🔄 daily merge: master → main 2025-11-12 #675
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…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 <alexyang@anyscale.com>
<!-- 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 <alexyang@anyscale.com>
…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 <zakelly.lan@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
…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 <israbbani@gmail.com>
…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 <jasli1909@gmail.com> Signed-off-by: Jason Li <57246540+JasonLi1909@users.noreply.github.com> Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
> 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 <joshlee@anyscale.com>
…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 <justinvyu@anyscale.com>
## Description Fix the typing for UDFs. This should not accept an instance as it is currently defined. Signed-off-by: Matthew Owen <mowen@anyscale.com>
…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 <bveeramani@berkeley.edu>
…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 <elliot.barnwell@anyscale.com>
## 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 <mowen@anyscale.com>
with comments to github issues Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
otherwise, the ordering or messages looks strange on windows. Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
Updates the vicuna lightning deepspeed example to run w/ Train V2. --------- Signed-off-by: Justin Yu <justinvyu@anyscale.com>
…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 <ak@anyscale.com>
…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 <srinath.krishnamachari@anyscale.com>
…#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 <can@anyscale.com>
otherwise they are failing windows core python tests Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
…y-project#58023) > 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 - Only increase threshold When `_update_queue_threshold` to adjust the queue threshold to cap concurrency based on current queued bytes, - Only allow increasing the threshold or maintaining it. - Cannot decrease threshold because the steady state of queued bytes is not known. ## 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 <srinath.krishnamachari@anyscale.com> Signed-off-by: Srinath Krishnamachari <68668616+srinathk10@users.noreply.github.com>
combining all depset checks into a single job TODO: add raydepset feature to build all depsets for the depset graph --------- Signed-off-by: elliot-barn <elliot.barnwell@anyscale.com>
- default deployment name was changed to _TaskConsumerWrapper after async inference implementation, fixed it now Signed-off-by: harshit <harshit@anyscale.com>
…#58033) ## Description This change properly handles of pushing of the renaming projections into read ops (that support projections, like parquet reads). ## 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 <ak@anyscale.com>
…-project#58011) Signed-off-by: Kourosh Hakhamaneshi <Kourosh@anyscale.com>
## Description
This PR adds support for reading Unity Catalog Delta tables in Ray Data
with automatic credential vending. This enables secure, temporary access
to Delta Lake tables stored in Databricks Unity Catalog without
requiring users to manage cloud credentials manually.
### What's Added
- **`ray.data.read_unity_catalog()`** - Updated public API for reading
Unity Catalog Delta tables
- **`UnityCatalogConnector`** - Handles Unity Catalog REST API
integration and credential vending
- **Multi-cloud support** - Works with AWS S3, Azure Data Lake Storage,
and Google Cloud Storage
- **Automatic credential management** - Obtains temporary,
least-privilege credentials via Unity Catalog API
- **Delta Lake integration** - Properly configures PyArrow filesystem
for Delta tables with session tokens
### Key Features
✅ **Production-ready credential vending API** - Uses stable, public
Unity Catalog APIs
✅ **Secure by default** - Temporary credentials with automatic cleanup
✅ **Multi-cloud** - AWS (S3), Azure (Blob Storage), and GCP (Cloud
Storage)
✅ **Delta Lake optimized** - Handles session tokens and PyArrow
filesystem configuration
✅ **Comprehensive error handling** - Helpful messages for common issues
(deletion vectors, permissions, etc.)
✅ **Full logging support** - Debug and info logging throughout
### Usage Example
```python
import ray
# Read a Unity Catalog Delta table
ds = ray.data.read_unity_catalog(
table="main.sales.transactions",
url="https://dbc-XXXXXXX-XXXX.cloud.databricks.com",
token="dapi...",
region="us-west-2" # Optional, for AWS
)
# Use standard Ray Data operations
ds = ds.filter(lambda row: row["amount"] > 100)
ds.show(5)
```
### Implementation Notes
This is a **simplified, focused implementation** that:
- Supports **Unity Catalog tables only** (no volumes - that's in private
preview)
- Assumes **Delta Lake format** (most common Unity Catalog use case)
- Uses **production-ready APIs** only (no private preview features)
- Provides ~600 lines of clean, reviewable code
The full implementation with volumes and multi-format support is
available in the `data_uc_volumes` branch and can be added in a future
PR once this foundation is reviewed.
### Testing
- ✅ All ruff lint checks pass
- ✅ Code formatted per Ray standards
- ✅ Tested with real Unity Catalog Delta tables on AWS S3
- ✅ Proper PyArrow filesystem configuration verified
- ✅ Credential vending flow validated
## Related issues
Related to Unity Catalog and Delta Lake support requests in Ray Data.
## Additional information
### Architecture
The implementation follows the **connector pattern** rather than a
`Datasource` subclass because Unity Catalog is a metadata/credential
layer, not a data format. The connector:
1. Fetches table metadata from Unity Catalog REST API
2. Obtains temporary credentials via credential vending API
3. Configures cloud-specific environment variables
4. Delegates to `ray.data.read_delta()` with proper filesystem
configuration
### Delta Lake Special Handling
Delta Lake on AWS requires explicit PyArrow S3FileSystem configuration
with session tokens (environment variables alone are insufficient). This
implementation correctly creates and passes the filesystem object to the
`deltalake` library.
### Cloud Provider Support
| Provider | Credential Type | Implementation |
|----------|----------------|----------------|
| AWS S3 | Temporary IAM credentials | PyArrow S3FileSystem with session
token |
| Azure Blob | SAS tokens | Environment variables
(AZURE_STORAGE_SAS_TOKEN) |
| GCP Cloud Storage | OAuth tokens / Service account | Environment
variables (GCP_OAUTH_TOKEN, GOOGLE_APPLICATION_CREDENTIALS) |
### Error Handling
Comprehensive error messages for common issues:
- **Deletion Vectors**: Guidance on upgrading deltalake library or
disabling the feature
- **Column Mapping**: Compatibility information and solutions
- **Permissions**: Clear list of required Unity Catalog permissions
- **Credential issues**: Detailed troubleshooting steps
### Future Enhancements
Potential follow-up PRs:
- Unity Catalog volumes support (when out of private preview)
- Multi-format support (Parquet, CSV, JSON, images, etc.)
- Custom datasource integration
- Advanced Delta Lake features (time travel, partition filters)
### Dependencies
- Requires `deltalake` package for Delta Lake support
- Uses standard Ray Data APIs (`read_delta`, `read_datasource`)
- Integrates with existing PyArrow filesystem infrastructure
### Documentation
- Full docstrings with examples
- Type hints throughout
- Inline comments with references to external documentation
- Comprehensive error messages with actionable guidance
---------
Signed-off-by: soffer-anyscale <stephen.offer@anyscale.com>
…ease test (ray-project#58048) ## Summary This PR removes the `image_classification_chaos_no_scale_back` release test and its associated setup script (`setup_cluster_compute_config_updater.py`). This test has become non-functional and is no longer providing useful signal. ## Background The `image_classification_chaos_no_scale_back` release test was designed to validate Ray Data's fault tolerance when many nodes abruptly get preempted at the same time. The test worked by: 1. Running on an autoscaling cluster with 1-10 nodes 2. Updating the compute config mid-test to downscale to 5 nodes 3. Asserting that there are dead nodes as a sanity check ## Why This Test Is Broken After the removal of Parquet metadata fetching in ray-project#56105 (September 2, 2025), the autoscaling behavior changed significantly: - **Before metadata removal**: The cluster would autoscale more aggressively because metadata fetching created additional tasks that triggered faster scale-up. The cluster would scale past 5 nodes, then downscale, leaving dead nodes that the test could detect. - **After metadata removal**: Without the metadata fetching tasks, the cluster doesn't scale up fast enough to get past 5 nodes before the downscale happens. This means there are no dead nodes to detect, causing the test to fail. ## Why We're Removing It 1. **Test is fundamentally broken**: The test's assumptions about autoscaling behavior are no longer valid after the metadata fetching removal 2. **Not actively monitored**: The test is marked as unstable and isn't closely watched ## Changes - Removed `image_classification_chaos_no_scale_back` test from `release/release_data_tests.yaml` - Deleted `release/nightly_tests/setup_cluster_compute_config_updater.py` (only used by this test) ## Related See ray-project#56105 Fixes ray-project#56528 Signed-off-by: Balaji Veeramani <bveeramani@berkeley.edu>
These numbers are outdated, and the ones we report are not very useful. We will refresh them soon. Signed-off-by: Edward Oakes <ed.nmi.oakes@gmail.com>
…54857) Signed-off-by: EkinKarabulut <ekarabulut@nvidia.com> Signed-off-by: EkinKarabulut <82878945+EkinKarabulut@users.noreply.github.com> Signed-off-by: Rueian <rueiancsie@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: angelinalg <122562471+angelinalg@users.noreply.github.com> Co-authored-by: fscnick <6858627+fscnick@users.noreply.github.com> Co-authored-by: Jiajun Yao <jeromeyjj@gmail.com> Co-authored-by: Rueian <rueiancsie@gmail.com>
## Description https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy <img width="772" height="270" alt="Screenshot 2025-10-18 at 3 14 36 PM" src="https://github.com/user-attachments/assets/d9cbf986-4271-41e6-9c4c-96201d32d1c6" /> `zero_copy_only` is actually default to True, so we should explicit pass False, for pyarrow version < 13.0.0 https://github.com/ray-project/ray/blob/1e38c9408caa92c675f0aa3e8bb60409c2d9159f/python/ray/data/_internal/arrow_block.py#L540-L546 ## Related issues Closes ray-project#57819 ## Additional information > Optional: Add implementation details, API changes, usage examples, screenshots, etc. --------- Signed-off-by: You-Cheng Lin (Owen) <mses010108@gmail.com> Signed-off-by: You-Cheng Lin <youchenglin@youchenglin-L3DPGF50JG.local> Signed-off-by: You-Cheng Lin <106612301+owenowenisme@users.noreply.github.com> Signed-off-by: You-Cheng Lin <mses010108@gmail.com> Co-authored-by: You-Cheng Lin <youchenglin@youchenglin-L3DPGF50JG.local>
…#58025) Signed-off-by: Kourosh Hakhamaneshi <Kourosh@anyscale.com>
…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 <jhsu@anyscale.com>
be consistent with doc build environment Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
migrating all doc related things to run on python 3.12 Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
excluding `*_tests` directories for now to reduce the impact Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
using `bazelisk run //java:gen_ray_java_pkg` everywhere Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
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 <zac@anyscale.com>
to 0.21.0; supports wanda priority now. Signed-off-by: Lonnie Liu <lonnie@anyscale.com>
…8498) Signed-off-by: Seiji Eicher <seiji@anyscale.com>
…#58286) ## Description Predicate pushdown (ray-project#58150) in conjunction with this PR should speed up reads from Iceberg. Once the above change lands, we can add the pushdown interface support for IcebergDatasource --------- Signed-off-by: Goutam <goutam@anyscale.com>
## Description * Does the work to bump pydoclint up to the latest version * And allowlist any new violations it finds ## Related issues n/a ## Additional information n/a --------- Signed-off-by: Thomas Desrosiers <thomas@anyscale.com>
fix pattern_async_actor demo typo. Add `self.`. --------- Signed-off-by: curiosity-hyf <curiooosity.h@gmail.com>
…hboard agent (ray-project#58405) Add a grpc service interceptor to intercept all dashboard agent rpc calls and validate the presence of auth token (when auth mode is token) --------- Signed-off-by: sampan <sampan@anyscale.com> Signed-off-by: Edward Oakes <ed.nmi.oakes@gmail.com> Co-authored-by: sampan <sampan@anyscale.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
…tests (ray-project#58528) the auth token test setup in `conftest.py` is breaking macos test. there are two test scripts (`test_microbenchmarks.py` and `test_basic.py`) that run after the wheel is installed but without editable mode. for these test to pass,` conftest.py` cannot import anything under `ray.tests`. this pr moves `authentication_test_utils` into `ray._private` to fix this issue Signed-off-by: sampan <sampan@anyscale.com> Co-authored-by: sampan <sampan@anyscale.com>
This PR enables open telemetry as the default backend for ray metric stack. The bulk of this PR is actually to fix tests that were written with some assumptions that no longer hold true. For ease of reviewing, I inline the reasons for the change together with the change for each tests in the comments. This PR also depends on a release of vllm (so that we can update the minimal supported version of vllm in ray). Test: - CI <!-- CURSOR_SUMMARY --> --- > [!NOTE] > Enable OpenTelemetry metrics backend by default and refactor metrics/Serve tests to use timeseries APIs and updated `ray_serve_*` metric names. > > - **Core/Config**: > - Default-enable OpenTelemetry: set `RAY_enable_open_telemetry` to `true` in `ray_constants.py` and `ray_config_def.h`. > - Metrics `Counter`: use `CythonCount` by default; keep legacy `CythonSum` only when OTEL is explicitly disabled. > - **Serve/Metrics Tests**: > - Replace text scraping with `PrometheusTimeseries` and `fetch_prometheus_metric_timeseries` throughout. > - Update metric names/tags to `ray_serve_*` and counter suffixes `*_total`; adjust latency metric names and processing/queued gauges. > - Reduce ad-hoc HTTP scrapes; plumb a reusable `timeseries` object and pass through helpers. > - **General Test Fixes**: > - Remove OTEL parametrization/fixtures; simplify expectations where counters-as-gauges no longer apply; drop related tests. > - Cardinality tests: include `"low"` level and remove OTEL gating; stop injecting `enable_open_telemetry` in system config. > - Actor/state/thread tests: migrate to cluster fixtures, wait for dashboard agent, and adjust expected worker thread counts. > - **Build**: > - Remove OTEL-specific Bazel test shard/env overrides; clean OTEL env from C++ stats test. > > <sup>Written by [Cursor Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit 1d0190f. This will update automatically on new commits. Configure [here](https://cursor.com/dashboard?tab=bugbot).</sup> <!-- /CURSOR_SUMMARY --> --------- Signed-off-by: Cuong Nguyen <can@anyscale.com>
…mmended (ray-project#57726) <!-- 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 If users schedule a detached actor into a placement group, Raylet will kill the actor when the placement group is removed. The actor will be stuck in the `RESTARTING` state forever if it's restartable until users explicitly kill it. In that case, if users try to `get_actor` with the actor's name, it can still return the restarting actor, but no process exists. It will no longer be restarted because the PG is gone, and no PG with the same ID will be created during the cluster's lifetime. The better behavior would be for Ray to transition a task/actor's state to dead when it is impossible to restart. However, this would add too much complexity to the core, so I think it's not worth it. Therefore, this PR adds a warning log, and users should use detached actors or PGs correctly. Example: Run the following script and run `ray list actors`. ```python import ray from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy from ray.util.placement_group import placement_group, remove_placement_group @ray.remote(num_cpus=1, lifetime="detached", max_restarts=-1) class Actor: pass ray.init() pg = placement_group([{"CPU": 1}]) ray.get(pg.ready()) actor = Actor.options( scheduling_strategy=PlacementGroupSchedulingStrategy( placement_group=pg, ) ).remote() ray.get(actor.__ray_ready__.remote()) ``` ## 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 ✨ - [x] Enhancement 🚀 - [ ] Code refactoring 🔧 - [ ] Documentation update 📖 - [ ] Chore 🧹 - [ ] Style 🎨 ## Checklist **Does this PR introduce breaking changes?** - [ ] Yes⚠️ - [x] No <!-- If yes, describe what breaks and how users should migrate --> **Testing:** - [ ] Added/updated tests for my changes - [x] Tested the changes manually - [ ] This PR is not tested ❌ _(please explain why)_ **Code Quality:** - [x] Signed off every commit (`git commit -s`) - [x] 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: Kai-Hsun Chen <khchen@x.ai> Signed-off-by: Robert Nishihara <robertnishihara@gmail.com> Signed-off-by: Kai-Hsun Chen <kaihsun@apache.org> Co-authored-by: Robert Nishihara <robertnishihara@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
…58182) Signed-off-by: dayshah <dhyey2019@gmail.com>
…y-project#57715) Signed-off-by: Nikhil Ghosh <nikhil@anyscale.com> Signed-off-by: Nikhil G <nrghosh@users.noreply.github.com>
Signed-off-by: dayshah <dhyey2019@gmail.com>
…-project#56783) Signed-off-by: dayshah <dhyey2019@gmail.com>
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Code Review
This is a massive automated PR that refactors large parts of the build and CI system. The changes are generally positive, moving towards more modularity, standard tooling (e.g. pkg_* rules, pre-commit), and optimizations (pre-built images). I've pointed out several areas where test coverage seems to have been reduced, which should be addressed. I also have a couple of questions about specific changes to ensure they are intentional and don't introduce issues. Overall, this is a great step forward for the project's infrastructure.
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This pull request has been automatically marked as stale because it has not had You can always ask for help on our discussion forum or Ray's public slack channel. If you'd like to keep this open, just leave any comment, and the stale label will be removed. |
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This Pull Request was created automatically to merge the latest changes from
masterintomainbranch.📅 Created: 2025-11-12
🔀 Merge direction:
master→main🤖 Triggered by: Scheduled
Please review and merge if everything looks good.