As far as I know, it does.
It should work all the same, but without the need to initialize an optimizer, scheduler, etc, using the accelerator, and only init the device, eval_dataloader, model with the accelerator.
In case it won’t work for for some reason there are more other wrappers to run distributed inference with (which also give a speed up), such as Optimum (made to accelerate inference).
In addition it’s worth to mention you can always do it the “hard” way and implement stuff with torch.nn.DataParallel or with torch.nn.parallel.DistributedDataParallel.
Then you can run your code via the torchrun console script. But again, I personally find this method harder than a wrapper like accelerate or Optimum.