The Staroid node provider has been abandoned and unmaintained for quite some time now. Due to the fact that there are no active maintainers, the original contributors cannot be reached, and there is no clear interest, we are no longer officially endorsing or supporting the node provider.
Co-authored-by: Alex Wu <alex@anyscale.com>
This is a down scoped change. For the full overview picture of Tune control loop, see [`Tune control loop refactoring`](https://docs.google.com/document/d/1RDsW7SVzwMPZfA0WLOPA4YTqbRyXIHGYmBenJk33HaE/edit#heading=h.2za3bbxbs5gn)
1. Previously there are separate waits on pg ready and other events. As a result, there are quite a few timing tweaks that are inefficient, hard to understand and unit test. This PR consolidates into a single wait that is handled by TrialRunner in each step.
- A few event types are introduced, and their mapping into scenarios
* PG_READY --> Should place a trial onto it. If somehow there is no trial to be placed there, the pg will be put in _ready momentarily. This is due to historically resources is conceptualized as a pull based model.
* NO_RUNNING_TRIALS_TIME_OUT --> possibly not sufficient resources case
* TRAINING_RESULT
* SAVING_RESULT
* RESTORING_RESULT
* YIELD --> This just means that simply taking very long to train. We need to punt back to the main loop to print out status info etc.
2. Previously TrialCleanup is not very efficient and can be racing between Trainable.stop() and `return_placement_group`. This PR streamlines the Trial cleanup process by explicitly let Trainable.stop() to finish followed by `return_placement_group(pg)`. Note, graceful shutdown is needed in cases like `pause_trial` where checkpointing to memory needs to be given the time to happen before the actor is gone.
3. There are quite some env variables removed (timing tweaks), that I consider OK to proceed without deprecation cycle.
Adds support for ignoring NaNs in aggregations. NaNs will now be ignored by default, and the user can pass in `ds.mean("A", ignore_nulls=False)` if they would rather have the NaN be propagated to the output. Specifically, we'd have the following null-handling semantics:
1. Mix of values and nulls - `ignore_nulls`=True: Ignore the nulls, return aggregation of values
2. Mix of values and nulls - `ignore_nulls`=False: Return `None`
3. All nulls: Return `None`
4. Empty dataset: Return `None`
This all null and empty dataset handling matches the semantics of NumPy and Pandas.
I’d like to make a small proposal to change the behavior of ray stop
### Status quo
It basically just sends a SIGTERM and finish asynchronously. It is vulnerable to have leaked processes or port conflict when you run ray stop && ray start I feel like the right behavior is as follow;
### New
- Send sigterm and wait for processes to terminate
- Display the progress to users
- If procs are not terminated by X seconds, send SIGKILL.
### API change
We will add `--grace-period` flag. The default is ray `stop --grace-period=X (10 seconds by default)`
And if users don’t want to be blocked, they can use ray stop --force (which already exists. It just sends SIGKILL, so procs are guaranteed to be terminated)
When a Ray program first creates an ObjectRef (via ray.put or task call), we add it with a ref count of 0 in the C++ backend because the language frontend will increment the initial local ref once we return the allocated ObjectID, then delete the local ref once the ObjectRef goes out of scope. Thus, there is a brief window where the object ref will appear to be out of scope.
This can cause problems with async protocols that check whether the object is in scope or not, such as the previous bug fixed in #19910. Now that we plan to enable lineage reconstruction to automatically recover lost objects, this race condition can also be problematic because we use the ref count to decide whether an object needs to be recovered or not.
This PR avoids these race conditions by incrementing the local ref count in the C++ backend when executing ray.put() and task calls. The frontend is then responsible for skipping the initial local ref increment when creating the ObjectRef. This is the same fix used in #19910, but generalized to all initial ObjectRefs.
This is a re-merge for #21719 with a fix for removing the owned object ref if creation fails.
https://github.com/ray-project/ray/issues/21488#issuecomment-1027122177 :
> We discussed this issue in a bit more detail and came to the conclusion that we should set the CPU resource requirement for each actor in the actor pool to 0, to make the Ray Pool compatible/same behavior as the Python multiprocessing pool. Would that work for you @yogeveran ? (very similar to solution 4 mentioned above, but with 0.0 instead of 0.1, so it works in all cases).
Added lines to autoscaler configuration yaml to create a service account that is used to give the autoscaler permissions to list and read pods and patch the cluster CRD for up/downscaling.
Previously code incorrectly assumed that an exiting worker would disconnect from the raylet promptly to release resources. This isn't the case if the worker is owning references. This PR plumbs through the right release resources call even in this scenario.
Closes https://github.com/ray-project/ray/issues/10960
Co-authored-by: SangBin Cho <rkooo567@gmail.com>
Followup from https://github.com/ray-project/ray/pull/21788. Previously we had a lot of `serialized_runtime_env == "{}" || serialized_runtime_env == ""` scattered around the C++ code; this PR puts this in a helper function.
Continuing docs overhaul, tune now has:
- [x] better landing page
- [x] a getting started guide
- [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials
- [x] the new user guide contains guides to tune features and practical integrations
- [x] we rewrote some of the feature guides for clarity
- [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower)
- [x] sphinx-gallery examples are now moved to markdown notebook, as started in #22030.
- [x] Examples are tested in the new framework, of course.
There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
`__dealloc__` is not allowed to call python code and this leads to two problems:
- The data has already been cleaned up
- Deadlock if there are locks used.
THis PR move the implementation to python layer to avoid this
We've had multiple issues that manifest as unexpected autoscaler logs about resource demands.
To make it easier to debug such issues, this PR adds a debug flag to allow logging the entire resource message used by the autoscaler as its source of truth about the Ray internals' resource usage.
If the env AUTOSCALER_LOG_RESOURCE_BATCH_DATA=1 is set, the autoscaler will log the resource message.
