# Feature request - toggle logging into experiment.json

**URL:** <https://discuss.ray.io/t/feature-request-toggle-logging-into-experiment-json/2001>\
**Category:** RLlib\
**Created:** [May 1, 2021, 5:31pm UTC](https://discuss.ray.io/t/feature-request-toggle-logging-into-experiment-json/2001 "2021-05-01T17:31:24Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![roireshef](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/roireshef/32/179_2.png) [@roireshef](https://discuss.ray.io/u/roireshef)\
**Post date:** [May 1, 2021, 5:31pm UTC](https://discuss.ray.io/t/feature-request-toggle-logging-into-experiment-json/2001/1 "2021-05-01T17:31:24Z")

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Hi Guys,

My experiment.json files are getting huge pretty fast. Running RLlib at scale (via Tune) on a big cluster makes the shared drive explode. I might be missing - why is experiment.json even being logged? Is it valuable to post-training analysis? Is there any automation/tool to analyze it?

And anyway, I’d opt for letting users toggle it off to save storage space.

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**Author:** ![rliaw](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/rliaw/32/24_2.png) [@rliaw](https://discuss.ray.io/u/rliaw)\
**Post date:** [May 2, 2021, 7:04am UTC](https://discuss.ray.io/t/feature-request-toggle-logging-into-experiment-json/2001/2 "2021-05-02T07:04:12Z")

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Yeah, you should be able to do this by setting `TUNE_GLOBAL_CHECKPOINT_S=1000000000` (some very large number)