When I recreate and reassign a JAX np array to the same variable name, for some reason the GPU memory nearly doubles the first recreation and then stays stable for subsequent recreations/reassignments. Why does this happen and is this generally expected behavior for JAX arrays? Fully runnable minimal example: https://colab.research.google.com/drive/1piUvyVylRBKm1xb1WsocsSVXJzvn5bdI?usp=sharing. For posterity in case colab goes down: Thank you! Answer
Tag: nvidia
CUDA Error: out of memory – Python process utilizes all GPU memory
Even after rebooting the machine, there is >95% of GPU Memory used by python3 process (system-wide interpreter). Note that memory consumption keeps even if there are no running training scripts, and I’ve never used keras/tensorflow in the system environment, only with venv or in docker container. UPDATED: The last activity was the execution of NN test script with the following
How do I check if PyTorch is using the GPU?
How do I check if PyTorch is using the GPU? The nvidia-smi command can detect GPU activity, but I want to check it directly from inside a Python script. Answer These functions should help: This tells us: CUDA is available and can be used by one device. Device 0 refers to the GPU GeForce GTX 950M, and it is currently