Can two kernels access the same conda environment at the same time even when using GPU?
This is not the best answer though, I realized that I can evaluate my model in another conda environment that has another version of Tensorflow. In this environment, my CUDA and CUDNN versions are not compatible with the version of Tensorflow, so my GPU was not used. In this sense, I evaluated a model without stopping or affecting learning a model in the running kernel.
I'm running one kernel to learn a Tensorflow model and that's using my GPU. Now, in the same conda environment, I would like to evaluate another model learned before, and the model is also a Tensorflow one. I'm sure I can run two kernels with the same conda environment mostly but I'm not sure when using GPU. Now if I run a kernel using Tensorflow, can it affect a kernel running early somehow, especially in terms of GPU usage?
My environment: Windows10, tensorflow2.1, python3.7.9