# How to force Tuner workers to use \*only\* the worker node

**URL:** <https://discuss.ray.io/t/how-to-force-tuner-workers-to-use-only-the-worker-node/11267>\
**Category:** Ray Tune\
**Created:** [July 5, 2023, 1:39am UTC](https://discuss.ray.io/t/how-to-force-tuner-workers-to-use-only-the-worker-node/11267 "2023-07-05T01:39:48Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![andrwang](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/andrwang/32/4697_2.png) [@andrwang](https://discuss.ray.io/u/andrwang)\
**Post date:** [July 5, 2023, 1:39am UTC](https://discuss.ray.io/t/how-to-force-tuner-workers-to-use-only-the-worker-node/11267/1 "2023-07-05T01:39:48Z")

</div>

**How severe does this issue affect your experience of using Ray?**

- Medium: It contributes to significant difficulty to complete my task, but I can work around it.

Currently, I’m spinning up a Ray Tuning job that trains and tunes very large model with concurrency set to 3. An individual training of the model takes a huge amount of memory, so the worker nodes are allocated with 350G of memory each. The head node is only allocated with 8G memory and 8 CPU.

For some reason, Ray is scheduling tasks or actors that are using a lot of the memory of the head nodes, while a number of the worker nodes’ memory remains mostly unused.

I tried to force them to use worker nodes by setting cpu to 0, but that didn’t seem to work: the memory would still explode on the head node.

I saw [Resources — Ray 2.5.1](https://docs.ray.io/en/latest/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements), which could definitely work for me, but I am not sure how to apply it to Ray Tuning. Where should I pass those specifications?