Here is a small guide I wrote while building my lab's AI workstation @ DigiLab, LSBG and relevant information I learned while building the system are documented. I will cover 5 topics in this blog and here is an overview diagram of the workstation:
- AI/ ML / DL Workstation Build
- Robotics, Electronics and Hardware
- AI Accelerators
- Benchmarking GPU Performance
- Benchmarking Standard DL tasks
- Essence of Computation & Networking NOTES
- AI / ML / DL hardware core components NOTES
- Parallel Programming with CUDA NOTES
- Final AI Work Station build NOTES
- Electronics components & workbench NOTES
- Robotics Components NOTES
- Robotics and Mechatronics NOTES
- IoT & AI on Edge devices NOTES
- 3D printing & PCB Design NOTES
- DIY & Advanced Robotics hardware NOTES
- Robot Operating System [ROS] NOTES
- Autonomous Systems NOTES
To know more about linux : you might find useful content in my repository/@linux_d
- 3D Mark Benchmark [x report] as no test suite for linux but windows
- Phoronix test suite report ✅✅
- Paessler Network benchmark link only windows sys (for linux - phoronix/ethr or phoronix/speedtest-cli)
- LAMMPS benckmark link ( classical molecular dynamics in phoronix )
- VASP benchmark link ( atomic scale molecular modelling in phoronix)
- V-Ray 5 benchmark link ( render latency benchmark in phoronix)
To know more about AI : you might find useful content in my repository/@aicore_n
- AI environment setup with CUDA, cudnn and python3 in linux report
- Stanford DAWN Deep Learning Benchmark [x report] - retired benchmark and upgraded to mlperf
- MLPerf training benchmarks report
- AI-Benchmark Alpha / Burnout report
@lab :
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