Implement Executor interface #333
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WalkthroughThe changes introduce a new module for parallel task execution in Python, utilizing the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant ParallelExecutor
participant TaskManager
participant LAMMPS
User->>TaskManager: Define tasks
TaskManager->>ParallelExecutor: Convert tasks to list
ParallelExecutor->>LAMMPS: Execute tasks in parallel
LAMMPS-->>ParallelExecutor: Return results
ParallelExecutor->>TaskManager: Convert results to dictionary
TaskManager-->>User: Return final results
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Actionable comments posted: 1
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (2)
- atomistics/shared/parallel.py (1 hunks)
- tests/test_evcurve_lammps_function_parallel.py (1 hunks)
Additional context used
Ruff
atomistics/shared/parallel.py
20-20: Use
key not in dictinstead ofkey not in dict.keys()Remove
.keys()(SIM118)
Additional comments not posted (3)
atomistics/shared/parallel.py (2)
4-12: LGTM!The function
_convert_task_dict_to_task_lstis well-implemented and handles both nested and non-nested task data effectively.
33-42: Well-implemented parallel execution function.The function
evaluate_with_parallel_executoreffectively utilizes theconcurrent.futureslibrary for parallel task execution. The use of helper functions for converting task formats before and after execution is a good practice.tests/test_evcurve_lammps_function_parallel.py (1)
27-79: Comprehensive and well-structured test method.The test method
test_calc_evcurve_functionalis well-structured and covers the setup, execution, and validation of results effectively. It makes good use of theevaluate_with_parallel_executorfunction for parallel execution. Consider adding more detailed assertions to ensure the robustness of the test, especially for edge cases and error handling.
atomistics/shared/parallel.py
Outdated
| def _convert_task_lst_to_task_dict(task_lst: list) -> dict: | ||
| task_dict = {} | ||
| for task in task_lst: | ||
| for task_name, task_data in task.items(): | ||
| if isinstance(task_data, dict): | ||
| if task_name not in task_dict.keys(): | ||
| task_dict[task_name] = {} | ||
| task_dict[task_name].update( | ||
| { | ||
| task_parameter: task_object | ||
| for task_parameter, task_object in task_data.items() | ||
| } | ||
| ) | ||
| else: | ||
| task_dict[task_name] = task_data | ||
| return task_dict |
There was a problem hiding this comment.
Optimize dictionary key check.
The function _convert_task_lst_to_task_dict is correctly implemented. However, you can optimize the dictionary key check by removing .keys() as suggested by the static analysis tool. This change will make the code cleaner and slightly more efficient.
Apply this diff to optimize the dictionary key check:
- if task_name not in task_dict.keys():
+ if task_name not in task_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.
| def _convert_task_lst_to_task_dict(task_lst: list) -> dict: | |
| task_dict = {} | |
| for task in task_lst: | |
| for task_name, task_data in task.items(): | |
| if isinstance(task_data, dict): | |
| if task_name not in task_dict.keys(): | |
| task_dict[task_name] = {} | |
| task_dict[task_name].update( | |
| { | |
| task_parameter: task_object | |
| for task_parameter, task_object in task_data.items() | |
| } | |
| ) | |
| else: | |
| task_dict[task_name] = task_data | |
| return task_dict | |
| def _convert_task_lst_to_task_dict(task_lst: list) -> dict: | |
| task_dict = {} | |
| for task in task_lst: | |
| for task_name, task_data in task.items(): | |
| if isinstance(task_data, dict): | |
| if task_name not in task_dict: | |
| task_dict[task_name] = {} | |
| task_dict[task_name].update( | |
| { | |
| task_parameter: task_object | |
| for task_parameter, task_object in task_data.items() | |
| } | |
| ) | |
| else: | |
| task_dict[task_name] = task_data | |
| return task_dict |
Tools
Ruff
20-20: Use
key not in dictinstead ofkey not in dict.keys()Remove
.keys()(SIM118)
# Conflicts: # atomistics/shared/parallel.py
There was a problem hiding this comment.
Actionable comments posted: 0
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (1)
- atomistics/shared/parallel.py (1 hunks)
Additional context used
Ruff
atomistics/shared/parallel.py
40-40: Use
key not in dictinstead ofkey not in dict.keys()Remove
.keys()(SIM118)
Additional comments not posted (3)
atomistics/shared/parallel.py (3)
4-22: Well-documented and correctly implemented.The function
_convert_task_dict_to_task_lstis well-documented and the implementation correctly converts a dictionary of tasks into a list format. This is crucial for the flexibility of task handling in parallel execution contexts.
25-50: Optimize dictionary key check.The function
_convert_task_lst_to_task_dictis correctly implemented. However, you can optimize the dictionary key check by removing.keys()as suggested by the static analysis tool. This change will make the code cleaner and slightly more efficient.Apply this diff to optimize the dictionary key check:
- if task_name not in task_dict.keys(): + if task_name not in task_dict:Tools
Ruff
40-40: Use
key not in dictinstead ofkey not in dict.keys()Remove
.keys()(SIM118)
53-75: Effective use of the Executor interface.The function
evaluate_with_parallel_executoreffectively uses the Executor interface to execute tasks in parallel. It integrates the task conversion functions_convert_task_dict_to_task_lstand_convert_task_lst_to_task_dictto handle task formats efficiently. This implementation should provide robust parallel execution capabilities.
Summary by CodeRabbit
New Features
Bug Fixes