I recently purchased a NVIDIA Jetson Nano developer kit (4GB LPDDR4 Memory) and am trying to train a model on my host machine using Ubuntu 20.04 to later run on my nano.
The host machine has the following specifications:
CPU: Intel Core i7-7700HQ CPU @ 2.80GHz x 8
Memory: 32GB RAM
GPU: NVIDIA GeForce GTX 1050 w/ 4GB GDDR5 Graphics memory
When I began training on my host machine, tensorflow-gpu does not recognize my gpu and proceeds to train my model using my cpu instead. I attempted to fix this issue by following the instructions on the tensorflow website (https://www.tensorflow.org/install/gpu).
I downgraded my OS to Ubuntu 18.04, and followed the instructions to download nvidia-driver-450 and CUDA 10.1. Upon doing so, CUDA automatically updated to version 11 which is incompatible with my model.
Is there a way to version-lock CUDA to 10.1? Or will I have to compile from source to solve this issue?