test_modin.py is flakey right now. It complains about some modules can't be imported. This seems like a init issue where client mode and non-client mode are mixed. This test closes the cluster for each run. It slows the test a little bit, but it's more stable.
This PR adds a filtering support. The filtering is done from the API server side (not from the source side). Source side filtering is a bit complicated to write an elegant solution, and we will handle it in the future (no optimization for alpha APIs).
We will also support limited types of columns for each API.
The API is as follows
ray list [resources] -- filter [key] [value] => filter data that's key==value.
In the future, we can also support more complicated filtering like !=, And, Or , or etc.
This adds a per-dataset config object to DataParallelTrainer. These configs define how the Dataset should be read into the DataParallelTrainer. It configures the preprocessing, splitting, and ingest strategy per-dataset. DataParallelTrainers declare default DatasetConfigs for each dataset passed in the ``datasets`` argument. Users have the opportunity to selectively override these configs by passing the ``dataset_config`` argument. Trainers can also define user customizable values (e.g., XGBoostTrainer doesn't support streaming ingest).
This PR adds the minimal support for dataset configs. Future PRs will:
- Add support for streaming ingest
- Move this config from DataParallelTrainer to ml.Trainer
This PR uses a task/actor launch hook to generate better error messages for nested Tune tasks/actors in the case there are no extra resources reserved for them. The idea is that the Tune trial runner actor can set a hook prior to executing the user code. If the user code launches a task, and the placement group for the trial cannot possibly fit the task, then we raise TuneError right off to warn the user.
The package "ml" should be renamed to "air".
Main question: Keep a `ml.py` with `from ray.air import *` for some level of backwards compatibility?
I'd go for no to force people to use the new structure.
The TrialExecutor base class was a stub and has been deprecated long ago; direct inheritance was disabled. This PR removes the base class and moves the remaining functionality into the RayTrialExecutor.
We removed the thread local core worker instance in this PR, which is the further arch cleaning stuff for removing multiple workers in one process.
It also removes the unnecessary parameter `workerId` from JNI.
The main issue with this test is that the worker is trying to connect to the raylet but the raylet exits, and in this case, it'll hang there. This happens before the periodical check runs so the worker won't exit as well.
This fix moves the hanging part to the place after the periodical check starts.
Another issue is the pubsub timeout. The default one is 60s, and we need to adjust it to smaller value to make it work within 60s for the test.
The Datasets UX assessment showed that users had difficulties in writing UDFs: what's input/output types, how to write the function etc.
Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-136.us-west-2.compute.internal>
Looks like the test_logging fails when syncer is enabled. However, I found the test was badly written, and the failure might be a side effect of syncer (I am not sure why. Maybe syncer slows down ray.init()?)
ray/python/ray/tests/test_logging.py
Line 228 in f75ede1
def test_log_monitor_backpressure(ray_start_cluster, monkeypatch):
Anyway, it seems like the test will fail if there's a delay after log monitor is started.
Testing this is not trivial. Instead, I made log_monitor unit testable and added full unit tests.
This also adds a better exception message on another flaky test test_log_rotation . I need more data before actually fixing this issue.
The current implementation of amp does not work if the model that is being wrapped defines a custom __getstate__ method. It would fail at the assertion like here: https://discuss.ray.io/t/ray-train-hangs-for-long-time/6333/7.
This PR fixes amp for this case, and adds tests for it.
Duplicate for #25247.
Adds a fix for Dask-on-Ray. Previously, for tasks with multiple return values, we implicitly allowed returning a dict with the return index as the key. This was used by Dask-on-Ray, but this is not documented behavior, and we now require task returns to be iterable instead.
We're currently installing matching wheels on the fly in the python script for Ray client tests. However, we can't reload modules with changed protobuf configurations, and thus can't reload ray completely. Since the `anyscale` pacakge depends on Ray, this effectively prevents us from installing matching wheels within the python script.
There are a few possible solutions to this. First, we could separate out the local environment preparation from the test running - this will duplicate some logic and is thus a bit more involved, but should be considered in the future. For now, we adjust the `run_release_tests.sh` shell script to install any passed `--ray-wheels` wheels locally. We only do this in CI instances per default as these wheels will not be compatible with e.g. MacOS.
Link to successful build: https://buildkite.com/ray-project/release-tests-branch/builds/619#_
Bazel operates by simply running the python scripts given to it in `py_test`. If the script doesn't invoke pytest on itself in the `if _name__ == "__main__"` snippet, no tests will be ran, and the script will pass. This has led to several tests (indeed, some are fixed in this PR) that, despite having been written, have never ran in CI. This PR adds a lint check to check all `py_test` sources for the presence of `if _name__ == "__main__"` snippet, and will fail CI if there are any detected without it. This system is only enabled for libraries right now (tune, train, air, rllib), but it could be trivially extended to other modules if approved.
In this PR we simulate the case where serve can continue to function even when GCS is down and the reconfig continue to work once GCS is back.
To make it close to the real-world case, the docker is used for isolation:
It starts a head node (0 cpus) and a worker node
It tried the basic function and make sure it's working
It kills GCS and make sure everything is working.
It starts GCS and make sure reconfig continues to work.
This is the basic cases for serve HA. We'll add more once we get better integrations.
This PR
- enables piping of autoscaler events to the driver's stdout with KubeRay
- cleans up the autoscaler's startup sequence
- removes some redis references
