In Ray 2.0, we want to achieve api server HA.
Originally serve endpoints are in head node.
This pr moves serve endpoints to dashboard agents, so they will be HA due to multiple replica of dashboard agent.
Enable checking of the ray core module, excluding serve, workflows, and tune, in ./ci/lint/check_api_annotations.py. This required moving many files to ray._private and associated fixes.
## Why are these changes needed?
This is to refactor the interaction of state cli to API server from a hard-coded request workflow to `SubmissionClient` based.
See #24956 for more details.
## Summary
<!-- Please give a short summary of the change and the problem this solves. -->
- Created a `StateApiClient` that inherits from the `SubmissionClient` and refactor various listing commands into class methods.
## Related issue number
Closes#24956Closes#25578
Currently when Raylets die, it is hard to figure out:
if a Raylet died at all in a cluster. Usually we have to check on nodes where a number of workers died and see if the Raylet has died as well.
reason of Raylet's death.
With this PR, if a Raylet dies from a reason other than SIGTERM, the dashboard agent will report the failure along with last 20 lines of the Raylet log.
This is a follow-up PRs of https://github.com/ray-project/ray/pull/24813 and https://github.com/ray-project/ray/pull/24628
Unlike the change in cpp layer, where the resubscription is done by GCS broadcast a request to raylet/core_worker and the client-side do the resubscription, in the python layer, we detect the failure in the client-side.
In case of a failure, the protocol is:
1. call subscribe
2. if timeout when doing resubscribe, throw an exception and this will crash the system. This is ok because when GCS has been down for a time longer than expected, we expect the ray cluster to be down.
3. continue to poll once subscribe ok.
However, there is an extreme case where things might be broken: the client might miss detecting a failure.
This could happen if the long-polling has been returned and the python layer is doing its own work. And before it sends another long-polling, GCS restarts and recovered.
Here we are not going to take care of this case because:
1. usually GCS is going to take several seconds to be up and the python layer's work is simply pushing data into a queue (sync version). For the async version, it's only used in Dashboard which is not a critical component.
2. pubsub in python layer is not doing critical work: it handles logs/errors for ray job;
3. for the dashboard, it can just restart to fix the issue.
A known issue here is that we might miss logs in case of GCS failure due to the following reasons:
- py's pubsub is only doing best effort publishing. If it failed too many times, it'll skip publishing the message (lose messages from producer side)
- if message is pushed to GCS, but the worker hasn't done resubscription yet, the pushed message will be lost (lose messages from consumer side)
We think it's reasonable and valid behavior given that the logs are not defined to be a critical component and we'd like to simplify the design of pubsub in GCS.
Another things is `run_functions_on_all_workers`. We'll plan to stop using it within ray core and deprecate it in the longer term. But it won't cause a problem for the current cases because:
1. It's only set in driver and we don't support creating a new driver when GCS is down.
2. When GCS is down, we don't support starting new ray workers.
And `run_functions_on_all_workers` is only used when we initialize driver/workers.
GCS pubsub has been the default for awhile. There is little chance that we would need to revert back to Redis pubsub in future. This is the step in removing Redis pubsub, by first removing the `enable_gcs_pubsub()` feature guard.
As we are turning redisless ray by default, dashboard doesn't need to talk with redis anymore. Instead it should talk with gcs and gcs can talk with redis.
This PR moves the sdk to its own folder, then includes everything in `import ray.autoscaler.sdk` in ray's import path.
Note: that there were circular dependencies in naively doing this because the ray core now uses constants that were defined in the autoscaler for internal kv operations (and the autoscaler similarly calls into the ray core). The solution was to move those internal kv keys into ray core constants so the imports flow (more) one way.
Co-authored-by: Alex Wu <alex@anyscale.com>
Currently `wait_until_succeeded_without_exception` is used in the dashboard, and it returns True/False. Unfortunately, there are lots of code that doesn't assert on this method (which means things are not actually tested).
This is part of gcs ha project. This PR try to bootstrap dashboard with gcs address instead of redis.
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
## Why are these changes needed?
This is part of redis removal project. In this PR all direct usage of redis got removed except function table.
Function table will be migrated in the next PR
## Related issue number
#19443
* formatting
* format util
* format release
* format rllib/agents
* format rllib/env
* format rllib/execution
* format rllib/evaluation
* format rllib/examples
* format rllib/policy
* format rllib utils and tests
* format streaming
* more formatting
* update requirements files
* fix rllib type checking
* updates
* update
* fix circular import
* Update python/ray/tests/test_runtime_env.py
* noqa
* Dashboard select port; Fix dashboard may hangs when exit
* Add test case
* Fix
* Fix test_stats_collector.py::test_get_all_node_details
* Refine dashboard error messages
* Refine code
* Refine code
* Show last 10 lines of dashboard log if start dashboard failed
* Fix ValueError: too many values to unpack (expected 2) when getsockname
* Fix test_multi_node_3.py::test_calling_start_ray_head may fail
* Fix Windows CI
* Disable dashboard in C++ test
* Refine code
* Fix issue 7084
Co-authored-by: 刘宝 <po.lb@antfin.com>
