# Can 'tune.run' just run a function on multiple GPUs with different configs without "trials"?

**URL:** <https://discuss.ray.io/t/can-tune-run-just-run-a-function-on-multiple-gpus-with-different-configs-without-trials/536>\
**Category:** Ray Tune\
**Created:** [January 22, 2021, 7:09am UTC](https://discuss.ray.io/t/can-tune-run-just-run-a-function-on-multiple-gpus-with-different-configs-without-trials/536 "2021-01-22T07:09:50Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![actuallyaswin](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/actuallyaswin/32/302_2.png) [@actuallyaswin](https://discuss.ray.io/u/actuallyaswin)\
**Post date:** [January 22, 2021, 7:09am UTC](https://discuss.ray.io/t/can-tune-run-just-run-a-function-on-multiple-gpus-with-different-configs-without-trials/536/1 "2021-01-22T07:09:50Z")

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Hello all! As the title suggests, I was just curious if it’s possible to use `tune.run` for just running any function with a different set of configs, auto-queued on fractions of GPUs? Like without the hyperparameter search. Say for example I have a function `train` and I want to run it with different configs in parallel on subdivisions of GPU memory, like say once with `config={model_type: 0}`, another time with `config={model_type: 1}`, and maybe one more time with `config={model_type: 2}` . So there’s no hyperparameter space to search. And I don’t want `tune.run` to pick out a “best” run in this case, since I want to see the results / checkpoints for all three configs.

I like the functionality of how all the results are tabulated and evenly split over GPUs, just want to do that without picking a ‘best’ run per se. 😃 Consequently, I wouldn’t want this to auto-terminate any trials, since I’m intentionally wanting to try out all the different configs; I guess this is means I shouldn’t use any Schedulers? Is it sufficient to just set `scheduler=None` to ensure that no experiment early-terminates?

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<div class="post-metadata">

**Author:** ![kai](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/kai/32/3380_2.png) [@kai](https://discuss.ray.io/u/kai)\
**Post date:** [January 22, 2021, 9:45am UTC](https://discuss.ray.io/t/can-tune-run-just-run-a-function-on-multiple-gpus-with-different-configs-without-trials/536/2 "2021-01-22T09:45:07Z")

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Hi @actuallyaswin, yes, that’s possible! Ray Tune does not care about the kind of function you’re running as long as it adheres to the API.

This means a few things:

1. You should probably use **grid search** and constant parameters instead of defining a random search space
2. For scheduling you’ll use the **FIFOScheduler** which is the default and just executes trials one after another. This is indeed what you get when you pass `scheduler=None` (or nothing at all).
3. Your trials should still return a metric so that Ray Tune doesn’t complain. This can be a constant value. If you’re using grid search and FIFO scheduling, the metric will just be recorded but not influence training/scheduling in any way.

Something like this should work:

```auto
from ray import tune

def train(config):
    # ...
    tune.report(metric=0)

config = {
    "model_type": tune.grid_search([0, 1, 2]), # you could also pass strings here
    "constant_value": 5.0
}

tune.run(
    train,
    config=config,
    resources_per_trial={"gpu": 0.25})

```