🔄 daily merge: master → main 2025-11-11 #674
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…API (ray-project#57977) ## Summary This PR updates the document embedding benchmark to use the canonical Ray Data implementation pattern, following best practices for the framework. ## Key Changes ### Use `download()` expression instead of separate materialization **Before:** ```python file_paths = ( ray.data.read_parquet(INPUT_PATH) .filter(lambda row: row["file_name"].endswith(".pdf")) .take_all() ) file_paths = [row["uploaded_pdf_path"] for row in file_paths] ds = ray.data.read_binary_files(file_paths, include_paths=True) ``` **After:** ```python ( ray.data.read_parquet(INPUT_PATH) .filter(lambda row: row["file_name"].endswith(".pdf")) .with_column("bytes", download("uploaded_pdf_path")) ``` This change: - Eliminates the intermediate materialization with `take_all()`, which loads all data into memory - Uses the `download()` expression to lazily fetch file contents as part of the pipeline - Removes the need for a separate `read_binary_files()` call ### Method chaining for cleaner code All operations are now chained in a single pipeline, making the data flow more clear and idiomatic. ### Consistent column naming Updated references from `path` to `uploaded_pdf_path` throughout the code for consistency with the source data schema. Signed-off-by: Balaji Veeramani <[email protected]>
This PR addresses several failing release tests likely due to the recent Ray Train V2 default enablement. The following failing release tests are addressed: - huggingface_transformers - distributed_training.regular - distributed_training.chaos distributed_training fix: `distributed_training.regular` and `distributed_training.chaos` were failing due to relying on the deprecated Reporting free-floating metrics functionality. The tests attempted to access an non existent key in the `result.metrics` that were not reported. The fix uploads a checkpoint to ensure this key exists. huggingface_transformers: The `huggingface_transformers` test was failing due to outdated accelerate and peft versions. The fix leverages a post-build file to ensure the proper accelerate and peft versions. Tests: Test Name | Before | After -- | -- | -- huggingface_transformers | https://buildkite.com/ray-project/release/builds/64733#019a0559-25da-401f-8d7e-3128b8f7d287 | https://buildkite.com/ray-project/release/builds/64888#019a090d-5f53-4f7d-b0ac-ac8cf7c529b6 distributed_training.regular | https://buildkite.com/ray-project/release/builds/64733#019a0572-1095-4b6f-b3bc-b496227c9280 | https://buildkite.com/ray-project/release/builds/64855#019a08c4-76b5-41b6-aaf6-2bbd443a0a1e distributed_training.chaos | https://buildkite.com/ray-project/release/builds/64733#019a0574-3862-4da2-a264-a9e11333bd72 | https://buildkite.com/ray-project/release/builds/64855#019a08c4-76b6-4344-90f2-cbcd637aae3d --------- Signed-off-by: JasonLi1909 <[email protected]> Signed-off-by: Jason Li <[email protected]> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
num_waiter == 0 does not necessarily mean that the request has been completed. --------- Signed-off-by: abrar <[email protected]>
…es (ray-project#57883) This PR adds a test to verify that DataOpTask handles node failures correctly during execution. To enable this testing, callback seams are added to DataOpTask that allow tests to simulate preemption scenarios by killing and restarting nodes at specific points during task execution. ## Summary - Add callback seams (`block_ready_callback` and `metadata_ready_callback`) to `DataOpTask` for testing purposes - Add `has_finished` property to track task completion state - Create `create_stub_streaming_gen` helper function to simplify test setup - Refactor existing `DataOpTask` tests to use the new helper function - Add new parametrized test `test_on_data_ready_with_preemption` to verify behavior when nodes fail during execution ## Test plan - Existing tests pass with refactored code - New preemption test validates that `on_data_ready` handles node failures correctly by testing both block and metadata callback scenarios --------- Signed-off-by: Balaji Veeramani <[email protected]> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
## Description 1. the `mlflow.start_run()` does not have the `tracking_uri` arg: https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html#mlflow.start_run 2. rewrite the mlflow set up as follow ``` mlflow.set_tracking_uri(uri="file://some_shared_storage_path/mlruns") mlflow.set_experiment("my_experiment") mlflow.start_run() ``` ## Related issues N/A --------- Signed-off-by: Lehui Liu <[email protected]>
…t#57855) > Thank you for contributing to Ray! 🚀 > Please review the [Ray Contribution Guide](https://docs.ray.io/en/master/ray-contribute/getting-involved.html) before opening a pull request. ## Description 1. Add visitors for collecting column names from all expressions and renaming names across the tree. 2. Use expressions for rename_columns, with_column, select_columns and remove cols and cols_rename in Project 3. Modify Projection Pushdown to work with combinations of the above operators correctly ## Related issues Closes ray-project#56878, ray-project#57700 ## Additional information > Optional: Add implementation details, API changes, usage examples, screenshots, etc. Signed-off-by: Goutam <[email protected]>
…ject#55291) Resolves: ray-project#55288 (wrong `np.array` in `TensorType`) Furthermore changes: - Changed comments to (semi)docstring which will be displayed as tooltips by IDEs (e.g. VSCode + Pylance) making that information available to the user. - `AgentID: Any -> Hashable` as it used for dict keys - changed `DeviceType` to be not a TypeVar (makes no sense in the way it is currently used), also includes DeviceLikeType (`int | str | device`) from `torch`. IMO it can fully replace the current type but being defensive I only added it as an extra possible type - Used updated DeviceType to improve type of Runner._device and make it more correct - Used torch's own type in `data`, current code supports more than just `str`. I refrained from adding a reference to `rllib` despite it being nice if they would be in sync. - Some extra formatting that is forced by pre-commit <!-- CURSOR_SUMMARY --> --- > [!NOTE] > Revamps `rllib.utils.typing` (NDArray-based `TensorType`, broader `DeviceType`, `AgentID` as `Hashable`, docstring cleanups) and updates call sites to use optional device typing and improved hints. > > - **Types**: > - Overhaul `rllib/utils/typing.py`: > - `TensorType` now uses `numpy.typing.NDArray`; heavy use of `TYPE_CHECKING` to avoid runtime deps on torch/tf/jax. > - `DeviceType` widened to `Union[str, torch.device, int]` (was `TypeVar`). > - `AgentID` tightened to `Hashable`; `NetworkType` uses `keras.Model`. > - Refined aliases (e.g., `FromConfigSpec`, `SpaceStruct`) and added concise docstrings. > - **Runners**: > - `Runner._device` now `Optional` (`Union[DeviceType, None]`) with updated docstring; same change in offline runners’ `_device` properties. > - **Connectors**: > - `NumpyToTensor`: `device` param typed as `Optional[DeviceType]` (via `TYPE_CHECKING`). > - **Utils**: > - `from_config`: typed `config: Optional[FromConfigSpec]` with `TYPE_CHECKING` import. > - **Misc**: > - Minor formatting/import ordering and comment typo fixes. > > <sup>Written by [Cursor Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit ae2e422. This will update automatically on new commits. Configure [here](https://cursor.com/dashboard?tab=bugbot).</sup> <!-- /CURSOR_SUMMARY --> --------- Signed-off-by: Daniel Sperber <[email protected]> Signed-off-by: Daraan <[email protected]> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Kamil Kaczmarek <[email protected]> Co-authored-by: Kamil Kaczmarek <[email protected]>
…57993) Although Spark-on-Ray depends on the Java bindings, we `java` tests are triggered by all C++ changes and we don't want to run Spark-on-Ray tests every time we change C++ code. --------- Signed-off-by: Edward Oakes <[email protected]>
…ay-project#57771) Signed-off-by: Nikhil Ghosh <[email protected]>
…ay-project#57987) Signed-off-by: daiping8 <[email protected]>
…ect#57974) This PR replace STATS with Metric as a way to define metric inside ray (as a unification effort) in all object-manager components. Normally, metrics are defined at the top-level component and passed down to sub-components. However, in this case, because object manager is used as an API across, doing so would feel unnecessarily cumbersome. I decided to define the metrics inline within each client and server class instead. Note that the metric classes (Metric, Gauge, Sum, etc.) are simply wrappers around static OpenCensus/OpenTelemetry entities. **Details** Full context of this refactoring work. - Each component (e.g., gcs, raylet, core_worker, etc.) now has a metrics.h file located in its top-level directory. This file defines all metrics for that component. - In most cases, metrics are defined once in the main entry point of each component (gcs/gcs_server_main.cc for GCS, raylet/main.cc for Raylet, core_worker/core_worker_process.cc for the Core Worker, etc.). These metrics are then passed down to subcomponents via the ray::observability::MetricInterface. - This approach significantly reduces rebuild time when metric infrastructure changes. Previously, a change would trigger a full Ray rebuild; now, only the top-level entry points of each component need rebuilding. - There are a few exceptions where metrics are tracked inside object libraries (e.g., task_specification). In these cases, metrics are defined within the library itself, since there is no corresponding top-level entry point. - Finally, the obsolete metric_defs.h and metric_defs.cc files can now be completely removed. This paves the way for further dead code cleanup in a future PR. Test: - CI Signed-off-by: Cuong Nguyen <[email protected]>
## Description Use `tune.report` instead of `train.report`. Signed-off-by: Matthew Deng <[email protected]>
…t#57620) <!-- Thank you for your contribution! Please review https://github.com/ray-project/ray/blob/master/CONTRIBUTING.rst before opening a pull request. --> <!-- Please add a reviewer to the assignee section when you create a PR. If you don't have the access to it, we will shortly find a reviewer and assign them to your PR. --> ## Why are these changes needed? This will be used to help control the targets that are returned. <!-- Please give a short summary of the change and the problem this solves. --> ## Related issue number <!-- For example: "Closes ray-project#1234" --> ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run pre-commit jobs to lint the changes in this PR. ([pre-commit setup](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: akyang-anyscale <[email protected]>
<!-- Thank you for contributing to Ray! 🚀 --> <!-- Please review https://github.com/ray-project/ray/blob/master/CONTRIBUTING.rst before opening a pull request. --> <!-- 💡 Tip: Mark as draft if you want early feedback, or ready for review when it's complete --> ## Description This PR adds a new check to make sure proxies are ready to serve traffic before finishing serve.run. For now, the check immediately finishes. <!-- Briefly describe what this PR accomplishes and why it's needed --> ## Related issues <!-- Link related issues: "Fixes ray-project#1234", "Closes ray-project#1234", or "Related to ray-project#1234" --> ## Types of change - [ ] Bug fix 🐛 - [ ] New feature ✨ - [ ] Enhancement 🚀 - [ ] Code refactoring 🔧 - [ ] Documentation update 📖 - [ ] Chore 🧹 - [ ] Style 🎨 ## Checklist **Does this PR introduce breaking changes?** - [ ] Yes⚠️ - [ ] No <!-- If yes, describe what breaks and how users should migrate --> **Testing:** - [ ] Added/updated tests for my changes - [ ] Tested the changes manually - [ ] This PR is not tested ❌ _(please explain why)_ **Code Quality:** - [ ] Signed off every commit (`git commit -s`) - [ ] Ran pre-commit hooks ([setup guide](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#lint-and-formatting)) **Documentation:** - [ ] Updated documentation (if applicable) ([contribution guide](https://docs.ray.io/en/latest/ray-contribute/docs.html)) - [ ] Added new APIs to `doc/source/` (if applicable) ## Additional context <!-- Optional: Add screenshots, examples, performance impact, breaking change details --> --------- Signed-off-by: akyang-anyscale <[email protected]>
…roject#57793) When deploying ray on Yarn using Skein, it's useful to expose the ray's dashboard via Skein's web ui. This PR shows how to expose that and update the related document. Signed-off-by: Zakelly <[email protected]> Co-authored-by: Edward Oakes <[email protected]>
…cgroup even if they are drivers (ray-project#57955) For more details about the resource isolation project see ray-project#54703. Driver processes that are registered in ray's internal namespace (such as ray dashboard's job and serve modules) are considered system processes. Therefore, they will not be moved into the workers cgroup when they register with the raylet. --------- Signed-off-by: irabbani <[email protected]>
…ray-project#57938) This PR adds persistent epoch data to the checkpointing logic in the [FSDP2 Template](https://docs.ray.io/en/master/train/examples/pytorch/pytorch-fsdp/README.html). This PR includes: - New logic for saving the epoch into a distributed checkpoint - New logic for resuming training from the saved epoch in a loaded checkpoint - Updates the [OSS FSDP2 example](https://docs.ray.io/en/master/train/examples/pytorch/pytorch-fsdp/README.html) to include the new logic Passing release test: https://buildkite.com/ray-project/release/builds/64867#019a08e3-1a3e-4fc5-9633-b8e3a0b0f34f --------- Signed-off-by: JasonLi1909 <[email protected]> Signed-off-by: Jason Li <[email protected]> Co-authored-by: matthewdeng <[email protected]>
> Briefly describe what this PR accomplishes and why it's needed. Making Cancel Remote Task RPC idempotent and fault tolerant. Added a python test to verify retry behavior, no cpp test since it just calls CancelTask RPC so nothing to test. Also renamed uses of RemoteCancelTask to CancelRemoteTask since it should be consistent. --------- Signed-off-by: joshlee <[email protected]>
…utside of a Ray Train worker (ray-project#57863) Introduce a decorator to mark functions that require running inside a worker process spawned by Ray Train. --------- Signed-off-by: Justin Yu <[email protected]>
## Description Fix the typing for UDFs. This should not accept an instance as it is currently defined. Signed-off-by: Matthew Owen <[email protected]>
…ock sizing (ray-project#58013) ## Summary Add a `repartition` call with `target_num_rows_per_block=BATCH_SIZE` to the audio transcription benchmark. This ensures blocks are appropriately sized to: - Prevent out-of-memory (OOM) errors - Ensure individual tasks don't take too long to complete ## Changes - Added `ds = ds.repartition(target_num_rows_per_block=BATCH_SIZE)` after reading the parquet file in `ray_data_main.py:98` Signed-off-by: Balaji Veeramani <[email protected]>
…57044) running core scalability tests on python 3.10 Updating unit test Successful release test: https://buildkite.com/ray-project/release/builds/60890#01999c8a-6fdc-446a-a9da-2b9b006692d3 --------- Signed-off-by: elliot-barn <[email protected]>
## Description We are using `read_parquet` in two of our tests in `test_operator_fusion.py`, this switches those to use `range` to make the tests less brittle. Signed-off-by: Matthew Owen <[email protected]>
with comments to github issues Signed-off-by: Lonnie Liu <[email protected]>
otherwise, the ordering or messages looks strange on windows. Signed-off-by: Lonnie Liu <[email protected]>
Updates the vicuna lightning deepspeed example to run w/ Train V2. --------- Signed-off-by: Justin Yu <[email protected]>
…8020) ## Description Currently, streaming repartition isn't combining blocks to the `target_num_rows_per_block` which is problematic, in a sense that it can only split blocks but not recombine them. This PR is addressing that by allowing it to recombine smaller blocks into bigger ones. However, one caveat is that the remainder of the block could still be under `target_num_rows_per_block`. ## Related issues > Link related issues: "Fixes ray-project#1234", "Closes ray-project#1234", or "Related to ray-project#1234". ## Additional information > Optional: Add implementation details, API changes, usage examples, screenshots, etc. --------- Signed-off-by: Alexey Kudinkin <[email protected]>
…e buildup (ray-project#57996) …e buildup > Thank you for contributing to Ray! 🚀 > Please review the [Ray Contribution Guide](https://docs.ray.io/en/master/ray-contribute/getting-involved.html) before opening a pull request. >⚠️ Remove these instructions before submitting your PR. > 💡 Tip: Mark as draft if you want early feedback, or ready for review when it's complete. ## Description > Briefly describe what this PR accomplishes and why it's needed. ### [Data] ConcurrencyCapBackpressurePolicy - Handle internal output queue buildup **Issue** - When there is internal output queue buildup specifically when preserve_order is set, we don't limit task concurrency in streaming executor and just honor static concurrency cap. - When concurrency cap is unlimited, we keep queuing more Blocks into internal output queue leading to spill and steep spill curve. **Solution** In ConcurrencyCapBackpressurePolicy, detect internal output queue buildup and then limit the concurrency of the tasks. - Keep the internal output queue history and detect trends in percentage & size in GBs. Based on trends, increase/decrease the concurrency cap. - Given queue based buffering is needed for `preserve_order`, allow adaptive queuing threshold. This would result in Spill, but would flatten out the Spill curve and not cause run away buffering queue growth. ## Related issues > Link related issues: "Fixes ray-project#1234", "Closes ray-project#1234", or "Related to ray-project#1234". ## Additional information > Optional: Add implementation details, API changes, usage examples, screenshots, etc. --------- Signed-off-by: Srinath Krishnamachari <[email protected]>
…#57999) We have a feature flag to control the rolling out of ray export event, but the feature flag is missing the controlling of `StartExportingEvents`. This PR fixes the issue. Test: - CI Signed-off-by: Cuong Nguyen <[email protected]>
…ct#58445) ## Summary Creates a dedicated `tests/unit/` directory for unit tests that don't require Ray runtime or external dependencies. ## Changes - Created `tests/unit/` directory structure - Moved 13 pure unit tests to `tests/unit/` - Added `conftest.py` with fixtures to prevent `ray.init()` and `time.sleep()` - Added `README.md` documenting unit test requirements - Updated `BUILD.bazel` to run unit tests with "small" size tag ## Test Files Moved 1. test_arrow_type_conversion.py 2. test_block.py 3. test_block_boundaries.py 4. test_data_batch_conversion.py 5. test_datatype.py 6. test_deduping_schema.py 7. test_expression_evaluator.py 8. test_expressions.py 9. test_filename_provider.py 10. test_logical_plan.py 11. test_object_extension.py 12. test_path_util.py 13. test_ruleset.py These tests are fast (<1s each), isolated (no Ray runtime), and deterministic (no time.sleep or randomness). --------- Signed-off-by: Balaji Veeramani <[email protected]> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
> Thank you for contributing to Ray! 🚀 > Please review the [Ray Contribution Guide](https://docs.ray.io/en/master/ray-contribute/getting-involved.html) before opening a pull request. >⚠️ Remove these instructions before submitting your PR. > 💡 Tip: Mark as draft if you want early feedback, or ready for review when it's complete. ## Description > Briefly describe what this PR accomplishes and why it's needed. ### [Data] Concurrency Cap Backpressure tuning - Maintain asymmetric EWMA of total queued bytes (this op + downstream) as the typical level: level. - Maintain asymmetric EWMA of absolute residual vs the previous level as a scale proxy: dev = EWMA(|q - level_prev|). - Define deadband: [lower, upper] = [level - K_DEVdev, level + K_DEVdev]. If q > upper -> target cap = running - BACKOFF_FACTOR (back off) If q < lower -> target cap = running + RAMPUP_FACTOR (ramp up) Else -> target cap = running (hold) - Clamp to [1, configured_cap], admit iff running < target cap. ## Related issues > Link related issues: "Fixes ray-project#1234", "Closes ray-project#1234", or "Related to ray-project#1234". ## Additional information > Optional: Add implementation details, API changes, usage examples, screenshots, etc. --------- Signed-off-by: Srinath Krishnamachari <[email protected]> Signed-off-by: Srinath Krishnamachari <[email protected]> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Signed-off-by: Nikhil Ghosh <[email protected]>
… in read-only mode (ray-project#58460) This ensures node type names are correctly reported even when the autoscaler is disabled (read-only mode). ## Description Autoscaler v2 fails to report prometheus metrics when operating in read-only mode on KubeRay with the following KeyError error: ``` 2025-11-08 12:06:57,402 ERROR autoscaler.py:215 -- 'small-group' Traceback (most recent call last): File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/autoscaler.py", line 200, in update_autoscaling_state return Reconciler.reconcile( File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/instance_manager/reconciler.py", line 120, in reconcile Reconciler._step_next( File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/instance_manager/reconciler.py", line 275, in _step_next Reconciler._scale_cluster( File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/instance_manager/reconciler.py", line 1125, in _scale_cluster reply = scheduler.schedule(sched_request) File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/scheduler.py", line 933, in schedule ResourceDemandScheduler._enforce_max_workers_per_type(ctx) File "/home/ray/anaconda3/lib/python3.9/site-packages/ray/autoscaler/v2/scheduler.py", line 1006, in _enforce_max_workers_per_type node_config = ctx.get_node_type_configs()[node_type] KeyError: 'small-group' ``` This happens because the `ReadOnlyProviderConfigReader` populates `ctx.get_node_type_configs()` using node IDs as node types, which is correct for local Ray (where local ray does not have `RAY_NODE_TYPE_NAME` set), but incorrect for KubeRay where `ray_node_type_name` is present and expected with `RAY_NODE_TYPE_NAME` set. As a result, in read-only mode the scheduler sees a node type name (ex. small-group) that never exists in the populated configs. This PR fixes the issue by using `ray_node_type_name` when it exists, and only falling back to node ID when it does not. ## Related issues Fixes ray-project#58227 Signed-off-by: Rueian <[email protected]>
…cess: bool (ray-project#58384) ## Description Pass in `status_code` directly into `do_reply`. This is a follow up to ray-project#58255 ## Related issues ## Additional information --------- Signed-off-by: iamjustinhsu <[email protected]>
ray-project#58395) ## Description there are places in the python code where we use the raw grpc library to make grpc calls (eg: pub-sub, some calls to gcs etc). In the long term we want to fully deprecate grpc library usage in our python code base but as that can take more effort and testing, in this pr I am introducing an interceptor to add auth headers (this will take effect for all grpc calls made from python). ## Testing ### case 1: submitting a job using CLI ``` export RAY_auth_mode="token" export RAY_AUTH_TOKEN="abcdef1234567890abcdef123456789" ray start --head ray job submit -- echo "hi" ``` output ``` ray job submit -- echo "hi" 2025-11-04 06:28:09,122 - INFO - NumExpr defaulting to 4 threads. Job submission server address: http://127.0.0.1:8265 ------------------------------------------------------- Job 'raysubmit_1EV8q86uKM24nHmH' submitted successfully ------------------------------------------------------- Next steps Query the logs of the job: ray job logs raysubmit_1EV8q86uKM24nHmH Query the status of the job: ray job status raysubmit_1EV8q86uKM24nHmH Request the job to be stopped: ray job stop raysubmit_1EV8q86uKM24nHmH Tailing logs until the job exits (disable with --no-wait): 2025-11-04 06:28:10,363 INFO job_manager.py:568 -- Runtime env is setting up. hi Running entrypoint for job raysubmit_1EV8q86uKM24nHmH: echo hi ------------------------------------------ Job 'raysubmit_1EV8q86uKM24nHmH' succeeded ------------------------------------------ ``` ### case 2: submitting a job with actors and tasks and verifying on dashboard test.py ```python import time import ray from ray._raylet import Config ray.init() @ray.remote def print_hi(): print("Hi") time.sleep(2) @ray.remote class SimpleActor: def __init__(self): self.value = 0 def increment(self): self.value += 1 return self.value actor = SimpleActor.remote() result = ray.get(actor.increment.remote()) for i in range(100): ray.get(print_hi.remote()) time.sleep(20) ray.shutdown() ``` ``` export RAY_auth_mode="token" export RAY_AUTH_TOKEN="abcdef1234567890abcdef123456789" python test.py ``` ### dashboard screenshots: #### promts user to input the token <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/008829d8-51b6-445a-b135-5f76b6ccf292" /> ### on passing the right token: overview page <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/cece0da7-0edd-4438-9d60-776526b49762" /> job page: tasks are listed <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/b98eb1d9-cacc-45ea-b0e2-07ce8922202a" /> task page <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/09ff38e1-e151-4e34-8651-d206eb8b5136" /> actors page <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/10a30b3d-3f7e-4f3d-b669-962056579459" /> specific actor page <img width="1720" height="1073" alt="image" src="https://github.com/user-attachments/assets/ab1915bd-3d1b-4813-8101-a219432a55c0" /> --------- Signed-off-by: sampan <[email protected]> Co-authored-by: sampan <[email protected]>
Currently are deprecation warnings sometimes not informative enough. The the warning is triggered it does not tell us *where* the deprecated feature is used. For example, ray internally raises a deprecation warning when an `RLModuleConfig` is initialized. ```python >>> from ray.rllib.core.rl_module.rl_module import RLModuleConfig >>> RLModuleConfig() 2025-11-02 18:21:27,318 WARNING deprecation.py:50 -- DeprecationWarning: `RLModule(config=[RLModuleConfig object])` has been deprecated. Use `RLModule(observation_space=.., action_space=.., inference_only=.., model_config=.., catalog_class=..)` instead. This will raise an error in the future! ``` This is confusing, where did *I* use a config, what am I doing wrong? This raises issues like: https://discuss.ray.io/t/warning-deprecation-py-50-deprecationwarning-rlmodule-config-rlmoduleconfig-object-has-been-deprecated-use-rlmodule-observation-space-action-space-inference-only-model-config-catalog-class-instead/23064 Tracing where the error actually happens is tedious - is it my code or internal? The output just shows `deprecation.:50`. Not helpful. This PR adds a stacklevel option with stacklevel=2 as the default to all `deprecation_warning`s. So devs and users can better see where is the deprecated option actually used. --- EDIT: **Before** ```python WARNING deprecation.py:50 -- DeprecationWarning: `RLModule(config=[RLModuleConfig object])` ``` **After** module.py:line where the deprecated artifact is used is shown in the log output: When building an Algorithm: ```python WARNING rl_module.py:445 -- DeprecationWarning: `RLModule(config=[RLModuleConfig object])` has been deprecated. Use `RLModule(observation_space=.., action_space=.., inference_only=.., model_config=.., catalog_class=..)` instead. This will raise an error in the future! ``` ```python .../ray/tune/logger/unified.py:53: RayDeprecationWarning: This API is deprecated and may be removed in future Ray releases. You could suppress this warning by setting env variable PYTHONWARNINGS="ignore::DeprecationWarning" ``` Signed-off-by: Daraan <[email protected]>
Fix some vale's error and suggestions on the kai-scheduler document. See ray-project#58161 (comment) Signed-off-by: fscnick <[email protected]>
> Briefly describe what this PR accomplishes and why it's needed. Our serve ingress keeps running into below error related to `uvloop` under heavy load ``` File descriptor 97 is used by transport ``` The uvloop team have a [PR](MagicStack/uvloop#646) to fix it, but seems like no one is working on it One of workaround mentioned in the ([PR](MagicStack/uvloop#646 (comment))) is to just turn off uvloop . We tried it in our env and didn't see any major performance difference Hence as part of this PR, we are defining a new env for controlling UVloop Signed-off-by: jugalshah291 <[email protected]>
- `test_target_capacity` windows test is failing, possibly because we have put up a short timeout of 10 seconds, increasing it to verify whether timeout is an issue or not. Signed-off-by: harshit <[email protected]>
so that they are not called lints any more Signed-off-by: Lonnie Liu <[email protected]>
…ject#57233) Update remaining mulitmodal release tests to use new depsets.
…y-project#58441) ## Description Currently, we clear _external_ queues when an operator is manually marked as finished. But we don't clear their _internal_ queues. This PR fixes that ## Related issues Fixes this test https://buildkite.com/ray-project/postmerge/builds/14223#019a5791-3d46-4ab8-9f97-e03ea1c04bb0/642-736 ## Additional information --------- Signed-off-by: iamjustinhsu <[email protected]>
be consistent with doc build environment Signed-off-by: Lonnie Liu <[email protected]>
migrating all doc related things to run on python 3.12 Signed-off-by: Lonnie Liu <[email protected]>
excluding `*_tests` directories for now to reduce the impact Signed-off-by: Lonnie Liu <[email protected]>
using `bazelisk run //java:gen_ray_java_pkg` everywhere Signed-off-by: Lonnie Liu <[email protected]>
This PR adds 2 new metrics to core_worker by way of the reference counter. The two new metrics keep track of the count and size of objects owned by the worker as well as keeping track of their states. States are defined as: - **PendingCreation**: An object that is pending creation and hasn't finished it's initialization (and is sizeless) - **InPlasma**: An object which has an assigned node address and isn't spilled - **Spilled**: An object which has an assigned node address and is spilled - **InMemory**: An object which has no assigned address but isn't pending creation (and therefore, must be local) The approach used by these new metrics is to examine the state 'before and after' any mutations on the reference in the reference_counter. This is required in order to do the appropriate bookkeeping (decrementing values and incrementing others). Admittedly, there is potential for counting on the in between decrements/increments depending on when the RecordMetrics loop is run. This unfortunate side effect however seems preferable to doing mutual exclusion with metric collection as this is potentially a high throughput code path. In addition, performing live counts seemed preferable then doing full accounting of the object store and across all references at time of metric collection. Reason being, that potentially the reference counter is tracking millions of objects, and each metric scan could potentially be very expensive. So running the accounting (despite being potentially innaccurate for short periods) seemed the right call. This PR also allows for object size to potentially change due to potential non deterministic instantation (say an object is initially created, but it's primary copy dies, and then the recreation fails). This is an edge case, but seems important for completeness sake. --------- Signed-off-by: zac <[email protected]>
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Summary of ChangesHello @antfin-oss, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request integrates the latest changes from the Highlights
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Code Review
This pull request represents a massive and impressive refactoring of the CI/CD and build systems. The changes significantly improve modularity, maintainability, and adopt modern best practices across the board. Key improvements include the modularization of the build pipeline, the introduction of a new dependency management system (raydepsets), the switch to uv and pre-commit for better linting and dependency handling, and the simplification of the Bazel build configuration. The refactoring is consistent and well-executed across all the changes. I have reviewed the changes in detail and found no issues. This is an excellent contribution to the project's health.
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This Pull Request was created automatically to merge the latest changes from
masterintomainbranch.📅 Created: 2025-11-11
🔀 Merge direction:
master→main🤖 Triggered by: Scheduled
Please review and merge if everything looks good.