⚠️ GPU Not Utilized – System Defaulting to CPU Execution
Hey team,
We’ve identified a critical issue: the system is running deep learning workloads on CPU instead of GPU. After digging into it, the root causes include:
TensorFlow and CUDA version mismatch
Missing or incompatible cuDNN libraries
Unset or misconfigured environment variables (e.g. LD_LIBRARY_PATH, CUDA_HOME)
No logic in code to detect and use available GPUs
This results in massive performance degradation—training times are 50x+ slower.
I’ve attached a detailed report with the full diagnosis and the fix. Please review and ensure this setup is applied across relevant systems to avoid wasting compute time.
Let me know once it’s handled.
We’ve identified a critical issue: the system is running deep learning workloads on CPU instead of GPU. After digging into it, the root causes include:
TensorFlow and CUDA version mismatch
Missing or incompatible cuDNN libraries
Unset or misconfigured environment variables (e.g. LD_LIBRARY_PATH, CUDA_HOME)
No logic in code to detect and use available GPUs
This results in massive performance degradation—training times are 50x+ slower.
I’ve attached a detailed report with the full diagnosis and the fix. Please review and ensure this setup is applied across relevant systems to avoid wasting compute time.
Let me know once it’s handled.
TECHNICAL_REPORT.md8.75KB
