Computer Graphics Training in Sud-Vest

This instructor-led live training (online or onsite) targets beginner to intermediate developers who wish to employ OpenACC for programming heterogeneous devices and leveraging 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.

This instructor-led, live training in Sud-Vest (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.

This instructor-led, live training in Sud-Vest (online or onsite) targets beginner to intermediate developers who wish to utilize 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.

This instructor-led live training in Sud-Vest (online or on-site) is designed for artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, and other projects.

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

• Learn how to create animations and visual effects with Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D motions.
• Introduction to non-destructive modelling and animation.
• Export 3D models and assets to a game engine, 3D printer, or other software.

This instructor-led live training in Sud-Vest (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 the ROCm Platform, an AMD GPU, and Visual Studio Code on Windows.
• Create a basic ROCm program that performs vector addition on the GPU and retrieves results from GPU memory.
• Use the ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
• Write kernels that execute on the GPU and manipulate data using the HIP language.
• Utilize HIP built-in functions, variables, and libraries to perform common tasks and operations.
• Leverage ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
• Use ROCm and HIP execution models to control the threads, blocks, and grids that define 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 Sud-Vest (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.

This instructor-led live training in Sud-Vest (online or onsite) targets artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, and other media.

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

• Understand the principles and core concepts of 3D modeling.
• Explore a variety of modes and tools for modeling and editing 3D meshes.
• Use tools for UV mapping/unwrapping, sculpting, and painting 3D models rendering.

This instructor-led live training, offered Sud-Vest (online or onsite), is targeted at beginner to intermediate graphic designers and animators who wish to learn how to craft stunning animations, interactive media, and engaging web content using Adobe Animate.

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

• Navigate the Adobe Animate interface and tools.
• Create and edit animations using keyframes, motion tweens, and shape tweens.
• Design interactive animations and applications with ActionScript and JavaScript.
• Incorporate audio and video elements into projects.
• Export animations for web, video, and mobile platforms.

This instructor-led, live training in Sud-Vest (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.

97% de clients satisfaits. 

This instructor-led, live training in Sud-Vest (online or onsite) is designed for artists, game developers, or complete beginners who want to use Blender to create 3D models for interactive applications, video games, animated films, and more.

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

• Grasp the principles and core concepts of 3D modeling.
• Explore various modes and tools for modeling and editing 3D meshes.
• Learn how to create animations and visual effects using Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D movements.
• Utilize tools for UV mapping/unwrapping, sculpting, and painting 3D models.
• Export 3D models and assets to game engines, 3D printers, or other software.

This instructor-led, live training in Sud-Vest (online or onsite) is designed for beginner-level system administrators and IT professionals who wish 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 troubleshoot common CUDA issues.

This instructor-led, live training in Sud-Vest (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 in Sud-Vest (online or onsite) is aimed at beginner-level to advanced-level 3D design and 3D printing enthusiasts who wish to use Fusion 360 to design, simulate, and prepare models for 3D printing.

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

• Install and configure Fusion 360 for optimal performance.
• Design, model, and simulate 3D objects in a unified environment.
• Optimize and prepare designs for the 3D printing process.
• Collaborate and share their designs using Fusion 360's cloud capabilities.

This instructor-led, live training in Sud-Vest (online or onsite) is designed for C++ developers who want to use CUDA to accelerate applications, write high-performance GPU kernels, and leverage parallel algorithm libraries for scientific computing, data processing, and machine learning workloads.

This instructor-led, live training in Sud-Vest (online or onsite) is designed for C/C++ developers who aim to use CUDA to accelerate compute-intensive applications, encompassing data processing, scientific simulations, machine learning tasks, and image processing pipelines.

This instructor-led, live training in Sud-Vest (online or onsite) is designed for software developers, data analysts, and technical professionals who intend to use TensorFlow 2.x and Keras to develop, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

In this instructor-led, live training course in Sud-Vest, you will learn how to program GPUs for parallel computing, how to use different 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.