Running the CUDA CNN model training on a CPU instead of a GPU on your laptop.
Running the CUDA CNN model training on a CPU instead of a GPU on your laptop.
During the process of training a CNN model with TensorFlow/Keras, the system operates on the CPU rather than the GPU, resulting in excessive CPU consumption and extended training durations. This problem continues even when a suitable GPU is present. Environment Details: Framework – TensorFlow GPU Hardware – Intel i5-13420H Operating System – Windows 11 Python Version – Tested on both 3.10.5 and 3.12 Versions Installed – CUDA and cuDNN properly configured Expected Outcome – Model training should utilize GPU to boost performance and keep CPU usage low. With GPU support, training speed should improve while CPU load stays minimal. Solutions Tried: Reinstalled TensorFlow with GPU support (tensorflow-gpu). Verified CUDA and cuDNN compatibility with the chosen TensorFlow version. Adjusted environment variables via Anaconda for Python 3.10.5 and repeated steps. Explored installing WSL, assuming it might be necessary if compatibility with Windows wasn't guaranteed. Technical Limitations: Maintaining compatibility between TensorFlow, CUDA, and cuDNN versions. Identifying any CPU-only dependencies that could override GPU configurations.
I installed Torch and tested various command options, but I couldn't locate settings like device selection. Could you clarify where to look next?
Run the script to view available devices. It displays the hardware your PyTorch instance can use. Make sure to adjust the formatting yourself.
Checked the script but no output appeared. Ran print statements to verify PyTorch version and CUDA availability. Result showed 2.2.1 on CPU with no GPU support. Possible issue may be environment setup or version mismatch.
I'm not sure how it happened. Just re-installed it twice. Updated system settings and added three new paths (x64, bin, lib).