## Why are these changes needed?
In the nightly test we see
```
Command returned non-success status: 1; Command logs:Traceback (most recent call last): File "dask_on_ray/large_scale_test.py", line 17, in from ray._private.test_utils import monitor_memory_usage File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/_private/test_utils.py", line 18, in import pytest ModuleNotFoundError: No module named 'pytest'
```
This PR fixes this error.
## Related issue number
## Why are these changes needed?
It's part of redis removal project. This PR focus on using gcs kv in internal kv.
- gcs client is introduced
- internal kv is updated to use gcs rpc client based kv
- related code got updated.
The other PR will update components using redis to use internal kv.
## Related issue number
https://github.com/ray-project/ray/issues/19443
## Why are these changes needed?
In this test case, the following case could happen:
1. actor creation first uses all resource in local node which is a GPU node
2. the actor need GPU will not be able to be scheduled since we only have one GPU node
The fixing is just a short term fix and only tries to connect to the head node with CPU resources.
## Related issue number
#19438
Why are these changes needed?
For Java worker, we generate a UUID string as the namespace if a job is not specified a namespace by user.
Related issue number
#16474
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* Add an RLlib Tune experiment to UserTest suite.
* Add ray.init()
* Move example script to example/tune/, so it can be imported as module.
* add __init__.py so our new module will get included in python wheel.
* Add block device to RLlib test instances.
* Reduce disk size a little bit.
* Add metrics reporting
* Allow max of 5 workers to accomodate all the worker tasks.
* revert disk size change.
* Minor updates
* Trigger build
* set max num workers
* Add a compute cfg for autoscaled cpu and gpu nodes.
* use 1gpu instance.
* install tblib for debugging worker crashes.
* Manually upgrade to pytorch 1.9.0
* -y
* torch=1.9.0
* install torch on driver
* bump timeout
* Write a more informational result dict.
* Revert changes to compute config files that are not used.
* add smoke test
* update
* reduce timeout
* Reduce the # of env per worker to 1.
* Small fix for getting trial_states
* Trigger build
* simply result dict
* lint
* more lint
* fix smoke test
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
