Comments (4)
I think there's a more general underlying question of what to do with stdio for all Ray system components. We could have something like a RAY_LOG_DIR where logs are aggregated for all system components including Redis. All stdio can be redirected there. User shouldn't see any of this. At the same time, they should be able to access these messages if they want to. So best practice is to store it in a per-daemon log and log-rotate them (to keep the space per log capped).
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Agreed. There is a deeper question about how to handle output from all the different processes.
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As a stopgap, this commit redirects the redis-server output to /dev/null
. This is not a long term solution.. 2d0d041
from ray.
We're suppressing the error for now (by always redirecting redis output to a file). We may want to look into this more in the future to improve redis performance..
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Related Issues (20)
- There are some deprecated items that need to be removed
- CI test linux://rllib:examples/evaluation/evaluation_parallel_to_training_multi_agent_duration_auto_torch_envrunner is flaky HOT 4
- Ray v2.11.0 missing windows distribution HOT 3
- [Ray Tune/ Train] Auth with aws_web_identity_token or use the provided file system provider in runtime config HOT 3
- [<Ray component: Core>] ray raise error on nvidia cuda machine for amdgpu missing
- [Tune] Trials on pre-started game instances
- Release test chaos_torch_batch_inference_16_gpu_300gb_raw.aws failed HOT 3
- [Data] Add `override_num_blocks` parameter to `from_pandas`
- Release test dataset_shuffle_push_based_sort_1tb.aws failed HOT 1
- [Core] Actor/Task cannot be scheduled on worker node. HOT 1
- [Ray core] Stopped job leaks worker HOT 4
- [serve] Support resource-based autoscaling HOT 1
- [Observability / Doc] Add support of ray debugger on windows HOT 5
- [Doc] Python 3.12 `docs` env has conflicts with `..scripts/format.sh` HOT 2
- [Dashboard] `py-spy` profiling initiated from the Ray Dashboard fails if `sudo` is not installed HOT 3
- [Ray Data] map_batches with actors is 25% slower than manually consuming with iter_batches
- [Core][Actors] Duplicate named actor exception should not be lazy if possible HOT 1
- [Core] `ray.wait` not actually wait until ready when the task is longer than 12 days
- [tune] `tune.with_resources` with `PlacementGroupFactory` cannot find GPUs in `train_fn` HOT 4
- CI test linux://python/ray/dashboard:test_dashboard is consistently_failing HOT 5
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