Refactor classes - highlight standalone modules #444
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WalkthroughThe pull request introduces a comprehensive restructuring of import paths across multiple files in the Changes
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Actionable comments posted: 10
🧹 Outside diff range and nitpick comments (17)
executorlib/standalone/__init__.py (1)
Line range hint
12-22: Consider reordering all list to match import order.While the all list correctly includes all imported symbols, consider reordering them to match the import order for better maintainability.
__all__ = [ SocketInterface, interface_bootup, interface_connect, - interface_send, - interface_shutdown, interface_receive, + interface_send, + interface_shutdown, + MpiExecSpawner, + SrunSpawner, RaisingThread, - MpiExecSpawner, - SrunSpawner, ]executorlib/standalone/queue.py (1)
4-11: Improve docstring formatting.The docstring has a line break that splits a sentence, affecting readability. Consider reformatting it and adding a return value section (even if void) for completeness.
def cancel_items_in_queue(que: queue.Queue): """ - Cancel items which are still waiting in the queue. If the executor is busy tasks remain in the queue, so the future - objects have to be cancelled when the executor shuts down. + Cancel items which are still waiting in the queue. If the executor is busy, + tasks remain in the queue, so the future objects have to be cancelled when + the executor shuts down. Args: que (queue.Queue): Queue with task objects which should be executed + + Returns: + None """executorlib/cache/backend.py (1)
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1-83: Good architectural direction with modular separation.The restructuring improves the codebase organization by:
- Isolating generic HDF operations in
standalone- Grouping cache-related functionality
- Making dependencies more explicit
This will make the codebase more maintainable and easier to understand.
tests/test_cache_hdf.py (1)
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22-63: Consider improving cache directory handling in tests.While the tests are well-structured, the cache directory handling could be more robust:
- Using absolute paths with
os.path.abspath("cache")multiple times creates duplication- The cache directory path could be standardized as a class variable
- The
exist_ok=Trueflag might mask potential issuesConsider refactoring the test class like this:
class TestSharedFunctions(unittest.TestCase): + def setUp(self): + self.cache_dir = os.path.abspath("cache") + if os.path.exists(self.cache_dir): + shutil.rmtree(self.cache_dir) + os.makedirs(self.cache_dir) + def test_hdf_mixed(self): - cache_directory = os.path.abspath("cache") - os.makedirs(cache_directory, exist_ok=True) - file_name = os.path.join(cache_directory, "test_mixed.h5") + file_name = os.path.join(self.cache_dir, "test_mixed.h5")executorlib/standalone/serialize.py (1)
9-28: Consider documenting usage patterns in module docstring.Since this function is part of the standalone modules and is used across multiple test cases, it would be beneficial to add module-level documentation explaining common usage patterns and integration points with the executor framework.
Consider adding this module-level docstring at the top of the file:
""" Serialization utilities for the executor framework. This module provides standalone serialization capabilities, including: - Function serialization to HDF5 format - Cloudpickle configuration for distributed execution - Hash generation for serialized content Usage: from executorlib.standalone.serialize import cloudpickle_register # Register the current module for pickling by value cloudpickle_register() # Or specify a specific stack level cloudpickle_register(ind=1) """executorlib/backend/interactive_parallel.py (2)
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82-89: Consider improving error handling for non-zero MPI ranks.The error handling block only sends errors to the socket when
mpi_rank_zerois true. However, errors occurring in non-zero ranks might be silently ignored. Consider gathering and reporting errors from all ranks.Here's a suggested improvement:
except Exception as error: + error_reply = MPI.COMM_WORLD.gather(error, root=0) if mpi_rank_zero: interface_send( socket=socket, - result_dict={"error": error, "error_type": str(type(error))}, + result_dict={"error": error_reply, "error_type": [str(type(e)) for e in error_reply if e is not None]}, )
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15-23: Consider updating docstring to reflect architectural context.The main function's docstring could be enhanced to describe its role within the standalone architecture and its relationship with the MPI-based parallel execution model.
Consider expanding the docstring:
def main() -> None: """ Entry point of the program. - This function initializes MPI, sets up the necessary communication, and executes the requested functions. + This function serves as the parallel execution backend for the standalone interactive + module. It initializes MPI for parallel processing, establishes ZMQ-based communication + with the frontend, and executes requested functions across multiple ranks. + + The function operates in a continuous loop, receiving commands through ZMQ when running + on rank 0 and broadcasting these commands to other ranks for parallel execution. Returns: None """executorlib/interactive/dependencies.py (1)
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110-134: Check exit method's return value handlingThe
__exit__method returns the result ofdraw()when_generate_dependency_graphis True, but context manager's__exit__return value is typically used to suppress exceptions (True/False). Consider separating the graph drawing logic into a separate method.Suggested fix:
def __exit__( self, exc_type: Any, exc_val: Any, exc_tb: Any, ) -> None: super().__exit__(exc_type=exc_type, exc_val=exc_val, exc_tb=exc_tb) if self._generate_dependency_graph: - node_lst, edge_lst = generate_nodes_and_edges( - task_hash_dict=self._task_hash_dict, - future_hash_inverse_dict={ - v: k for k, v in self._future_hash_dict.items() - }, - ) - return draw(node_lst=node_lst, edge_lst=edge_lst) + self._draw_dependency_graph() + + def _draw_dependency_graph(self) -> Any: + """Draw the dependency graph if enabled.""" + node_lst, edge_lst = generate_nodes_and_edges( + task_hash_dict=self._task_hash_dict, + future_hash_inverse_dict={ + v: k for k, v in self._future_hash_dict.items() + }, + ) + return draw(node_lst=node_lst, edge_lst=edge_lst)executorlib/interactive/executor.py (2)
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13-13: Fix typo in docstring.There's a typo in the word "exeutorlib" in the docstring.
- The executorlib.interactive.executor.InteractiveExecutor leverages the exeutorlib interfaces to distribute python + The executorlib.interactive.executor.InteractiveExecutor leverages the executorlib interfaces to distribute python
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89-89: Update example in docstring.The example uses
PyFluxStepExecutorinstead ofInteractiveStepExecutor.- >>> with PyFluxStepExecutor(max_cores=2) as p: + >>> with InteractiveStepExecutor(max_cores=2) as p:tests/test_flux_executor.py (1)
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1-190: Consider documenting the new module organization.The refactoring improves the code organization by separating standalone and interactive components. Consider adding documentation (e.g., in README.md or module docstrings) to explain:
- The purpose and scope of the
standalonenamespace- The relationship between
interactiveandstandalonemodules- Guidelines for deciding what belongs in each namespace
tests/test_dependencies_executor.py (2)
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36-207: Test coverage validates refactoring changes.The test suite effectively validates that the module reorganization maintains the same functionality:
- Integration tests for executor dependencies remain intact
- All core features (task execution, dependency handling, plotting) are covered
- No changes to test logic were needed, confirming interface compatibility
Consider adding explicit test cases for the new module boundaries to ensure they remain well-defined as the codebase evolves.
Line range hint
39-39: Consider improving cloudpickle registration in tests.The
cloudpickle_register(ind=1)call appears in multiple test methods with a magic number. Consider:
- Moving this to a setUp method or fixture
- Documenting the purpose of the registration
- Explaining the significance of
ind=1Example refactor:
def setUp(self): """Set up test fixtures before each test method.""" # Register serialization handler for task dependencies # ind=1 is used for... [explain the reason] cloudpickle_register(ind=1)Also applies to: 52-52, 82-82, 134-134, 164-164
executorlib/cache/shared.py (1)
9-11: LGTM! Good architectural improvement.Moving these utilities to the
standalonenamespace improves modularity by clearly separating independent utilities from shared state. This aligns well with the PR objective of highlighting standalone modules.This refactoring makes the codebase more maintainable by:
- Reducing coupling between components
- Making dependencies more explicit
- Facilitating easier testing and reuse of these utilities
executorlib/interactive/__init__.py (1)
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7-21: Consider documenting the architectural changes.The refactoring from
sharedtostandalonenamespace improves modularity and makes the codebase structure more explicit. Consider:
- Documenting the rationale for this architectural change
- Updating architecture diagrams if they exist
- Adding migration guides if other projects depend on these modules
Also applies to: 23-26
🧰 Tools
🪛 Ruff
23-26: Use
contextlib.suppress(ImportError)instead oftry-except-pass(SIM105)
executorlib/__init__.py (2)
Line range hint
1-236: Consider documenting the module organization principles.The move from
sharedtostandalonenamespace represents good architectural evolution. Consider adding documentation (e.g., in README.md) that explains:
- The purpose and scope of the
standalonenamespace- Guidelines for what belongs in
standalonevs. other namespaces- Migration path for other modules that might benefit from this reorganization
This will help maintain consistency as the codebase continues to evolve.
Documentation needs to be updated to reflect current API
The example in the docstring has two issues:
- It uses the deprecated
coresparameter instead ofmax_cores- The example's output shows incorrect array formatting compared to actual implementations
Suggested corrections:
- Replace
cores=2withmax_cores=2in the example- Update the example output to match the actual implementation pattern seen in tests
🔗 Analysis chain
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24-89: Verify example code compatibility.While the documentation is comprehensive, let's ensure the provided example remains valid with the new module structure changes.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify if the example code in the docstring is still valid # by checking if all imported modules exist in their specified locations # Test 1: Check if the example's imports are available rg -l "from mpi4py import MPI" # Test 2: Verify the Executor import path shown in example rg -l "from executorlib import Executor"Length of output: 748
Script:
#!/bin/bash # Let's verify the actual implementation and usage patterns to ensure example's correctness # Check the actual implementation of calc function in tests or examples rg "def calc\([^)]*\):" -A 5 # Check if there are any other examples using cores parameter instead of max_cores rg "Executor\(cores=" -B 2 -A 2 # Verify the initialization pattern with init_function rg "init_function=" -B 2 -A 2Length of output: 9870
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (33)
- executorlib/init.py (1 hunks)
- executorlib/backend/interactive_parallel.py (1 hunks)
- executorlib/backend/interactive_serial.py (1 hunks)
- executorlib/base/executor.py (1 hunks)
- executorlib/cache/backend.py (1 hunks)
- executorlib/cache/executor.py (1 hunks)
- executorlib/cache/shared.py (1 hunks)
- executorlib/interactive/init.py (3 hunks)
- executorlib/interactive/dependencies.py (1 hunks)
- executorlib/interactive/executor.py (1 hunks)
- executorlib/interactive/flux.py (2 hunks)
- executorlib/interactive/shared.py (2 hunks)
- executorlib/standalone/init.py (1 hunks)
- executorlib/standalone/queue.py (1 hunks)
- executorlib/standalone/serialize.py (1 hunks)
- tests/test_cache_executor_mpi.py (1 hunks)
- tests/test_cache_executor_serial.py (1 hunks)
- tests/test_cache_hdf.py (1 hunks)
- tests/test_cache_shared.py (1 hunks)
- tests/test_dependencies_executor.py (1 hunks)
- tests/test_executor_backend_mpi.py (1 hunks)
- tests/test_executor_backend_mpi_noblock.py (1 hunks)
- tests/test_flux_executor.py (1 hunks)
- tests/test_integration_pyiron_workflow.py (1 hunks)
- tests/test_local_executor.py (3 hunks)
- tests/test_local_executor_future.py (1 hunks)
- tests/test_shared_backend.py (1 hunks)
- tests/test_shared_communication.py (1 hunks)
- tests/test_shared_executorbase.py (1 hunks)
- tests/test_shared_input_check.py (1 hunks)
- tests/test_shared_thread.py (1 hunks)
- tests/test_shell_executor.py (1 hunks)
- tests/test_shell_interactive.py (1 hunks)
✅ Files skipped from review due to trivial changes (5)
- executorlib/backend/interactive_serial.py
- tests/test_integration_pyiron_workflow.py
- tests/test_shared_executorbase.py
- tests/test_shared_input_check.py
- tests/test_shared_thread.py
🧰 Additional context used
🪛 Ruff
executorlib/base/executor.py
45-45: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
51-51: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
67-67: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
145-148: Use
contextlib.suppress(AttributeError, RuntimeError)instead oftry-except-passReplace with
contextlib.suppress(AttributeError, RuntimeError)(SIM105)
executorlib/standalone/queue.py
15-15: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
tests/test_cache_executor_mpi.py
9-9:
executorlib.cache.shared.execute_tasks_h5imported but unused; consider usingimportlib.util.find_specto test for availability(F401)
9-9:
executorlib.cache.shared.execute_in_subprocessimported but unused; consider usingimportlib.util.find_specto test for availability(F401)
🔇 Additional comments (49)
executorlib/standalone/__init__.py (2)
9-9: Verify the impact of relocated spawner imports.The spawner classes have been moved to the standalone namespace. Let's verify all dependent modules have been updated.
#!/bin/bash # Search for any remaining references to spawner imports from old locations rg "from executorlib\.(shared|interactive).*import.*(MpiExecSpawner|SrunSpawner)"
10-10: Verify the impact of relocated thread imports.The RaisingThread class has been moved to the standalone namespace. Let's verify all dependent modules have been updated.
✅ Verification successful
The previous search might have been too specific. Let's broaden the search to find all references to RaisingThread and check thread.py files to ensure we have the complete picture of the migration.
Import changes for RaisingThread are consistent across the codebase
All imports of
RaisingThreadare correctly using the new locationexecutorlib.standalone.thread. The class is properly defined inexecutorlib/standalone/thread.pyand imported consistently across all dependent modules:
- executorlib/base/executor.py
- executorlib/cache/executor.py
- executorlib/interactive/dependencies.py
- executorlib/interactive/executor.py
- executorlib/interactive/shared.py
- tests/test_cache_executor_serial.py
- tests/test_dependencies_executor.py
- tests/test_shared_thread.py
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for any remaining references to RaisingThread from old locations rg "from executorlib\.(shared|interactive).*import.*RaisingThread"Length of output: 83
Script:
#!/bin/bash # Search for any imports of RaisingThread across the codebase rg "RaisingThread" -A 2 -B 2 # Also search for any potential old file that might have contained RaisingThread fd "thread.py"Length of output: 9222
executorlib/standalone/queue.py (2)
1-3: LGTM!The import statement and spacing follow PEP 8 guidelines.
12-19:⚠️ Potential issueSeveral improvements needed for robustness.
- Simplify the dictionary key check as per static analysis.
- Add error handling for potential exceptions during future cancellation.
- Consider type checking the future object before calling cancel.
Here's the suggested implementation:
while True: try: item = que.get_nowait() - if isinstance(item, dict) and "future" in item.keys(): - item["future"].cancel() - que.task_done() + if isinstance(item, dict) and "future" in item: + future = item["future"] + try: + if hasattr(future, 'cancel'): + future.cancel() + except Exception as e: + # Log error but continue processing other items + pass + finally: + que.task_done() except queue.Empty: breakLet's verify the usage of this function and the types of futures being passed:
🧰 Tools
🪛 Ruff
15-15: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
tests/test_cache_executor_mpi.py (2)
9-9: Verify import path changes across the codebase.The import path change from
executorlib.shared.cachetoexecutorlib.cache.sharedis part of the package restructuring. Let's verify that this change is consistent across the codebase.#!/bin/bash # Description: Verify no old import paths remain and new paths are used consistently echo "Checking for any remaining old import paths..." rg "from executorlib.shared.cache import" echo "Verifying consistent usage of new import paths..." rg "from executorlib.cache.shared import"🧰 Tools
🪛 Ruff
9-9:
executorlib.cache.shared.execute_tasks_h5imported but unused; consider usingimportlib.util.find_specto test for availability(F401)
9-9:
executorlib.cache.shared.execute_in_subprocessimported but unused; consider usingimportlib.util.find_specto test for availability(F401)
9-9: Verify the necessity of unused imports.The imported functions
execute_tasks_h5andexecute_in_subprocessare not used in the current test file. If these imports are intended for future test cases or are required for the test setup, consider adding a comment explaining their purpose. Otherwise, they should be removed.Consider adding a comment if these imports are intentional:
+ # These imports verify the availability of required functionality from executorlib.cache.shared import execute_tasks_h5, execute_in_subprocess🧰 Tools
🪛 Ruff
9-9:
executorlib.cache.shared.execute_tasks_h5imported but unused; consider usingimportlib.util.find_specto test for availability(F401)
9-9:
executorlib.cache.shared.execute_in_subprocessimported but unused; consider usingimportlib.util.find_specto test for availability(F401)
executorlib/cache/executor.py (1)
3-5: LGTM! The import restructuring aligns with architectural goals.The reorganization of imports improves module organization by:
- Moving base classes to a dedicated
basepackage- Highlighting standalone utilities in their own namespace
- Maintaining domain-specific code in appropriate packages
Let's verify the import consistency across the codebase:
✅ Verification successful
Import paths have been consistently updated across the codebase
The verification results show that:
- No instances of old import paths (
executorlib.shared.executor,executorlib.shared.thread,executorlib.shared.cache) were found- All relevant imports now use the new paths:
executorlib.base.executorfor ExecutorBaseexecutorlib.standalone.threadfor RaisingThreadexecutorlib.cache.sharedfor execute functions🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths that need updating # Test 1: Check for any remaining old import paths echo "Checking for old import paths..." rg "from executorlib.shared.(executor|thread|cache)" # Test 2: Verify new import paths are used consistently echo "Verifying new import paths usage..." rg "from executorlib.(base.executor|standalone.thread|cache.shared)"Length of output: 1937
executorlib/cache/backend.py (2)
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8-83: Functions remain robust after import changes.The functions continue to work correctly with the relocated imports:
- Proper handling of
FutureIteminstances in arguments- Safe file operations with intermediate
.h5readyand.h5outstates- Clear and accurate documentation
4-5: LGTM! Import restructuring aligns with PR objectives.The relocation of imports follows a logical organization:
- Cache-specific
FutureItemmoved tocache.shared- Generic HDF utilities moved to
standalone.hdfLet's verify the consistency of these new import paths across the codebase:
✅ Verification successful
Import paths have been consistently updated across the codebase
The verification results show:
- No instances of old import paths (
executorlib.shared.cacheorexecutorlib.shared.hdf) remainFutureItemis consistently imported fromexecutorlib.cache.shareddumpandloadfunctions are consistently imported fromexecutorlib.standalone.hdfacross all files🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that old import paths are not used elsewhere in the codebase # and new paths are used consistently. echo "Checking for any remaining old import paths..." rg "from executorlib.shared.(cache|hdf) import" echo "Verifying consistent usage of new FutureItem import..." rg "from executorlib.(cache.shared|shared.cache) import.*FutureItem" echo "Verifying consistent usage of new HDF utility imports..." rg "from executorlib.(standalone.hdf|shared.hdf) import.*(dump|load)"Length of output: 1024
tests/test_cache_hdf.py (1)
7-7: LGTM! Import path update aligns with module restructuring.The change from
sharedtostandalonenamespace is consistent with the PR's objective of highlighting standalone modules.Let's verify the consistency of this restructuring across the codebase:
✅ Verification successful
Import path migration from
shared.hdftostandalone.hdfis complete and consistentThe verification confirms that:
- No references to the old path
executorlib.shared.hdfremain in the codebase- The new path
executorlib.standalone.hdfis consistently used across all relevant files:
- tests/test_cache_shared.py
- tests/test_cache_hdf.py
- executorlib/interactive/shared.py
- executorlib/cache/backend.py
- executorlib/cache/shared.py
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all imports of dump/load are consistently using the new standalone path # and no references to the old shared path remain. # Test 1: Check for any remaining references to the old path echo "Checking for remaining references to shared.hdf..." rg "executorlib\.shared\.hdf" # Test 2: Verify consistent usage of new path echo "Verifying usage of new standalone.hdf path..." rg "executorlib\.standalone\.hdf"Length of output: 689
tests/test_executor_backend_mpi_noblock.py (2)
4-4: LGTM! Import path change aligns with module restructuring.The relocation of
cloudpickle_registerfromshared.executortostandalone.serializealigns well with the PR's objective of highlighting standalone modules.
4-4: Verify consistent import changes across test files.Let's verify that this import path change is consistent across all test files using cloudpickle_register.
✅ Verification successful
Import path changes are consistent across test files
The verification shows that all test files consistently use the new import path
executorlib.standalone.serializeforcloudpickle_register. No instances of the old import pathexecutorlib.shared.executorwere found.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths and verify consistency of new imports # Expected: All imports should use the new path from standalone.serialize echo "Checking for any remaining old imports..." rg "from executorlib.shared.executor import cloudpickle_register" tests/ echo -e "\nVerifying consistent use of new import path..." rg "from executorlib.standalone.serialize import cloudpickle_register" tests/Length of output: 1259
executorlib/standalone/serialize.py (2)
2-2: LGTM!The
inspectmodule import is appropriately placed with other standard library imports and is required for the new functionality.
30-30: LGTM!The new function is well-integrated with the existing codebase, maintaining clean separation and enhancing the module's capabilities without disrupting existing functionality.
executorlib/backend/interactive_parallel.py (1)
7-11: LGTM! Verify module availability in new locations.The import path changes align well with the PR objective of highlighting standalone modules. This restructuring improves module independence.
Let's verify that these modules exist in their new locations and maintain consistent interfaces:
✅ Verification successful
Let's gather more specific information about these functions by searching their implementations.
Import paths are correctly relocated and functions are available
The verification confirms that:
- All imported functions (
call_funct,parse_arguments,interface_connect,interface_receive,interface_send) are properly implemented in their new locations under thestandalone.interactivenamespace- The functions maintain their interfaces and are being used correctly in tests
- Multiple test files already reference these modules through the new import paths, indicating the restructuring is consistent
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the existence and interface consistency of relocated modules # Check if the modules exist in their new locations echo "Checking module existence..." rg -l "standalone.interactive.backend" --type py rg -l "standalone.interactive.communication" --type py # Verify the interface consistency by checking function signatures echo "Checking function signatures..." ast-grep --pattern 'def call_funct' ast-grep --pattern 'def parse_arguments' ast-grep --pattern 'def interface_connect' ast-grep --pattern 'def interface_receive' ast-grep --pattern 'def interface_send' ast-grep --pattern 'def interface_shutdown'Length of output: 836
Script:
#!/bin/bash # Search for function implementations in the new standalone module structure echo "Searching in standalone modules..." rg -p "standalone/interactive/backend.py" --type py rg -p "standalone/interactive/communication.py" --type py # Search for actual function implementations echo "Searching for function implementations..." rg -p "def (call_funct|parse_arguments|interface_connect|interface_receive|interface_send)" --type py -A 2 # Check imports in test files to verify module structure echo "Checking test imports..." rg "from.*standalone.interactive" tests/ --type pyLength of output: 2230
tests/test_shared_communication.py (2)
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22-106: Test implementation correctly uses the relocated imports.The test cases properly utilize all the imported components from their new locations while maintaining the original test logic and assertions. This confirms that the import path changes are purely organizational and don't impact functionality.
9-17: Verify consistent usage of new import paths across the codebase.The relocation of imports from
sharedtostandalonenamespace looks good and aligns with the PR objective of highlighting standalone modules. Let's verify that these changes are consistent across the codebase.✅ Verification successful
Import paths have been consistently updated across the codebase
The verification shows that:
- No instances of old imports from
executorlib.sharednamespace were found- The new imports from
executorlib.standaloneare consistently used across test files and main package modules- All related modules (communication, spawner, serialize) follow the new import structure
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that old import paths are not used elsewhere in the codebase # Test 1: Check for any remaining imports from the old paths echo "Checking for remaining old imports..." rg "from executorlib.shared.(communication|executor|spawner) import" # Test 2: Verify new import paths are used consistently echo "Verifying new import paths usage..." rg "from executorlib.standalone.(interactive.communication|serialize|interactive.spawner) import"Length of output: 3293
tests/test_shared_backend.py (2)
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11-103: Verify test coverage remains valid with new imports.The test cases themselves remain unchanged, which is good as they validate core functionality. However, let's verify that the test coverage is still comprehensive with the new import structure.
Let's check if there are any new methods or classes in the standalone modules that might need additional test coverage:
#!/bin/bash # Description: Compare the public interfaces of old and new modules to ensure test coverage is complete # Find all public methods/classes in the standalone modules echo "Analyzing standalone module interfaces..." ast-grep --pattern 'class $_:' executorlib/standalone/interactive/spawner.py ast-grep --pattern 'def $_(' executorlib/standalone/interactive/backend.py # Look for any TODO/FIXME comments that might indicate incomplete implementations echo "Checking for TODO/FIXME comments in new modules..." rg "TODO|FIXME" executorlib/standalone/interactive/
5-6: LGTM! Verify import path consistency across codebase.The import path changes align with the PR objective of highlighting standalone modules. The move from
sharedtostandalonenamespace is a logical restructuring.Let's verify the consistency of these import changes across the codebase:
✅ Verification successful
Import changes are consistent across the codebase
The verification shows that:
- All imports of spawner and backend modules consistently use the new
executorlib.standalone.interactivenamespace- No old import paths (
executorlib.interactive.backendorexecutorlib.shared.spawner) are being used- The module structure shows the correct placement of files in the
standalone/interactivedirectory- All dependent modules have been updated to use the new import paths
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all imports of these classes use the new standalone namespace # and that there are no lingering old import paths # Check for any remaining old import paths echo "Checking for old import paths..." rg "from executorlib\.(interactive\.backend|shared\.spawner)" # Verify new import paths are used consistently echo "Verifying new import paths..." rg "from executorlib\.standalone\.interactive\.(backend|spawner)"Length of output: 413
Script:
#!/bin/bash # Let's try a different approach to verify the import changes # 1. First, let's find all Python files that might import these modules echo "=== Files importing backend or spawner modules ===" rg -l "import.*spawner|import.*backend" --type py # 2. Let's see the actual import statements with context echo -e "\n=== Import statements with context ===" rg "from.*import.*[Ss]pawner|from.*import.*backend" --type py -B 1 -A 1 # 3. Let's specifically check the standalone namespace usage echo -e "\n=== Standalone namespace imports ===" rg "from executorlib\.standalone" --type py # 4. Let's verify the module structure echo -e "\n=== Module structure verification ===" fd -e py . executorlib/Length of output: 10648
tests/test_cache_shared.py (1)
8-14: Import restructuring aligns with modular design goals.The reorganization of imports follows a clear pattern:
- Cache-specific functionality is now under a dedicated
cachepackage- Standalone utilities (HDF, serialization) are appropriately moved to the
standalonenamespaceThis change improves the module organization by separating core cache functionality from standalone utilities.
Let's verify the consistency of this new structure across the codebase:
✅ Verification successful
Import restructuring is consistently applied across the codebase
The verification results show:
- No legacy imports from
executorlib.shared.(hdf|serialize)orexecutorlib.shared.cacheremain- New
standalonenamespace is consistently used across the codebase for utilities:
serializemodule is imported in executor-related fileshdfmodule is imported in cache and interactive components- New
cache.sharednamespace is properly referenced in cache-related modules🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the new import structure is consistently applied across the codebase # and that no old import paths remain. # Test 1: Check for any remaining old import paths echo "Checking for old import patterns..." rg "from executorlib.shared.(hdf|serialize)" || echo "No old standalone utility imports found" rg "from executorlib.shared.cache" || echo "No old cache imports found" # Test 2: Verify the new structure is used consistently echo -e "\nVerifying new import structure usage..." rg "from executorlib.standalone.(hdf|serialize)" rg "from executorlib.cache.shared"Length of output: 2677
tests/test_executor_backend_mpi.py (2)
Line range hint
31-143: LGTM! Test coverage is comprehensive and well-structured.The test suite provides thorough coverage:
- Serial and parallel execution scenarios
- Error handling for invalid configurations
- Cache functionality with timing verification
- Proper cleanup of test artifacts
7-7: LGTM! Import path update aligns with module restructuring.The change from
executorlib.shared.executortoexecutorlib.standalone.serializealigns with the PR's objective of highlighting standalone modules.Let's verify if similar import updates are needed elsewhere:
✅ Verification successful
Import path update is consistent across the codebase
The verification shows that:
- No files are using the old import path
executorlib.shared.executor- The new import path
executorlib.standalone.serializeis consistently used across all relevant test files and the base executor module🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining imports from the old path # and verify consistency of the new import path usage # Test 1: Check for any remaining old imports echo "Checking for remaining old imports from shared.executor:" rg "from executorlib.shared.executor import cloudpickle_register" # Test 2: Verify consistent usage of new import path echo "Verifying usage of new import path:" rg "from executorlib.standalone.serialize import cloudpickle_register"Length of output: 1342
tests/test_cache_executor_serial.py (2)
11-11: LGTM! Verify consistent usage of the new cache.shared namespace.The reorganization of cache-related functions into a dedicated cache namespace with shared utilities makes the structure clearer.
Let's verify that all imports of these cache functions have been updated consistently:
✅ Verification successful
Import changes are consistently applied across the codebase
The verification shows that all imports of
execute_tasks_h5andexecute_in_subprocessare using the newexecutorlib.cache.sharednamespace. No instances of the old import pathexecutorlib.shared.cachewere found.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths and # confirm all new imports are consistent echo "Checking for any remaining old imports..." rg "from executorlib.shared.cache import.*(execute_tasks_h5|execute_in_subprocess)" echo "Verifying new import usage..." rg "from executorlib.cache.shared import.*(execute_tasks_h5|execute_in_subprocess)"Length of output: 671
7-7: LGTM! Verify consistent usage of the new import path.The move of
RaisingThreadto the standalone namespace aligns with the PR objective of highlighting standalone modules.Let's verify that all imports of RaisingThread have been updated consistently:
✅ Verification successful
Import path change for RaisingThread is consistently applied across the codebase
The verification shows that:
- No instances of the old import path (
executorlib.shared.thread) remain- The new import path (
executorlib.standalone.thread) is consistently used across 9 files- All relevant modules (base, cache, interactive, tests) have been updated
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths of RaisingThread # and confirm all new imports are consistent echo "Checking for any remaining old imports..." rg "from executorlib.shared.thread import.*RaisingThread" echo "Verifying new import usage..." rg "from executorlib.standalone.thread import.*RaisingThread"Length of output: 1093
executorlib/interactive/flux.py (2)
16-17: Verify terminology change from "per core" to "per base".The documentation update changes resource allocation terminology from "per core" to "per base". This seems like a significant conceptual change that could affect how users understand and use these parameters.
Let's check if this terminology is consistently used across the codebase:
#!/bin/bash # Description: Verify terminology consistency echo "Checking for 'per core' terminology..." rg "per core" -t py echo "Checking for 'per base' terminology..." rg "per base" -t py # Check if there's any documentation explaining this change echo "Looking for documentation about this terminology..." fd -e md -e rst | xargs rg -l "per (core|base)"
6-6: LGTM! Verify consistent usage of new import path.The import path change aligns with the PR objective of highlighting standalone modules.
Let's verify the consistency of this import path change across the codebase:
✅ Verification successful
Import path change is consistent across the codebase
The verification shows that all files importing
BaseSpawnerare using the new pathexecutorlib.standalone.interactive.spawner. No instances of the old import path were found, indicating consistent usage across the codebase:
- executorlib/interactive/shared.py
- executorlib/interactive/executor.py
- executorlib/interactive/flux.py
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths and verify new import usage # Test 1: Check for any remaining old import paths echo "Checking for old import paths..." rg "from executorlib.shared.spawner import BaseSpawner" # Test 2: Verify new import path usage echo "Verifying new import path usage..." rg "from executorlib.standalone.interactive.spawner import BaseSpawner"Length of output: 1259
Script:
#!/bin/bash # Let's try a different approach to verify the import changes # Check all Python files that might import BaseSpawner echo "Files importing BaseSpawner:" rg "import.*BaseSpawner" -t py # Check the actual location of BaseSpawner class echo -e "\nLocation of BaseSpawner class definition:" ast-grep --pattern 'class BaseSpawner' # Check if the old directory structure exists echo -e "\nChecking directory structure:" fd "shared/spawner.py" fd "standalone/interactive/spawner.py"Length of output: 711
tests/test_shell_interactive.py (2)
Line range hint
39-143: Clean refactoring with preserved test functionality.The test implementation successfully validates that the refactored modules maintain their original behavior. Both test methods continue to work as expected with the new import paths, demonstrating that the module reorganization was done without breaking changes.
8-10: LGTM! Import paths align with module reorganization.The updated import paths properly reflect the new module structure, separating standalone components from shared functionality. This change aligns well with the PR objective of highlighting standalone modules.
Let's verify the consistency of these new module paths across the codebase:
✅ Verification successful
Import paths are correctly structured and consistent
The verification confirms that:
- All new module paths exist and are correctly placed:
executorlib/standalone/serialize.pyexecutorlib/interactive/shared.pyexecutorlib/standalone/interactive/spawner.py- The
MpiExecSpawnerclass is properly defined in its new location and all imports across the codebase are consistently using the new path.- The import changes align with the module reorganization, properly separating standalone components from shared functionality.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the new module structure and import consistency # Test 1: Verify the existence and structure of the new module paths echo "Verifying module structure..." fd -t f . executorlib/standalone/serialize.py executorlib/interactive/shared.py executorlib/standalone/interactive/spawner.py # Test 2: Check for any remaining old import paths that might need updating echo "Checking for potentially outdated imports..." rg -l "from executorlib\.(shared|interactive)" --type pyLength of output: 1011
Script:
#!/bin/bash # Let's check the actual content of the imports in the affected files to verify consistency # Check the actual imports in test_shell_interactive.py echo "=== Content of test_shell_interactive.py ===" rg "from executorlib\." -A 2 tests/test_shell_interactive.py # Check if the new module paths exist echo -e "\n=== Checking new module paths ===" fd "serialize.py|shared.py|spawner.py" --type f # Look for any references to MpiExecSpawner to verify its location echo -e "\n=== References to MpiExecSpawner ===" rg "MpiExecSpawner" --type py -C 2Length of output: 19350
executorlib/interactive/dependencies.py (2)
5-14: Well-structured import reorganization!The separation of imports into
sharedandstandalonenamespaces improves code organization and clearly identifies reusable components. This aligns well with the PR's objective of highlighting standalone modules.
Line range hint
89-108: Review potential race condition in submit methodWhen
_generate_dependency_graphis True, the method creates an immediately completed Future with None result. This could lead to race conditions if any dependent tasks expect actual results from this Future.Let's check for any dependent tasks that might be affected:
executorlib/interactive/executor.py (2)
Line range hint
45-57: Implementation looks good!The refactoring maintains the original behavior while improving code organization. Both classes properly initialize their processes using the new standalone components, and the implementation remains clean and consistent.
Also applies to: 108-120
1-8: Verify the new import structure across the codebase.The reorganization of imports from
sharedtostandalonenamespace appears to be part of a larger refactoring effort. Let's verify the consistency of these changes.✅ Verification successful
Let's gather more information to verify the import structure and potential circular dependencies.
Import structure reorganization is consistent and properly implemented
The verification shows that:
- All new module paths (
interactive/shared.py,standalone/interactive/spawner.py,standalone/thread.py) exist and are properly structured- No old import patterns from
executorlib.shared.executorremain- The import hierarchy is clean:
standalonemodules are base implementations with no circular dependenciesinteractivemodules properly import fromstandalonewithout creating cycles- The new import structure in
executor.pyaligns with the overall architecture🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the new import structure exists and is consistent # Test 1: Verify the new module structure exists echo "Checking for new module structure..." fd -e py -p "executorlib/(interactive/shared|standalone/(interactive/spawner|thread)).py$" # Test 2: Check for any remaining old import patterns that might have been missed echo "Checking for any remaining old import patterns..." rg -l "from executorlib.shared.executor import" # Test 3: Verify no circular imports were introduced echo "Checking for potential circular imports..." rg -l "from executorlib.(standalone|interactive)" executorlib/Length of output: 1046
Script:
#!/bin/bash # Check the actual imports in the files that might have circular dependencies echo "Checking imports in interactive/shared.py..." rg "^from executorlib\." executorlib/interactive/shared.py -A 1 echo "\nChecking imports in standalone/interactive/spawner.py..." rg "^from executorlib\." executorlib/standalone/interactive/spawner.py -A 1 echo "\nChecking imports in standalone/thread.py..." rg "^from executorlib\." executorlib/standalone/thread.py -A 1 echo "\nChecking what components are imported from these modules..." rg "from executorlib\.(interactive\.shared|standalone\.interactive\.spawner|standalone\.thread) import" -A 1Length of output: 4946
tests/test_local_executor_future.py (3)
9-9: LGTM! Import path update aligns with module restructuring.The change from
sharedtostandalone.interactivenamespace is consistent with the PR's objective of highlighting standalone modules.
Line range hint
20-143: Test coverage remains comprehensive after refactoring.The test suite maintains thorough coverage of InteractiveExecutor functionality:
- Single-core and multi-core scenarios
- Future state verification
- Callback handling
- Executor lifecycle management
The import path changes don't impact the test logic or coverage.
9-9: Verify all import path updates mentioned in the summary.The AI summary mentions several other import path updates that are not visible in the provided code:
ExecutorBasefromexecutorlib.base.executorcloudpickle_registerfromexecutorlib.standalone.serializecall_functfromexecutorlib.standalone.interactive.backendLet's verify these imports:
tests/test_flux_executor.py (2)
Line range hint
116-157: LGTM! Test methods maintain correct functionality.The test methods
test_execute_taskandtest_execute_task_threadscontinue to work correctly with the relocated functions, maintaining the same validation logic and assertions.
9-10: LGTM! Import changes align with modular restructuring.The relocation of imports follows the PR's objective of highlighting standalone modules and improving code organization.
Let's verify the consistency of these import changes across the codebase:
✅ Verification successful
Import paths have been consistently updated across the codebase
The verification confirms:
- No instances of old imports from
executorlib.shared.executorremain- New import paths are used consistently across all files:
cloudpickle_registerfromexecutorlib.standalone.serializein 10 filesexecute_parallel_tasksfromexecutorlib.interactive.sharedin 4 files🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all imports of these functions use the new paths # Test 1: No remaining old imports of cloudpickle_register rg "from executorlib.shared.executor import .*cloudpickle_register" # Test 2: No remaining old imports of execute_parallel_tasks rg "from executorlib.shared.executor import .*execute_parallel_tasks" # Test 3: Verify new import paths are used consistently rg "from executorlib.standalone.serialize import cloudpickle_register" rg "from executorlib.interactive.shared import execute_parallel_tasks"Length of output: 1667
tests/test_shell_executor.py (1)
7-9: LGTM! Import restructuring aligns well with modular design.The reorganization of imports reflects a cleaner separation of concerns:
- Serialization logic moved to dedicated
standalone.serialize- Interactive execution components grouped under
interactivenamespace- Spawner functionality properly isolated in
standalone.interactive.spawnerLet's verify the consistency of these import changes across the codebase:
✅ Verification successful
Import paths are consistently updated across the codebase ✓
All imports of the refactored components follow the new module structure:
cloudpickle_registeris consistently imported fromexecutorlib.standalone.serializeacross 10 filesexecute_parallel_tasksis consistently imported fromexecutorlib.interactive.sharedacross 4 filesMpiExecSpawneris consistently imported fromexecutorlib.standalone.interactive.spawneracross 9 files🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all imports of these components use the new paths consistently # Test 1: Verify cloudpickle_register is consistently imported from standalone.serialize echo "Checking cloudpickle_register imports..." rg "import.*cloudpickle_register" # Test 2: Verify execute_parallel_tasks is consistently imported from interactive.shared echo "Checking execute_parallel_tasks imports..." rg "import.*execute_parallel_tasks" # Test 3: Verify MpiExecSpawner is consistently imported from standalone.interactive.spawner echo "Checking MpiExecSpawner imports..." rg "import.*MpiExecSpawner"Length of output: 2725
tests/test_dependencies_executor.py (1)
7-11: LGTM! Import reorganization aligns with modular design.The relocation of imports follows a logical pattern:
- Core utilities (serialization, threading, plotting) moved to
standalone- Execution-specific functionality moved to
interactiveLet's verify the consistency of these import changes across the codebase:
✅ Verification successful
The results show that:
- No old import paths were found in the codebase (
rgreturned no matches)- All new module files exist in their expected locations:
executorlib/standalone/{plot.py,serialize.py,thread.py}executorlib/interactive/shared.pyLet's verify one final aspect - that the moved functions are actually defined in these new locations:
Import paths have been correctly reorganized and verified
All changes are consistent and properly implemented:
- No legacy imports remain in the codebase
- All new module files exist in their expected locations
- All referenced functions/classes are properly defined in their new locations:
cloudpickle_registerinstandalone/serialize.pyRaisingThreadinstandalone/thread.pyexecute_tasks_with_dependenciesininteractive/shared.pygenerate_nodes_and_edgesinstandalone/plot.py🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that all imports have been updated consistently # Test 1: Check for any remaining old import paths echo "Checking for old import paths..." rg "from executorlib.shared.(executor|thread|plot)" -l # Test 2: Verify new import locations exist echo "Verifying new module structure..." fd -e py -E "tests" "serialize.py|thread.py|plot.py" executorlib/standalone/ fd -e py -E "tests" "shared.py" executorlib/interactive/Length of output: 474
Script:
#!/bin/bash # Verify the functions exist in their new locations echo "Checking cloudpickle_register in serialize.py..." rg "def cloudpickle_register" executorlib/standalone/serialize.py -A 2 echo "Checking RaisingThread in thread.py..." rg "class RaisingThread" executorlib/standalone/thread.py -A 2 echo "Checking execute_tasks_with_dependencies in shared.py..." rg "def execute_tasks_with_dependencies" executorlib/interactive/shared.py -A 2 echo "Checking generate_nodes_and_edges in plot.py..." rg "def generate_nodes_and_edges" executorlib/standalone/plot.py -A 2Length of output: 1201
executorlib/cache/shared.py (1)
9-11: Verify the impact of import path changes.Let's ensure all dependent modules have been updated to use the new import paths.
✅ Verification successful
Import path changes have been correctly implemented
The verification shows that:
- All references to the functions (
get_command_path,dump,get_output,serialize_funct_h5) are now using the newexecutorlib.standalone.*paths- The standalone modules exist and contain the expected function implementations
- No remaining references to the old import paths (
executorlib.shared.*) were found- No circular imports were detected between the standalone modules and the cache/shared modules
The import path changes have been properly implemented across the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining references to the old import paths # and verify the existence of the new modules echo "Checking for any remaining references to old import paths..." rg "from executorlib.shared.(command|hdf|serialize) import" --type py echo -e "\nVerifying existence of new standalone modules..." for module in command hdf serialize; do echo "Checking for executorlib/standalone/${module}.py" if [ ! -f "executorlib/standalone/${module}.py" ]; then echo "Warning: executorlib/standalone/${module}.py not found" fi done echo -e "\nChecking for potential circular imports..." rg "from executorlib.(shared|cache).*import" executorlib/standalone/Length of output: 1057
Script:
#!/bin/bash # Let's try a different approach to verify the import changes # 1. First, let's check if there are any references to these specific functions from the old paths echo "Checking for references to specific functions from old paths..." rg "get_command_path|dump|get_output|serialize_funct_h5" --type py # 2. Let's also verify the new standalone modules exist and contain these functions echo -e "\nVerifying function definitions in new locations..." for file in command.py hdf.py serialize.py; do echo "=== Content of executorlib/standalone/${file} ===" if [ -f "executorlib/standalone/${file}" ]; then cat "executorlib/standalone/${file}" else echo "File not found" fi echo -e "\n" done # 3. Let's check all Python imports in the executorlib directory for a complete picture echo "Checking all imports in executorlib..." rg "^from executorlib\.|^import executorlib\." --type pyLength of output: 18096
executorlib/interactive/__init__.py (3)
51-51: LGTM! Documentation update is consistent.The docstring update correctly reflects the new flux-base requirement, maintaining consistency with the import block comment.
23-26: Verify dependency change impact: flux-core → flux-base.The dependency requirement has changed from flux-core to flux-base. This could potentially be a breaking change.
Let's check the dependency implications:
#!/bin/bash # Description: Check for any references to flux dependencies # to understand the scope of this change echo "Checking for flux-core references in documentation..." rg -g "!*.py" "flux-core" echo "Checking for flux-base references in documentation..." rg -g "!*.py" "flux-base" echo "Checking setup files for flux dependencies..." fd "setup.(py|cfg)" -x grep -l "flux"🧰 Tools
🪛 Ruff
23-26: Use
contextlib.suppress(ImportError)instead oftry-except-pass(SIM105)
Line range hint
7-21: LGTM! Verify import path consistency across the codebase.The refactoring of imports from
sharedtostandalonenamespace aligns with the PR objective of highlighting standalone modules.Let's verify the consistency of these import changes across the codebase:
✅ Verification successful
Import path migration is complete and consistent
The verification confirms that:
- No references to the old
executorlib.sharedpaths remain in the codebase- The new
standalonemodule structure is properly in place with all required files, including:
executorlib/standalone/inputcheck.pyexecutorlib/standalone/interactive/spawner.pyThe import changes in the review are correct and complete, aligning with the broader refactoring effort.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining references to the old import paths # to ensure complete migration echo "Checking for any remaining references to old shared paths..." rg "executorlib\.shared\.(inputcheck|spawner)" echo "Verifying new standalone paths exist..." fd -e py -e pyi "inputcheck.py" -x grep -l "executorlib\.standalone\.inputcheck" fd -e py -e pyi "spawner.py" -x grep -l "executorlib\.standalone\.interactive\.spawner"Length of output: 1381
Script:
#!/bin/bash # Let's try a different approach to verify the import changes # Check for any remaining references to shared paths echo "Checking for remaining shared paths..." rg "from executorlib\.shared\." rg "import executorlib\.shared\." # Check if the standalone paths exist and are properly structured echo -e "\nVerifying standalone module structure..." fd -t f "standalone" --full-pathLength of output: 745
🧰 Tools
🪛 Ruff
23-26: Use
contextlib.suppress(ImportError)instead oftry-except-pass(SIM105)
executorlib/__init__.py (2)
6-11: LGTM! Clean import restructuring.The migration of input check functions from
sharedtostandalonenamespace aligns well with the PR's objective of highlighting standalone modules. The import statements are properly structured with clear internal usage indicators.
Line range hint
207-236: LGTM! Clean implementation of relocated validation functions.The implementation correctly uses the relocated input check functions while maintaining the existing behavior. The code handles both dependency-enabled and disabled cases appropriately.
tests/test_local_executor.py (2)
9-17: LGTM! Import restructuring aligns with PR objectives.The import changes correctly reflect the movement of modules from
sharedtostandalonenamespace, improving the modularity of the codebase.
9-17: Verify consistent usage of new import paths across the codebase.Let's ensure all imports of these modules have been updated consistently across the codebase.
✅ Verification successful
Based on the results, I can generate the final response as we have comprehensive information about the import usage across the codebase.
Import paths have been consistently updated across the codebase
The verification shows that:
- No instances of old import paths (
executorlib.shared) were found- All relevant imports now correctly use the new paths under
executorlib.standalone- The new import structure is consistently used across test files and main package modules
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for any remaining old import paths that need updating echo "Checking for old import paths..." rg "from executorlib.shared.(spawner|backend|serialize)" || echo "No old import paths found." echo -e "\nVerifying new import paths are used consistently..." rg "from executorlib.standalone.(interactive.spawner|interactive.backend|serialize)"Length of output: 3301
executorlib/interactive/shared.py (2)
5-5: ApprovedThe import of
Futurefromconcurrent.futuresis appropriate and necessary for handling asynchronous computations.
9-18: Verify that updated import paths are correct and modules existThe import statements have been updated to reflect the new modular structure under the
executorlib.standalonenamespace. Ensure that all the imported modules and functions are available at the specified paths to prevent runtimeImportError.Run the following script to verify the existence of the imported modules:
✅ Verification successful
All import paths are valid and modules exist in the codebase
The verification script confirmed that all imported modules exist at their specified paths:
executorlib/base/executor.pyexecutorlib/standalone/command.pyexecutorlib/standalone/inputcheck.pyexecutorlib/standalone/interactive/communication.pyexecutorlib/standalone/interactive/spawner.pyexecutorlib/standalone/serialize.pyexecutorlib/standalone/thread.py🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify that the imported modules exist in the new structure. # List of expected module files based on imports modules=( "executorlib/base/executor.py" "executorlib/standalone/command.py" "executorlib/standalone/inputcheck.py" "executorlib/standalone/interactive/communication.py" "executorlib/standalone/interactive/spawner.py" "executorlib/standalone/serialize.py" "executorlib/standalone/thread.py" ) # Check for the existence of each module file for module in "${modules[@]}"; do if ! fd "$(basename "$module")" "$(dirname "$module")"; then echo "Module ${module} not found" fi doneLength of output: 1780
| from executorlib.standalone.interactive.communication import ( | ||
| SocketInterface, | ||
| interface_bootup, | ||
| interface_connect, | ||
| interface_receive, | ||
| interface_send, | ||
| interface_shutdown, |
There was a problem hiding this comment.
💡 Codebase verification
No action needed - docstring reference is harmless
The only remaining reference to executorlib.shared.communication is in a docstring return type hint in the interface_bootup function. This is a documentation artifact that should be updated but doesn't affect the actual code functionality. The code itself has been properly migrated to the new namespace structure.
- Update return type hint in
interface_bootupdocstring inexecutorlib/standalone/interactive/communication.pyto useexecutorlib.standalone.interactive.communication.SocketInterfaceinstead ofexecutorlib.shared.communication.SocketInterface
🔗 Analysis chain
Verify the impact of relocated communication imports.
The communication imports have been moved from shared to standalone namespace. While the change aligns with the PR objectives, we should verify that all dependent modules have been updated accordingly.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for any remaining references to the old import path
rg "from executorlib.shared.communication import"
# Search for any direct usage of the old module
rg "executorlib.shared.communication"
Length of output: 239
Script:
#!/bin/bash
# Check the content of communication.py to understand the context of the reference
cat executorlib/standalone/interactive/communication.py
Length of output: 7088
| def cloudpickle_register(ind: int = 2): | ||
| """ | ||
| Cloudpickle can either pickle by value or pickle by reference. The functions which are communicated have to | ||
| be pickled by value rather than by reference, so the module which calls the map function is pickled by value. | ||
| https://github.com/cloudpipe/cloudpickle#overriding-pickles-serialization-mechanism-for-importable-constructs | ||
| inspect can help to find the module which is calling executorlib | ||
| https://docs.python.org/3/library/inspect.html | ||
| to learn more about inspect another good read is: | ||
| http://pymotw.com/2/inspect/index.html#module-inspect | ||
| 1 refers to 1 level higher than the map function | ||
|
|
||
| Args: | ||
| ind (int): index of the level at which pickle by value starts while for the rest pickle by reference is used | ||
| """ | ||
| try: # When executed in a jupyter notebook this can cause a ValueError - in this case we just ignore it. | ||
| cloudpickle.register_pickle_by_value(inspect.getmodule(inspect.stack()[ind][0])) | ||
| except IndexError: | ||
| cloudpickle_register(ind=ind - 1) | ||
| except ValueError: | ||
| pass |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding parameter validation and improving error handling.
While the implementation is generally sound, there are a few potential improvements to consider:
- The default value of
ind=2makes assumptions about the call stack depth. Consider documenting this assumption in the docstring. - The recursive fallback could potentially hit Python's recursion limit for deeply nested calls.
- The silent pass on ValueError might mask issues in non-notebook environments.
Consider this improved implementation:
def cloudpickle_register(ind: int = 2):
"""
Cloudpickle can either pickle by value or pickle by reference. The functions which are communicated have to
be pickled by value rather than by reference, so the module which calls the map function is pickled by value.
https://github.com/cloudpipe/cloudpickle#overriding-pickles-serialization-mechanism-for-importable-constructs
inspect can help to find the module which is calling executorlib
https://docs.python.org/3/library/inspect.html
to learn more about inspect another good read is:
http://pymotw.com/2/inspect/index.html#module-inspect
1 refers to 1 level higher than the map function
Args:
ind (int): index of the level at which pickle by value starts while for the rest pickle by reference is used
+ Default is 2, assuming direct call from user code.
+
+ Raises:
+ ValueError: If ind is less than 1 or if running in an unsupported environment
+ RecursionError: If the call stack is exhausted before finding a valid module
"""
+ if ind < 1:
+ raise ValueError("Stack level index must be positive")
+
try: # When executed in a jupyter notebook this can cause a ValueError - in this case we just ignore it.
cloudpickle.register_pickle_by_value(inspect.getmodule(inspect.stack()[ind][0]))
except IndexError:
+ if ind <= 1:
+ raise ValueError("Could not find a valid module in the call stack")
cloudpickle_register(ind=ind - 1)
except ValueError:
+ if not any(frame.filename.startswith('<ipython') for frame in inspect.stack()):
+ raise # Re-raise if not in a notebook environment
pass📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def cloudpickle_register(ind: int = 2): | |
| """ | |
| Cloudpickle can either pickle by value or pickle by reference. The functions which are communicated have to | |
| be pickled by value rather than by reference, so the module which calls the map function is pickled by value. | |
| https://github.com/cloudpipe/cloudpickle#overriding-pickles-serialization-mechanism-for-importable-constructs | |
| inspect can help to find the module which is calling executorlib | |
| https://docs.python.org/3/library/inspect.html | |
| to learn more about inspect another good read is: | |
| http://pymotw.com/2/inspect/index.html#module-inspect | |
| 1 refers to 1 level higher than the map function | |
| Args: | |
| ind (int): index of the level at which pickle by value starts while for the rest pickle by reference is used | |
| """ | |
| try: # When executed in a jupyter notebook this can cause a ValueError - in this case we just ignore it. | |
| cloudpickle.register_pickle_by_value(inspect.getmodule(inspect.stack()[ind][0])) | |
| except IndexError: | |
| cloudpickle_register(ind=ind - 1) | |
| except ValueError: | |
| pass | |
| def cloudpickle_register(ind: int = 2): | |
| """ | |
| Cloudpickle can either pickle by value or pickle by reference. The functions which are communicated have to | |
| be pickled by value rather than by reference, so the module which calls the map function is pickled by value. | |
| https://github.com/cloudpipe/cloudpickle#overriding-pickles-serialization-mechanism-for-importable-constructs | |
| inspect can help to find the module which is calling executorlib | |
| https://docs.python.org/3/library/inspect.html | |
| to learn more about inspect another good read is: | |
| http://pymotw.com/2/inspect/index.html#module-inspect | |
| 1 refers to 1 level higher than the map function | |
| Args: | |
| ind (int): index of the level at which pickle by value starts while for the rest pickle by reference is used | |
| Default is 2, assuming direct call from user code. | |
| Raises: | |
| ValueError: If ind is less than 1 or if running in an unsupported environment | |
| RecursionError: If the call stack is exhausted before finding a valid module | |
| """ | |
| if ind < 1: | |
| raise ValueError("Stack level index must be positive") | |
| try: # When executed in a jupyter notebook this can cause a ValueError - in this case we just ignore it. | |
| cloudpickle.register_pickle_by_value(inspect.getmodule(inspect.stack()[ind][0])) | |
| except IndexError: | |
| if ind <= 1: | |
| raise ValueError("Could not find a valid module in the call stack") | |
| cloudpickle_register(ind=ind - 1) | |
| except ValueError: | |
| if not any(frame.filename.startswith('<ipython') for frame in inspect.stack()): | |
| raise # Re-raise if not in a notebook environment | |
| pass |
| @@ -331,7 +329,7 @@ def test_executor_exception_future(self): | |||
| def test_meta(self): | |||
| meta_data_exe_dict = { | |||
| "cores": 2, | |||
| "spawner": "<class 'executorlib.shared.spawner.MpiExecSpawner'>", | |||
| "spawner": "<class 'executorlib.standalone.interactive.spawner.MpiExecSpawner'>", | |||
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider making class path assertions more maintainable.
While the updated class paths are correct, hardcoded string literals for class paths can be fragile during future refactoring. Consider using MpiExecSpawner.__module__ and MpiExecSpawner.__qualname__ to dynamically construct the expected class path.
Example improvement:
- "spawner": "<class 'executorlib.standalone.interactive.spawner.MpiExecSpawner'>",
+ "spawner": f"<class '{MpiExecSpawner.__module__}.{MpiExecSpawner.__qualname__}'>",Also applies to: 361-361
| """ | ||
| return self._future_queue | ||
|
|
||
| def submit(self, fn: callable, *args, resource_dict: dict = {}, **kwargs) -> Future: |
There was a problem hiding this comment.
Avoid using mutable default arguments
Using a mutable default argument like {} for resource_dict can lead to unexpected behavior because the same dictionary is shared across all calls to the function. It's better to use None and initialize the dictionary inside the function.
Apply this diff:
- def submit(self, fn: callable, *args, resource_dict: dict = {}, **kwargs) -> Future:
+ def submit(self, fn: callable, *args, resource_dict: Optional[dict] = None, **kwargs) -> Future:Then, initialize resource_dict within the function:
if resource_dict is None:
resource_dict = {}🧰 Tools
🪛 Ruff
67-67: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
| def __del__(self): | ||
| """ | ||
| Clean-up the resources associated with the Executor. | ||
| """ | ||
| try: | ||
| self.shutdown(wait=False) | ||
| except (AttributeError, RuntimeError): | ||
| pass |
There was a problem hiding this comment.
Avoid using __del__ for resource cleanup
Relying on the __del__ method for cleanup is not recommended because the timing of object destruction is not guaranteed, which can lead to unexpected behavior. Consider providing an explicit close() or shutdown() method that users can call to clean up resources.
Would you like assistance in refactoring the cleanup process to use an explicit method instead of __del__?
🧰 Tools
🪛 Ruff
145-148: Use
contextlib.suppress(AttributeError, RuntimeError)instead oftry-except-passReplace with
contextlib.suppress(AttributeError, RuntimeError)(SIM105)
| return meta_data_dict | ||
| elif self._process is not None: | ||
| meta_data_dict = self._process._kwargs.copy() | ||
| if "future_queue" in meta_data_dict.keys(): |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Simplify dictionary key check
Similarly, update this condition for better readability and efficiency.
Apply this diff:
- if "future_queue" in meta_data_dict.keys():
+ if "future_queue" in meta_data_dict:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if "future_queue" in meta_data_dict.keys(): | |
| if "future_queue" in meta_data_dict: |
🧰 Tools
🪛 Ruff
51-51: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
| try: | ||
| self.shutdown(wait=False) | ||
| except (AttributeError, RuntimeError): | ||
| pass |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use contextlib.suppress for exception handling
Instead of using a try-except block that catches exceptions and does nothing, you can use contextlib.suppress to handle the exceptions more elegantly.
Apply this diff:
- try:
- self.shutdown(wait=False)
- except (AttributeError, RuntimeError):
- pass
+ with contextlib.suppress(AttributeError, RuntimeError):
+ self.shutdown(wait=False)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| self.shutdown(wait=False) | |
| except (AttributeError, RuntimeError): | |
| pass | |
| with contextlib.suppress(AttributeError, RuntimeError): | |
| self.shutdown(wait=False) |
🧰 Tools
🪛 Ruff
145-148: Use
contextlib.suppress(AttributeError, RuntimeError)instead oftry-except-passReplace with
contextlib.suppress(AttributeError, RuntimeError)(SIM105)
| """ | ||
| if self._process is not None and isinstance(self._process, list): | ||
| meta_data_dict = self._process[0]._kwargs.copy() | ||
| if "future_queue" in meta_data_dict.keys(): |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Simplify dictionary key check
Instead of using "key in dict.keys()", it's more Pythonic to use "key in dict".
Apply this diff:
- if "future_queue" in meta_data_dict.keys():
+ if "future_queue" in meta_data_dict:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if "future_queue" in meta_data_dict.keys(): | |
| if "future_queue" in meta_data_dict: |
🧰 Tools
🪛 Ruff
45-45: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
| Future: A Future representing the given call. | ||
| """ | ||
| check_resource_dict_is_empty(resource_dict=resource_dict) | ||
| check_resource_dict(function=fn) |
There was a problem hiding this comment.
Correct the argument passed to check_resource_dict
It seems that check_resource_dict is being called with function=fn, but it likely expects a resource_dict argument. Ensure that you're passing the correct parameter to the function.
Apply this diff:
- check_resource_dict(function=fn)
+ check_resource_dict(resource_dict=resource_dict)Committable suggestion was skipped due to low confidence.
| @@ -691,7 +512,7 @@ def _execute_task_with_cache( | |||
| future_queue (Queue): Queue for receiving new tasks. | |||
| cache_directory (str): The directory to store cache files. | |||
| """ | |||
| from executorlib.shared.hdf import dump, get_output | |||
| from executorlib.standalone.hdf import dump, get_output | |||
There was a problem hiding this comment.
🛠️ Refactor suggestion
Move import statement outside the function for better practice
Importing dump and get_output inside the _execute_task_with_cache function can lead to unnecessary overhead and reduced readability. It's a good practice to place imports at the top of the file unless there's a specific reason to delay the import (e.g., to prevent circular dependencies or reduce startup time for rarely used code paths).
Apply this diff to move the import to the module level:
+from executorlib.standalone.hdf import dump, get_output
def _execute_task_with_cache(
interface: SocketInterface,
task_dict: dict,
future_queue: queue.Queue,
cache_directory: str,
):
"""
Execute the task in the task_dict by communicating it via the interface using the cache in the cache directory.
Args:
interface (SocketInterface): socket interface for zmq communication
task_dict (dict): task submitted to the executor as dictionary. This dictionary has the following keys
{"fn": callable, "args": (), "kwargs": {}, "resource_dict": {}}
future_queue (Queue): Queue for receiving new tasks.
cache_directory (str): The directory to store cache files.
"""
- from executorlib.standalone.hdf import dump, get_output
task_key, data_dict = serialize_funct_h5(
task_dict["fn"], *task_dict["args"], **task_dict["kwargs"]
)
# Rest of the code...📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from executorlib.standalone.hdf import dump, get_output | |
| from executorlib.standalone.hdf import dump, get_output | |
| def _execute_task_with_cache( | |
| interface: SocketInterface, | |
| task_dict: dict, | |
| future_queue: queue.Queue, | |
| cache_directory: str, | |
| ): | |
| """ | |
| Execute the task in the task_dict by communicating it via the interface using the cache in the cache directory. | |
| Args: | |
| interface (SocketInterface): socket interface for zmq communication | |
| task_dict (dict): task submitted to the executor as dictionary. This dictionary has the following keys | |
| {"fn": callable, "args": (), "kwargs": {}, "resource_dict": {}} | |
| future_queue (Queue): Queue for receiving new tasks. | |
| cache_directory (str): The directory to store cache files. | |
| """ | |
| task_key, data_dict = serialize_funct_h5( | |
| task_dict["fn"], *task_dict["args"], **task_dict["kwargs"] | |
| ) | |
| # Rest of the code... |
for more information, see https://pre-commit.ci
There was a problem hiding this comment.
Actionable comments posted: 4
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (9)
- executorlib/init.py (1 hunks)
- executorlib/interactive/init.py (0 hunks)
- executorlib/interactive/dependencies.py (0 hunks)
- executorlib/interactive/executor.py (1 hunks)
- executorlib/interactive/shared.py (3 hunks)
- tests/test_dependencies_executor.py (1 hunks)
- tests/test_flux_executor.py (1 hunks)
- tests/test_local_executor.py (3 hunks)
- tests/test_local_executor_future.py (1 hunks)
💤 Files with no reviewable changes (2)
- executorlib/interactive/init.py
- executorlib/interactive/dependencies.py
🚧 Files skipped from review as they are similar to previous changes (5)
- executorlib/init.py
- tests/test_dependencies_executor.py
- tests/test_flux_executor.py
- tests/test_local_executor.py
- tests/test_local_executor_future.py
🧰 Additional context used
🪛 Ruff
executorlib/interactive/executor.py
32-35: Use
contextlib.suppress(ImportError)instead oftry-except-pass(SIM105)
85-85: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
159-159: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
executorlib/interactive/shared.py
161-161: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
213-213: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
🔇 Additional comments (2)
executorlib/interactive/executor.py (1)
83-84:⚠️ Potential issueAvoid mutable default arguments to prevent unexpected behavior
Using mutable default arguments like
{}can lead to unexpected behavior because the same object is shared across all calls to the function. Replace the default value withNoneand initialize the mutable object within the function.Apply this diff to fix the issue:
def submit( self, fn: Callable[..., Any], *args: Any, - resource_dict: Dict[str, Any] = {}, + resource_dict: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Future: """ Submits a task to the executor. Args: fn (callable): The function to be executed. *args: Variable length argument list. - resource_dict (dict, optional): A dictionary of resources required by the task. Defaults to {}. + resource_dict (dict, optional): A dictionary of resources required by the task. Defaults to None. **kwargs: Arbitrary keyword arguments. Returns: Future: A future object representing the result of the task. """ + if resource_dict is None: + resource_dict = {}Likely invalid or redundant comment.
executorlib/interactive/shared.py (1)
619-619: Import statements should be at the module levelAs previously noted, importing
dumpandget_outputinside the_execute_task_with_cachefunction is not a best practice. It can lead to unnecessary overhead and may affect performance. Consider moving the import statement to the top of the file.
| try: # The PyFluxExecutor requires flux-base to be installed. | ||
| from executorlib.interactive.flux import FluxPythonSpawner | ||
| except ImportError: | ||
| pass |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Simplify ImportError handling using contextlib.suppress
Instead of using a try-except block with a pass, you can use contextlib.suppress(ImportError) for cleaner and more readable code.
Apply this diff to refactor the code:
+from contextlib import suppress
-try: # The PyFluxExecutor requires flux-base to be installed.
- from executorlib.interactive.flux import FluxPythonSpawner
-except ImportError:
- pass
+with suppress(ImportError): # The PyFluxExecutor requires flux-base to be installed.
+ from executorlib.interactive.flux import FluxPythonSpawner📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: # The PyFluxExecutor requires flux-base to be installed. | |
| from executorlib.interactive.flux import FluxPythonSpawner | |
| except ImportError: | |
| pass | |
| from contextlib import suppress | |
| with suppress(ImportError): # The PyFluxExecutor requires flux-base to be installed. | |
| from executorlib.interactive.flux import FluxPythonSpawner |
🧰 Tools
🪛 Ruff
32-35: Use
contextlib.suppress(ImportError)instead oftry-except-pass(SIM105)
| gpus_per_worker: int = 0, | ||
| cwd: Optional[str] = None, | ||
| openmpi_oversubscribe: bool = False, | ||
| slurm_cmd_args: list[str] = [], |
There was a problem hiding this comment.
Avoid mutable default arguments to prevent unexpected behavior
Using mutable default arguments like [] can lead to unexpected behavior because the same object is shared across all calls to the function. Replace the default value with None and initialize the mutable object within the function.
Apply this diff to fix the issue:
def create_executor(
max_workers: int = 1,
backend: str = "local",
max_cores: int = 1,
cache_directory: Optional[str] = None,
cores_per_worker: int = 1,
threads_per_core: int = 1,
gpus_per_worker: int = 0,
cwd: Optional[str] = None,
openmpi_oversubscribe: bool = False,
- slurm_cmd_args: list[str] = [],
+ slurm_cmd_args: Optional[list[str]] = None,
flux_executor=None,
flux_executor_pmi_mode: Optional[str] = None,
flux_executor_nesting: bool = False,
hostname_localhost: Optional[bool] = None,
block_allocation: bool = False,
init_function: Optional[callable] = None,
):
"""
...
"""
+ if slurm_cmd_args is None:
+ slurm_cmd_args = []📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| slurm_cmd_args: list[str] = [], | |
| slurm_cmd_args: Optional[list[str]] = None, |
🧰 Tools
🪛 Ruff
159-159: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
| def __init__( | ||
| self, | ||
| max_workers: int = 1, | ||
| executor_kwargs: dict = {}, |
There was a problem hiding this comment.
Avoid using mutable default arguments
Using a mutable default value like {} for executor_kwargs can lead to unexpected behavior because the same dictionary is shared across all calls. It's better to use None as the default value and initialize a new dictionary inside the method.
Apply this diff to fix the issue:
- executor_kwargs: dict = {},
+ executor_kwargs: Optional[dict] = None,And add the initialization inside the __init__ method:
if executor_kwargs is None:
executor_kwargs = {}🧰 Tools
🪛 Ruff
161-161: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
| def __init__( | ||
| self, | ||
| max_cores: int = 1, | ||
| executor_kwargs: dict = {}, |
There was a problem hiding this comment.
Avoid using mutable default arguments
Similar to the previous comment, using {} as a default value for executor_kwargs can cause unintended side effects. It's advisable to set the default to None and initialize a new dictionary within the method.
Apply this diff to fix the issue:
- executor_kwargs: dict = {},
+ executor_kwargs: Optional[dict] = None,And add the initialization inside the __init__ method:
if executor_kwargs is None:
executor_kwargs = {}🧰 Tools
🪛 Ruff
213-213: Do not use mutable data structures for argument defaults
Replace with
None; initialize within function(B006)
Summary by CodeRabbit
Release Notes
New Features
ExecutorBaseclass for managing asynchronous tasks.cancel_items_in_queuefunction to handle task cancellations in queues.cloudpickle_registerfor module registration in pickling.ExecutorWithDependenciesclass to manage task submissions with dependencies.Refactor
Bug Fixes
Documentation