Python & Linux for AI Operations
This course covers the Linux command-line environment and Python scripting skills an AI operations technician needs to support GPU cluster work, without assuming prior programming experience. Students work in a Linux shell to manage processes, file systems, permissions, and services; write shell scripts to automate routine operational tasks; and write Python scripts that call system commands, parse logs, and interact with REST APIs. The course introduces the NVIDIA driver and CUDA toolkit stack at the level an operations technician must recognize (nvidia-smi output, driver/CUDA version compatibility, GPU process listing) without requiring model-development skill. Students configure SSH key-based access, use tmux/screen for persistent remote sessions, and practice reading and filtering system and application logs with standard command-line tools. The course is a direct prerequisite for GPU Cluster Bring-Up & Commissioning and every subsequent AIO course, and is where students first build the command-line fluency that GPU cluster diagnostic work assumes.
What you'll be able to do
- Navigate and manage a Linux file system using command-line tools including permissions and ownership changes.
- Write a shell script to automate a recurring operational task such as log rotation or disk-usage reporting.
- Configure SSH key-based authentication to a remote Linux host and disable password authentication. Safety-critical
- Interpret nvidia-smi output to identify GPU utilization, memory usage, temperature, and running processes.
- Verify NVIDIA driver and CUDA toolkit version compatibility before a workload deployment.
- Write a Python script that parses a system log file and extracts error events matching a pattern.
- Write a Python script that calls a REST API and handles a non-200 response.
- Maintain a persistent remote session using tmux or screen across an SSH disconnect.
- Diagnose a failed system service using systemctl status and journalctl log output.
- Use Python virtual environments and package managers to isolate project dependencies.
Train the team that runs the factory.
The Institute travels with every SAVRN campus.