東京のコンピュータグラフィックストレーニング

OpenACC is an open standard for heterogeneous programming that enables a code to run on different platforms and devices, such as multicore CPUs, GPUs, FPGAs, and others. This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

GPU programming is a technique that leverages the parallel processing power of GPUs to accelerate applications that require high-performance computing, such as artificial intelligence, gaming, graphics, and scientific computing. There are several frameworks and tools that enable GPU programming, each with its own advantages and disadvantages. Some of the most popular ones are OpenCL, CUDA, ROCm, and HIP.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

GPU programming is a technique that leverages the parallel processing power of GPUs to accelerate applications that require high-performance computing, such as artificial intelligence, gaming, graphics, and scientific computing. There are several frameworks that enable GPU programming, each with its own advantages and disadvantages. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. ROCm is a platform that supports GPU programming on AMD GPUs, and also provides compatibility with CUDA and OpenCL.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

ROCm is an open source platform for GPU programming that supports AMD GPUs, and also provides compatibility with CUDA and OpenCL. ROCm exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.ROCm for Windows is a recent development that allows users to install and use ROCm on Windows operating system, which is widely used for personal and professional purposes. ROCm for Windows enables users to leverage the power of AMD GPUs for various applications, such as artificial intelligence, gaming, graphics, and scientific computing.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

ROCm is an open source platform for GPU programming that supports AMD GPUs, and also provides compatibility with CUDA and OpenCL. ROCm exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.HIP is a C++ runtime API and kernel language that allows you to write portable code that can run on both AMD and NVIDIA GPUs. HIP provides a thin abstraction layer over the native GPU APIs, such as ROCm and CUDA, and allows you to leverage the existing GPU libraries and tools.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

CUDA is an open standard for GPU programming that enables a code to run on NVIDIA GPUs, which are widely used for high-performance computing, artificial intelligence (AI), gaming, and graphics. CUDA exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

OpenSCAD is an open-source, script-based application for modeling solid 3D CAD (computer-aided design) objects. This instructor-led, live training (online or onsite) is aimed at designers, mechanical engineers, and anyone who wishes to learn how to create 3D models that can be constructed or 3D printed. By the end of this training, participants will be able to:

• Create printable 3D models.
• Use geometric transformations to build complex 3D shapes.
• Model in a non-interactive manner using minimal lines of code.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

OpenCL is an open standard for heterogeneous programming that enables a code to run on different platforms and devices, such as multicore CPUs, GPUs, FPGAs, and others. OpenCL exposes the programmer to the hardware details and gives full control over the parallelization process. However, this also requires a good understanding of the device architecture, memory model, execution model, and optimization techniques.This instructor-led, live training (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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface model created by Nvidia.This instructor-led, live training (online or onsite) is aimed at beginner-level system administrators and IT professionals who wish to install, configure, manage, and troubleshoot CUDA environments.By the end of this training, participants will be able to:

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

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

What you will learn during the training:

• principles of creating computer graphics and desktop publishing
• ways of defining and working with color
• differences between vector and bitmap graphics
• ways to customize color photos and graphics
• principles of retouching and creating photomontages
• create your own illustrations and graphics
• entrusted to adapt to the needs of graphic material composition and printing
• how to do logo and logos
• create interesting charts and tables
• create business cards and letterhead
• creating labels, diplomas, invitations
• preparation of leaflets
• how to format text
• use of spot colors
• principles of preparing to print
• digital printing, offset printing, screen printing

Sample topics of classes:

• my poster
• portrait
• expanse
• my catalog
• my face
• billboard
• my logo

Adobe LiveCycle Designer is a software tool that allows users to create and edit PDF forms that can be filled out electronically or printed. Adobe LiveCycle Designer enables users to add various elements to PDF forms, such as text fields, buttons, checkboxes, lists, tables, images, and scripts. Adobe LiveCycle Designer also allows users to control the layout, appearance, validation, and logic of PDF forms, as well as integrate them with data sources and web services. This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers and UI/UX designers who wish to use Adobe LiveCycle Designer to create interactive and dynamic PDF forms. By the end of this training, participants will be able to:

• Create and edit PDF forms with various elements and properties.
• Add scripts and logic to PDF forms using JavaScript.
• Validate and secure PDF forms.
• Integrate PDF forms with data sources and web services.
• Deploy and distribute PDF forms.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

For those who want to start with new software as well as who already know other software for vector graphics and want to quickly start working with new software.

The participant will learn during the training:

• use a vector graphics program.
• create simple objects
• creating advanced curves
• create a logo
• tracing elements created on paper or photograph
• receipt of leaflets
• advertising posters
• open and modify documents and PDF vector, EPS

During the 3-day training, participants will learn a bit of history of theory and guidelines converted into example projects: "Scribing" from a drawing on paper to a vector image that can be used for printing, cutting, engraving, etc. "Pierre Bézier curves" rules for creating basic curves and complex curves as well as editing and repairing. "Typography" creating your own font from drawing to obtaining an open type font (OTF) file.

This course demonstrates through hands-on practice the fundamentals and applications of Inkscape.

You will learn:

• to create computer graphics
• how to make photos look better
• the rules of retouching and creating photomontages
• how to prepare logos, charts, tables and illustrations
• ho to prepare business cards, simple advertising, billboard and leaflets
• basis of preparation your graphics for print and Web applications

Sample topics:

• my poster
• portrait
• space
• my catalog
• my face
• billboard
• my logo

ARToolKit is an open-source tracking library for creating Augmented Reality (AR) applications. It supports Windows, Linux, Mac, Android and iOS. In this instructor-led, live training, participants will learn how to build an augmented reality mobile application with ARToolKit. By the end of this training, participants will be able to:

• Develop a mobile application that overlays virtual imagery onto the real world
• Preprocess video to adjust lighting and resolve issues
• Integrate ARToolKit with third-party tools and functionality, including OpenCV, Unity, OpenSceneGraph and the mobile device's GPS, compass, and camera calibration utilities

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• The mobile application developed in this course can be target Android and/or iOS. To request your preference, please contact us to arrange.

Blender is an open-source 3D computer graphics software tool set for creating animated films, visual effects, art, 3D printed models, interactive 3D applications and video games.This instructor-led, live training (online or onsite) is aimed at artists, game developers, or complete beginners who wish to use Blender to create 3D models for interactive applications, video games, animated films, etc.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.
• Learn how to create animations and visual effects with Blender.
• Add curves, surfaces, metaballs, and hair particles to simulate realistic 3D motions.
• Use the tools for UV mapping/unwrapping, sculpting, and painting 3D models.
• Export 3D models and assets to a game engine, 3D printer, or other software. 

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

FreeCAD is an open-source, customizable, multi-platform, scriptable and extensible parametric 3D modeler for designing real-life objects. In this instructor-led, live training, participants will learn how to model an object for later construction or 3D printing. By the end of this training, participants will be able to:

• Use FreeCad's tools and workflows to replace commercial CAD or BIM modeling software
• Write to many formats such as STEP, IGES, STL, SVG, DXF, OBJ, IFC, DAE, etc.
• Use Python to extend FreeCAD's functionality
• Automate FreeCad, build modules and embed FreeCAD into an existing application

Audience

• Designers
• Developers
• Mechanical engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Universal Scene Description (USD) is an open source framework developed by Pixar for exchanging 3D computer graphics data across different applications. It is designed to be flexible and extensible to other collaborative environments that run interconnected tools, such as NVIDIA Omniverse. This instructor-led, live training (online or onsite) is aimed at design engineers, architects, and developers who wish to use Pixar USD to create, build, and share common 3D scenes or assets while working simultaneously from different graphics tools. By the end of this training, participants will be able to:

• Set up the necessary development environment to start building 3D graphics and workflows.
• Understand the basic USD composition and apply modifications to enhance 3D assets and scenes.     
• Share, combine, and transform multiple assets using the supported USD plugins for 3rd party applications (Maya, Katana, and Houdini).

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• This course is focused on USD, but other software applications such as Maya, Katana, and Houdini may also be used. Please confirm if you have specific requirements.
• To request a customized training for this course, please contact us to arrange.

This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.

This instructor-led, live training course 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.

Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc. This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data. By the end of this training, participants will be able to:

• Install and configure the necessary development environment, software and libraries to begin developing.
• Build, train, and deploy deep learning models to analyze live video feeds.
• Identify, track, segment and predict different objects within video frames.
• Optimize object detection and tracking models.
• Deploy an intelligent video analytics (IVA) application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

WebGL (Web Graphics Library) is a JavaScript API for rendering 3D graphics within a web browser without the use of plug-ins. In this instructor-led, live training, participants will learn how to generate realistic computer images using 3D graphics as they step through the creation of an animated 3D application that runs in a browser. By the end of this training, participants will be able to:

• Understand and use WebGL's various functionality, including meshes, transforms, cameras, materials, lighting, and animation
• Animate objects with WebGL
• Create 3D objects using WebGL

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Autodesk Maya or Maya is a 3D computer graphics application. Maya allows users to create realistic animations from scratch. This instructor-led, live training (online or onsite) is aimed at web designers who wish to use Maya for creating 3D animations. By the end of this training, participants will be able to:

• Create realistic models and textures in Maya.
• Animate and render projects for high quality playback.
• Simulate natural effects like water and smoke.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

CUDA (Compute Unified Device Architecture) is a parallel computing platform and API created by Nvidia. This instructor-led, live training (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs. By the end of this training, participants will be able to:

• Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
• Create, compile and launch custom CUDA kernels.
• Manage GPU memory.
• Convert a CPU based application into a GPU-accelerated application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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