CUDA Administration Training by AcadNXT is designed to provide participants with practical expertise in managing, configuring, monitoring, and optimizing CUDA-enabled GPU environments in enterprise and high-performance computing (HPC) systems. This instructor-led training program covers NVIDIA GPU architecture, CUDA runtime environment management, driver installation, toolkit configuration, multi-GPU system administration, performance monitoring, resource allocation, troubleshooting, and security considerations. Participants will gain hands-on experience in administering GPU infrastructure for AI, machine learning, scientific computing, and large-scale data processing workloads. The course is ideal for system administrators, DevOps engineers, HPC engineers, and IT professionals responsible for managing GPU-enabled infrastructure.

• Understand CUDA ecosystem and GPU infrastructure management
• Install and configure NVIDIA drivers and CUDA toolkits
• Monitor GPU performance and system utilization effectively
• Manage multi-GPU environments and workloads
• Troubleshoot CUDA runtime and driver issues
• Optimize system performance for GPU workloads
• Apply Linux administration skills in GPU environments
• Maintain stable and scalable CUDA infrastructure

• System Administrators
• DevOps Engineers
• HPC (High Performance Computing) Engineers
• Cloud Infrastructure Engineers
• AI Infrastructure Engineers
• Platform Engineers
• IT Support Engineers
• Data Center Operations Teams

• Basic understanding of Linux/Unix administration
• Familiarity with command-line operations
• Knowledge of computer hardware fundamentals is beneficial
• Understanding of basic networking and system monitoring concepts

• Understanding CUDA ecosystem and GPU infrastructure architecture
• Installing and configuring NVIDIA drivers and CUDA toolkits
• Managing multi-GPU and multi-node environments
• Monitoring GPU performance and utilization effectively
• Troubleshooting CUDA runtime and driver issues
• Optimizing system resources for GPU workloads
• Managing CUDA-compatible application environments
• Applying security and access control in GPU systems
• Configuring workload distribution across GPU clusters
• Maintaining stable and high-performance CUDA infrastructure

After completing the CUDA Administration Training, participants will be able to install, configure, manage, and optimize CUDA-enabled GPU environments in enterprise and HPC systems. Learners will gain practical expertise in GPU system administration, performance monitoring, driver management, troubleshooting, and multi-GPU infrastructure optimization.

Introduction to CUDA Infrastructure and GPU Systems

• Overview of CUDA ecosystem and GPU computing
• Understanding NVIDIA GPU architecture in systems
• CPU vs GPU workload distribution concepts
• CUDA runtime environment overview
• System requirements for CUDA deployment

Installation and Configuration

• Installing NVIDIA drivers
• Setting up CUDA toolkit environment
• Configuring environment variables
• Verifying CUDA installation
• Managing compatibility between drivers and toolkit

GPU System Management

• Monitoring GPU devices using system tools
• Understanding GPU memory and utilization metrics
• Multi-GPU system configuration basics
• Device visibility and control settings
• Basic system health checks

Linux Administration for CUDA Systems

• Managing GPU servers in Linux environments
• Process management for GPU workloads
• System logging and diagnostics
• Resource allocation techniques
• User access and permissions management

Performance Monitoring and Optimization

• Monitoring GPU usage with NVIDIA tools
• Identifying performance bottlenecks
• Optimizing GPU workload distribution
• Memory utilization tuning techniques
• System performance benchmarking basics

Troubleshooting and Debugging CUDA Environments

• Diagnosing driver and runtime issues
• Resolving CUDA compatibility problems
• Handling GPU memory errors
• Debugging application execution issues
• Log analysis and system recovery

Multi-GPU and Cluster Management

• Managing multi-GPU servers
• Load balancing across GPUs
• Introduction to GPU clusters
• Resource scheduling concepts
• Scalability considerations in GPU systems

Mini Project and Practical Implementation

• Setting up a CUDA-enabled system environment
• Configuring multi-GPU monitoring setup
• Troubleshooting real-world CUDA issues
• Optimizing system performance for workloads
• Final system administration review

• CUDA system architecture and GPU management
• Driver and toolkit installation procedures
• GPU monitoring and performance analysis
• Multi-GPU configuration and workload distribution
• Troubleshooting CUDA runtime issues
• Linux administration for GPU systems
• Performance optimization techniques
• CUDA infrastructure mini project implementation

• Hands-on CUDA environment setup exercises
• GPU monitoring and administration tasks
• Driver installation and configuration assignments
• Troubleshooting and debugging activities
• Mini project development and evaluation
• Interactive system administration discussions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in CUDA Administration Training, validating their expertise in CUDA environment management, GPU system administration, performance monitoring, driver configuration, troubleshooting, and enterprise GPU infrastructure operations.