ScalingConfig with Ray Tune

Ray Libraries (Data, Train, Tune, Serve) Ray Tune
1

Hello,

I am using TorchTrainer to wrap a pytorch Lightning training script to use with RayTune

I am wondering what the difference is between these two ScalingConfigs:

scaling_config = ScalingConfig(
    trainer_resources={"CPU": 0},
    resources_per_worker={"CPU": 8, "GPU": 2},
    num_workers=1,
    use_gpu=True,
)

and

scaling_config = ScalingConfig(
    trainer_resources={"CPU": 0},
    resources_per_worker={"CPU": 4, "GPU": 1},
    num_workers=2,
    use_gpu=True,
)

If my ray cluster, say, has 4 GPUs and 16 CPUs, will both of these configurations launch 2 concurrent trials, each trial utilizing 2 GPUs and 8 CPUs? Will both of these trials use lightning DDP under the hood? thank you!