lectures combined with live demonstrations.
• Practical laboratory sessions focusing on edge-specific models and use cases.
• Real-world deployment examples executed on virtual or physical edge hardware.

Customization Options

• To request a tailored training session for this course, please reach out to us for arrangements.

This instructor-led, live training in Italy (online or onsite) is aimed at 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 Italy (online or onsite) targets beginner to intermediate 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) 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 seeking to comprehend how CANN integrates into the model lifecycle—from training through to deployment—and how it interoperates with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completion of this training, participants will be capable of:

• Grasping the purpose and architectural design of the CANN toolkit.
• Establishing a development environment utilizing CANN and MindSpore.
• Converting and deploying a simple AI model to Ascend hardware.
• Acquiring foundational knowledge to support future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical hands-on labs focused on simple model deployment.
• A step-by-step walkthrough of the CANN toolchain and its integration points.

Customization Options

• To request customized training for this course, please reach out to us to arrange details.

Ascend, Biren, and Cambricon stand out as premier AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale AI production workloads.

This instructor-led live training, available online or onsite, targets advanced AI infrastructure and performance engineers seeking to optimize model inference and training processes across various Chinese AI chip architectures.

Upon completing this training, participants will be equipped to:

• Conduct benchmarks on Ascend, Biren, and Cambricon platforms.
• Diagnose system bottlenecks and identify inefficiencies in memory and compute resources.
• Implement optimizations at the graph, kernel, and operator levels.
• Optimize deployment pipelines to enhance both throughput and reduce latency.

Course Format

• Interactive lectures and group discussions.
• Practical application of profiling and optimization tools specific to each platform.
• Guided exercises centered on real-world tuning scenarios.

Customization Options

• To arrange a customized training session tailored to your specific performance environment or model type, please reach out to us directly.

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

This instructor-led live training, available online or onsite, targets advanced AI developers and system engineers seeking to maximize inference performance by leveraging CANN’s advanced tools, such as the Graph Engine, TIK, and custom operator development capabilities.

Upon completion of this training, participants will be capable of:

• Gaining insight into CANN's runtime architecture and performance lifecycle.
• Utilizing profiling tools and the Graph Engine to analyze and optimize performance.
• Developing and optimizing custom operators using TIK and TVM.
• Addressing memory bottlenecks and increasing model throughput.

Course Format

• Interactive lectures and discussions.
• Practical labs involving real-time profiling and operator tuning.
• Optimization exercises based on real-world edge-case deployment scenarios.

Customization Options

• For customized training arrangements, please contact us directly.

Domestic GPU architectures in China, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and High-Performance Computing (HPC) sectors.

This guided, live training session (available online or on-site) is designed for advanced GPU developers and infrastructure experts looking to migrate and optimize their existing CUDA applications for Chinese hardware platforms.

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

• Assess the compatibility of current CUDA workloads with Chinese chip alternatives.
• Transfer CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Evaluate performance metrics and identify optimization opportunities across different platforms.
• Resolve practical challenges related to cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Practical labs involving code translation and performance benchmarking.
• Instructor-led exercises focused on multi-GPU adaptation strategies.

Customization Options

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

This instructor-led, live training in Italy (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, a 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 CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Use 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 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.

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CANN (Compute Architecture for Neural Networks) serves as Huawei's AI compute stack, designed to deploy and optimize AI models on Ascend AI processors.

This instructor-led live training, available both online and on-site, targets intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be capable of:

• Understanding the CANN architecture and its function within the AI deployment pipeline.
• Converting and adapting models from popular frameworks into Ascend-compatible formats.
• Utilizing tools like ATC, OM model conversion, and MindSpore for inference on both edge and cloud environments.
• Diagnosing deployment issues and optimizing performance on Ascend hardware.

Course Format

• Interactive lectures and demonstrations.
• Hands-on lab exercises 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, supporting large-scale training and inference.

This instructor-led live training (available online or onsite) targets intermediate to advanced developers looking 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:

• Comprehend the Biren GPU architecture and 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 techniques.

Course Format

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

Course Customization Options

• For customized training tailored to your application stack or integration needs, please contact us to arrange.

Cambricon MLUs (Machine Learning Units) are specialized artificial intelligence chips designed to optimize both inference and training processes for edge computing and data center environments.

This instructor-led live training, available either online or on-site, is designed for intermediate-level developers aiming to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Establish and configure development environments for both BANGPy and Neuware.
• Create and optimize models based on Python and C++ specifically for Cambricon MLUs.
• Deploy models to edge devices and data centers operating on the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

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

Customization Options

• To arrange customized training tailored to your specific Cambricon device model or use case, please contact us.

This instructor-led, live training in Italy (online or onsite) targets beginner-level system administrators and IT professionals who aim to install, configure, manage, and troubleshoot CUDA environments.

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

• Grasp the architecture, components, and capabilities of CUDA.
• Install and configure CUDA environments.
• Manage and optimize CUDA resources.
• Debug and resolve common CUDA issues.

This instructor-led, live training in Italy (online or onsite) targets beginner to intermediate developers who want to utilize OpenCL for programming heterogeneous devices and harnessing their parallel processing power.

Upon completing this training, participants will be capable of:

• Establishing a development environment comprising the OpenCL SDK, a compatible device, and Visual Studio Code.
• Developing a fundamental OpenCL program that executes vector addition on the device and retrieves results from device memory.
• Utilizing the OpenCL API to query device details, and to create contexts, command queues, buffers, kernels, and events.
• Writing OpenCL C kernels that run on the device and manipulate data.
• Applying OpenCL built-in functions, extensions, and libraries for common tasks and operations.
• Leveraging OpenCL host and device memory models to optimize data transfers and memory access patterns.
• Controlling work-items, work-groups, and ND-ranges using the OpenCL execution model.
• Debugging and testing OpenCL programs with tools like CodeXL, Intel VTune, and NVIDIA Nsight.
• Optimizing OpenCL programs through techniques such as vectorization, loop unrolling, local memory usage, and profiling.

This instructor-led, live training course in Italy explores how to program GPUs for parallel computing, use various platforms, work with the CUDA platform and its features, and perform various optimization techniques using CUDA. Key application areas include deep learning, analytics, image processing, and engineering.