Language :
Leadtek RTX AI Software Pack is a toolkit that integrates all the analysis components used for AI projects. It has pre-installed a complete development environment, including operating system, GPU drivers, CUDA toolkit, deep learning libraries, and various deep learning and machine learning frameworks.
Leadtek RTX AI Software Pack is a toolkit that integrates all the analysis components used for AI projects. It has pre-installed a complete development environment, including operating system, GPU drivers, CUDA toolkit, deep learning libraries, and various deep learning and machine learning frameworks. All kinds of AI research projects and big data development tasks can be started immediately after system booting. GPU workstations and servers are optimized for hardware and software, eliminating the cumbersome installation process. When combined with GDMS, it brings the benefits of managing AI projects and GPU resources more effectively.
After verification and startup, the operating system, GPU driver, CUDA toolkit, cuDNN, NCCL, nvidia-docker package, and NVIDIA DCGM will be automatically installed. It avoids the cumbersome installation and debugging process in GPU and AI project environment, thus allowing researchers to quickly start their development projects.
In case of any problems, the restore function will restore the system back to its factory default state, thereby reducing the time required for the system to inspect and reinstall the software.
Docker engine is one of the most popular development platforms in the world today, featuring easy installation, complete portability, and simple maintenance. Through the nvidia-docker plug-in, Docker can now support the GPU development environment, and reduce the system burden and version conflicts of dependent libraries that easily happen when the development environment is installed on the host machine.
The software is pre-loaded with the most popular deep learning and machine learning frameworks, including AI development frameworks optimized by NVIDIA NGC. The software also provides a framework test window to facilitate your verification efforts of the development environment.
The software can monitor the execution and health status of the GPU and GPU memory, and can export monitoring records for archiving. It allows you to clearly understand the GPU hardware resources and GPU states during execution.
By becoming a GPU system managed by GDMS, you can manage, allocate, and utilize GPU resources through GDMS more effectively. The integrated solution is ideal for enterprise AI project management, smart classroom applications, and research teams.
AI Deep Learning Solution
Learn More
| Specification | Description | |
| Recommended System Requirements | RTX AI Software requires minimum specifications as below and the performance depends on the actual workload and hardware resources available:
|
|
| RTX AI Utility | RTX AI Software Operation Mode | Allows software engineers, data scientists and AI experts to operate workstations and servers either locally or remotely:
|
| Framework Diagnostics | Perform the selected DL/ML framework and run it with a test model, in order to verify if it is valid and feasible to use | |
| GPU Monitoring/Log | Instantly monitor health status of GPU and GPU memory and record the log | |
| GPU Test Tool | A CUDA test tool for testing GPU’s hardware status in containers | |
| Jupyter Notebook | An integrated, web-based interactive development tool to write or iterate on source codes in Docker containers | |
| Software Component Version | A handy tool for viewing each current software version by predefining the system commands used | |
| GDMS Server Support | Embed GDMS agent to support GPU resource allocation managed by GDMS administrators. This function is only valid when deployed with GDMS server | |
| System Recovery | Restore the system back to its factory default state in one click | |
| Rescue Disk | When system is unable to boot up, the tool will rebuild the boot drive and restore the system back to its factory default state | |
| RTX AI Framework | Chainer、TensorFlow、PyTorch、NVIDIA DIGITS、NVIDIA CUDA、NVIDIA Caffe、NVIDIA MxNet、NVIDIA PyTorch、NVIDIA TensorFlow、NVIDIA TensorRT、NVIDIA TensorRT Server、NVIDIA RAPIDS | |
| RTX AI Base Component | Ubuntu Desktop OS v18.04 LTS、NVIDIA CUDA、NVIDIA cuDNN、NVIDIA NCCL、NVIDIA Docker、Docker Engine、NVIDIA DCGM Fabric Manager 、NVIDIA Drivers | |
| Supported Language(UI and Keyboard Layout) | English and Japanese (English only for RTX AI Utility) | |
| RTX AI Software Pack | |||
|---|---|---|---|
| Language | Version | Description |  |
| User's Manual | |||
| Multilanguage | 2.0 ( 2021/9/23 ) | English, Traditional Chinese, Simlified Chinese, Japanese Total size: [ 33149 KB ] |  |
Future
