Ray tune with environment using GPU

So I have an environment doing some fluid dynamics on a gpu. I want to use some rllib algorithms and tune to train an agent in this environment. I don’t have a proper cuda for tensorflow and simulating the environment will still be the bottleneck so I don’t care enough to fix it. Can I assign a gpu to a worker without ray complaining that tensorflow can’t use it the gpu that was assigned?

The environment works fine without ray. The code running ray is pretty much just

tune.run("PPO", config={"env": TestEnv, "num_workers": 1, "num_gpus_per_worker": 1}) 

which results in

RuntimeError: GPUs were assigned to this worker by Ray, but your DL framework (tf) reports GPU acceleration is disabled. This could be due to a bad CUDA- or tf installation.

Could you simply try to comment out this error in the code and see what happens?

Before I saw this I actually just went and installed a full cuDNN so that tf would stop complaining to ray and that worked fine. But this seems like it would also solve my problem, I disabled the cuDNN installation and commented out the line for that error and it seems to run like it should.

Thanks for taking the time anyway!