[Core] Add `fallback_strategy` API to Task/Actor remote options by ryanaoleary · Pull Request #56374 · ray-project/ray

ryanaoleary · 2025-09-09T12:08:28Z

Why are these changes needed?

This PR contains only the python changes from #56369, adding fallback_strategy as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is label_selector.

Example using fallback_strategy to schedule on different instance types:

@ray.remote(
    label_selector={"instance_type": "m5.16xlarge"},
    fallback_strategy=[
        # Fall back to selector for a "m5.large" instance type if "m5.16xlarge"
        # cannot be satisfied.
        {"label_selector": {"instance_type": "m5.large"}},
        # Finally, fall back to an empty set of labels (no constraints).
        # neither desired m5 type can be sastisfied.
        {"label_selector": {}},
    ],
)
class A:
    pass

In the above field, first the label_selector field will be tried. Then, the scheduler will iterate through each dict in fallback_strategy and attempt to scheduling using the label selector specified there (first {"instance_type": "m5.large"} and then the empty set). The first satisfied label_selector will be scheduled.

Related issue number

#51564

Checks

I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
I've run scripts/format.sh to lint the changes in this PR.
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 :(

Note

Introduces experimental fallback_strategy (list of label selectors) for task/actor scheduling, with validation and unit tests.

Core:
- Options/Validation: Add fallback_strategy to _common_options in python/ray/_common/ray_option_utils.py; implement validate_fallback_strategy in python/ray/_private/label_utils.py.
- Scheduling API: Extend ray.remote overload and docstrings in python/ray/_private/worker.py, python/ray/actor.py, and python/ray/remote_function.py to accept and propagate fallback_strategy alongside label_selector.
Tests:
- Add test_decorator_fallback_strategy_args in python/ray/tests/test_actor.py and test_validate_fallback_strategy in python/ray/tests/test_label_utils.py.
CI:
- Update ci/lint/pydoclint-baseline.txt for new/changed docstrings.

^{Written by Cursor Bugbot for commit 9ef798c. This will update automatically on new commits. Configure here.}

gemini-code-assist

Code Review

This pull request introduces a fallback_strategy API to the remote options for Tasks and Actors, enhancing scheduling flexibility. The implementation is well-structured, adding validation logic and comprehensive tests for the new feature. My review focuses on improving documentation consistency and correcting a minor inaccuracy in a test comment to ensure clarity and maintainability.

python/ray/actor.py

python/ray/_private/worker.py

python/ray/tests/test_label_scheduling.py

MengjinYan

Looks good!

python/ray/_private/worker.py

python/ray/actor.py

MengjinYan · 2025-09-16T20:12:21Z

There seems to be lint failure. The java test failure should be unrelated. cc: @ryanaoleary

ryanaoleary · 2025-09-17T09:15:27Z

There seems to be lint failure. The java test failure should be unrelated. cc: @ryanaoleary

There were some pydoc lint failures, should be fixed with d377805

edoakes · 2025-09-26T16:54:35Z

Test failures @ryanaoleary

Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Update python/ray/actor.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Update python/ray/actor.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Update python/ray/tests/test_label_scheduling.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Update python/ray/_private/worker.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Add remaining changes Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Remove arg in create_actor Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Update python/ray/actor.py Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Update python/ray/actor.py Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> Update python/ray/_private/worker.py Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Signed-off-by: Ryan O'Leary <113500783+ryanaoleary@users.noreply.github.com> fix lint and pydoc Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Undo pydoclint change Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Update pydoc baseline Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Fix label util function Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Fix test and lint Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Fix remote function pydoc Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Undo pydoc lint baseline changes Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Fix baseline Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Fix pydoc lint errors Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Re-run linters and regenerate baseline Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

ryanaoleary · 2025-10-03T03:40:48Z

@MengjinYan I think this PR is ready to merge since CI is now passing.

edoakes · 2025-10-03T19:31:57Z

@MengjinYan @ryanaoleary from the REP, fallback strategy should be a list of dicts of decorator options that are overridden in tandem. To start with, it would only support label_selector as a key within those dicts. Here, it is accepting only the label selector as the dictionary directly.

ryanaoleary · 2025-10-04T00:13:27Z

@MengjinYan @ryanaoleary from the REP, fallback strategy should be a list of dicts of decorator options that are overridden in tandem. To start with, it would only support label_selector as a key within those dicts. Here, it is accepting only the label selector as the dictionary directly.

@edoakes Sorry I must have misread it when I was implementing it, so rather than a fallback_strategy like this:

fallback_strategy=[
         {"ray.io/label-selector-1": "value1"}, 
         {"ray.io/label-selector-2": "value2"}, 
},

where it's just a list of label selectors that we iterate through to try, it should be formatted like this:

fallback_strategy=[
       {"label_selector": {"ray.io/label-selector-1": "value1"}, .... <some-other-fields-in-the-future>},
       {"label_selector": {"ray.io/label-selector-2": "value2"}},
   ],

If the above looks correct I'll fix this PR to follow that format asap.

edoakes · 2025-10-04T15:32:47Z

@ryanaoleary yes that's right. And the reason for this is to open the door to supporting coordinated fallback with other overrides (like resources) in the future.

Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

ryanaoleary · 2025-10-06T10:31:09Z

@ryanaoleary yes that's right. And the reason for this is to open the door to supporting coordinated fallback with other overrides (like resources) in the future.

Sounds good, should be fixed with 9cca330.

Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

jjyao · 2025-10-06T23:46:27Z

@ryanaoleary could you update the RP description with a few examples of how fallback_strategy should be used?

ryanaoleary · 2025-10-07T02:52:05Z

@ryanaoleary could you update the RP description with a few examples of how fallback_strategy should be used?

Sounds good, updated the PR description with an example and explanation.

ryanaoleary · 2025-10-07T02:58:02Z

The failing CI test appears unrelated and passes when I run it locally with this PR:

python -m pytest -v -s test_sql.py::test_databricks_u
c_datasource_empty_result
/usr/local/google/home/ryanaoleary/miniconda3/envs/myenv/lib/python3.11/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.
  "cipher": algorithms.TripleDES,
/usr/local/google/home/ryanaoleary/miniconda3/envs/myenv/lib/python3.11/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.
  "class": algorithms.TripleDES,
Test session starts (platform: linux, Python 3.11.13, pytest 7.4.4, pytest-sugar 0.9.5)
cachedir: .pytest_cache
rootdir: /usr/local/google/home/ryanaoleary/Desktop/forks/ray
configfile: pytest.ini
plugins: sphinx-0.5.1.dev0, rerunfailures-11.1.2, mock-3.14.0, virtualenv-1.8.1, pytest_httpserver-1.1.3, lazy-fixtures-1.1.2, shutil-1.8.1, docker-tools-3.1.3, timeout-2.1.0, anyio-3.7.1, sugar-0.9.5, forked-1.4.0, aiohttp-1.1.0, asyncio-0.17.2
timeout: 180.0s
timeout method: signal
timeout func_only: False
asyncio: mode=Mode.AUTO
collecting ... 2025-10-07 02:56:59,604  INFO worker.py:2004 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 
2025-10-07 02:57:00,607 INFO databricks_uc_datasource.py:72 -- Waiting for query 'select * from dummy_table' execution result.

 python/ray/data/tests/test_sql.py::test_databricks_uc_datasource_empty_result ✓                                  100% ██████████

Results (5.20s):
       1 passed

edoakes · 2025-10-07T16:11:40Z

merged master to re-run CI, enabling auto-merge

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com> Signed-off-by: Josh Kodi <joshkodi@gmail.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com> Signed-off-by: xgui <xgui@anyscale.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com> Signed-off-by: Aydin Abiar <aydin@anyscale.com>

…project#56374) This PR contains only the python changes from ray-project#56369, adding `fallback_strategy` as an option to the remote decorator of Tasks/Actors. Fallback strategy consists of a list of dict of decorator options. The dict of decorator options are evaluated together, and the first satisfied strategy dict is scheduled. With this PR, the only supported option is `label_selector`. Example using `fallback_strategy` to schedule on different instance types: ``` @ray.remote( label_selector={"instance_type": "m5.16xlarge"}, fallback_strategy=[ # Fall back to selector for a "m5.large" instance type if "m5.16xlarge" # cannot be satisfied. {"label_selector": {"instance_type": "m5.large"}}, # Finally, fall back to an empty set of labels (no constraints). # neither desired m5 type can be sastisfied. {"label_selector": {}}, ], ) class A: pass ``` In the above field, first the `label_selector` field will be tried. Then, the scheduler will iterate through each dict in `fallback_strategy` and attempt to scheduling using the label selector specified there (first `{"instance_type": "m5.large"}` and then the empty set). The first satisfied `label_selector` will be scheduled. ray-project#51564 --------- Signed-off-by: Ryan O'Leary <ryanaoleary@google.com> Co-authored-by: Mengjin Yan <mengjinyan3@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com> Signed-off-by: Future-Outlier <eric901201@gmail.com>

ryanaoleary requested review from a team, edoakes and jjyao as code owners September 9, 2025 12:08

ryanaoleary mentioned this pull request Sep 9, 2025

[Core] Add fallback strategy scheduling logic #56369

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[Core] Ray Label Selector API Implementation Tracker #51564

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ryanaoleary force-pushed the python-api-fallback-strategy branch 2 times, most recently from daed44c to fe58310 Compare September 25, 2025 23:18

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ryanaoleary and others added 4 commits September 26, 2025 21:26

Merge branch 'master' into python-api-fallback-strategy

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Re-run linters

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Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

Merge branch 'master' into python-api-fallback-strategy

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Merge branch 'master' into python-api-fallback-strategy

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ryanaoleary and others added 2 commits October 6, 2025 10:30

Fix fallback_strategy structure

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Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

Merge branch 'master' into python-api-fallback-strategy

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ryanaoleary and others added 2 commits October 6, 2025 12:20

Add better validation

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Merge branch 'master' into python-api-fallback-strategy

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edoakes merged commit 9b0dc60 into ray-project:master Oct 7, 2025
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