Ray Tune currently gracefully stops training on SIGINT. However, the Ray core worker prevents SIGINT (and SIGTERM) to be processed by child tasks, which means that Ray Tune runs that are started in remote tasks (e.g. via Ray client) cannot be gracefully interrupted.
In k8s-based cloud tests that used the Ray client to kick off a Ray Tune run, this lead to test flakiness, as final experiment state could not be gracefully persisted to cloud storage.
This PR adds support for SIGUSR1 in addition to SIGINT to interrupt training gracefully.
`test_cluster: test_replica_startup_status_transitions` is periodically flaky with the replica hanging in `PENDING_ALLOCATION`. This could be because there is no ordering guarantee on async actor calls, so the `reconfigure` method might execute first and block the asyncio loop (due to `ray.get`), not allowing the `is_allocated` call to run.
Closes#23503
We are fixing two issue here:
1. The unified controller API used pickle to pack the init args, we are changing it to cloudpickle for now. (this is something I missed during code review)
2. The checkpoint state functionality in controller uses pickle to prevent ray cluster specific state written to checkpoint and unable to recover in a fresh new cluster. However, this recover from new cluster is not good UX and we should prefer an end to end solution like resubmitting via REST API.
As a corollary, the deployment state manager should not care about deserializing replica config and init args. Rather, it should just pass the protobuf directly to replica. I can do that either here or as a follow up.
`set_start_time()` was not implemented for the progress reporter base class, but it's called in `tune.run()`.
Instead of adding new methods to set runtime arguments, this PR moves to a singular and forward-compatible `setup()` method that defaults to no-op. This way custom reporters can make use of runtime information passed to the reporter, but can choose to ignore it per default.
Previously we have double dump behavior that makes json serde not human readable or friendly, but it's required given `DAGDriver` takes `dag_node_json` as first arg and it will appear in YAML.
This PR removes extra `json.dumps()` in encoder path, eliminated and simplified most encoder / object_hooks that are not needed in the first place to make everything simpler again.
Sample YAML now for a complex DAG: https://gist.github.com/jiaodong/32991771e9d78c35767eb24ed73f8236
We're pretty close to have a better minimal JSON representation of the whole dag after this. I might include in this PR or separate one.
`gcsfs` complains about an invalid `create_parents` argument when using google cloud storage with cloud checkpoints. Thus we should use an alternative fs spec handler that omits this argument for gs.
The root issue will be fixed here: https://github.com/fsspec/gcsfs/pull/471
Implements `SklearnTrainer` and `SklearnPredictor`. Full parallelism with joblib + support for GPU enabled estimators like cuML.
Interface has been modified slightly by addition of several arguments, which were required for full functionality.
I haven't tested cuML yet, will do it later.
Depends on https://github.com/ray-project/ray/pull/23889
Co-authored-by: Kai Fricke <kai@anyscale.com>
Adds a `ScalingConfigDataClass.validate_config` classmethod to allow for a generic way of validating ScalingConfigs by allowing only certain keys.
Co-authored-by: Kai Fricke <kai@anyscale.com>
The ray.timeline command currently only shows task for task events, which isn't very useful if your program has multiple types of tasks. This PR adds "::<function name>" to the string, similar to what we do for process names, to distinguish between different tasks.
A legacy K8s test fails due to incorrect usage of @ray.method which only started raising errors after the Ray 1.12.0 branch cut.
This PR removes the use of @ray.method in the test.
Some context in #23271 and #23471
In addition, I noticed some of the test were flakey due to out-of-memory issues. For that reason, I've doubled the memory request and limits in the legacy operator's example files.
I've also added CPU limits in an example file that was missing them -- it makes the most sense for consistency with Ray's resource model to use CPU limits in K8s configs.
Finally, I added an extra note to the instructions for running the tests.
This change sets `"memory"`'s default to `0` in the `resource_dict` but keeps the default as `None` in `ray_actor_options`. It adds logic to both problematic lines to handle `None` in case of future settings updates. It also adds unit tests to prevent regressions.
* Provide a utility to ping a Ray cluster and verify it has the same Ray version. This is useful to check if a Ray cluster is available at a given address, without connecting to the cluster with the more heavyweight ray.init(). This utility is integrated with ray memory to provide a better error message when the Ray cluster is unavailable. There seem to be user demand for exposing this as an API as well.
* Improve the error message when the address provided to Ray does not contain port.
Memory limit determination test is not relevant for Windows; we already skip the CPU limit determination tests for Windows, so we skip for the memory limit determination as well.
People use models that accept dictionaries as input. For example, a model might take the following dictionary as input:
{
"value1": [[7], [8]],
"value2": [[10], [15]],
}
To facilitate using Ray Datasets with such models and to provide feature parity with to_torch, we should support more feature_columns types.
This PR adds support for vectorized global and grouped aggregations, porting the built-in aggregations to vectorized block aggregations for tabular datasets.
This PR refactors `LazyBlockList` in service of out-of-band serialization (see [mono-PR](https://github.com/ray-project/ray/pull/22616)) and is a precursor to an execution plan refactor (PR #2) and adding the actual out-of-band serialization APIs (PR #3). The following is included in this refactor:
1. `ReadTask`s are now a first-class concept, replacing calls;
2. read stage progress tracking is consolidated into `LazyBlockList._get_blocks_with_metadta()` and more of the read task complexity, e.g. the read remote function, was pushed into `LazyBlockList` to make `ray.data.read_datasource()` simpler;
3. we are a bit smarter with how we progressively launch tasks and fetch and cache metadata, including fetching the metadata for read tasks in `.iter_blocks_with_metadata()` instead of relying on the pre-read task metadata (which will be less accurate), and we also fix some small bugs in the lazy ramp-up around progressive metadata fetching.
(1) is the most important item for supporting out-of-band serialization and fundamentally changes the `LazyBlockList` data model. This is required since we need to be able to reference the underlying read tasks when rewriting read stages during optimization and when serializing the lineage of the Dataset. See the [mono-PR](https://github.com/ray-project/ray/pull/22616) for more context.
Other changes:
1. Changed stats actor to a global named actor singleton in order to obviate the need for serializing the actor handle with the Dataset stats; without this, we were encountering serialization failures.
This PR adds a `Checkpoint_as_directory()` context manager that either returns the local path (if checkpoint is already a directory) or a temporary directory path containing the checkpoint data, which is cleaned up after use. The path should be considered as a read-only source for loading data from the checkpoint.
A common use case for processing checkpoint data is to convert it into a directory with `Checkpoint.to_directory()` and then do some read-only processing (e.g. restoring a ML model).
This process has two flaws: First, `to_directory()` creates a temporary directory that has to be explicitly cleaned up by the user after use. Secondly, if the checkpoint is already a directory checkpoint, it is copied over, which is inefficient for large checkpoints (e.g. huggingface models) and then even more prone to unwanted side effects if not cleaned up properly.
With this context manager that effectively returns a directory that is to be used as a read-only data source, we can avoid manual cleaning up and unnecessary data copies (or avoid internal inspection as e.g. in https://github.com/ray-project/ray/pull/23876/files#diff-47db2f054ca359879f77306e7b054dd8b780aab994961e3b4911330ae15eeae3R57-R60)
See also discussion in https://github.com/ray-project/ray/pull/23850/files#r850036905
Add BatchMapper preprocessor.
Update the semantics of preprocessor.fit() to allow for multiple fit. This is to follow scikitlearn example.
Introduce FitStatus to explicitly incorporate Chain case.
Following #23862, there was an uncaught bug when comparing nan-priority checkpoints. This is because float("nan") <= float("nan") is always False (unlike e.g. np.nan <= np.nan, which is True).
This PR fixes this bug and adds a new test to ensure correct behavior.
Changes the logic in CheckpointManager to consider checkpoints with nan value of the metric as worst values, meaning they will be deleted first if keep_checkpoints_num is set.
Serve gets actors using the current Ray namespace. However, the Ray namespace and the controller namespace may not match when using the `_override_controller_namespace` argument in `serve.start()`. This change ensures that the `get_actor()` calls in `ActorReplicaWrapper` use the controller namespace. This also allows `num_replicas` to be scaled up and down properly when using `_override_controller_namespace`.
Clean up the ci/ directory. This means getting rid of the travis/ path completely and moving the files into sensible subdirectories.
Details:
- Moves everything under ci/travis into subdirectories, e.g. ci/build, ci/lint, etc.
- Minor adjustments to some scripts (variable renames)
- Removes the outdated (unused) asan tests
