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138 lines
5.2 KiB
138 lines
5.2 KiB
# Module/Function Name: BaseStructure
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## Introduction:
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The `BaseStructure` module contains the basic structure and attributes required for running machine learning models and associated metadata, error logging, artifact saving/loading, and relevant event logging.
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The module provides the flexibility to save and load the model metadata, log errors, save artifacts, and maintain a log for multiple events associated with multiple threads and batched operations. The key attributes of the module include **name**, **description**, **save_metadata_path**, and **save_error_path**.
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## Class Definition:
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### Arguments:
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| Argument | Type | Description |
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|----------------------|--------|----------------------------------------------------------------------|
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| name | str | (Optional) The name of the structure. |
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| description | str | (Optional) A description of the structure. |
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| save_metadata | bool | A boolean flag to enable or disable metadata saving. |
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| save_artifact_path | str | (Optional) The path to save artifacts. |
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| save_metadata_path | str | (Optional) The path to save metadata. |
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| save_error_path | str | (Optional) The path to save errors. |
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## Methods:
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### 1. run
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Runs the structure.
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### 2. save_to_file
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Saves data to a file.
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* **data**: Value to be saved.
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* **file_path**: Path where the data is to be saved.
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### 3. load_from_file
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Loads data from a file.
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* **file_path**: Path from where the data is to be loaded.
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### 4. save_metadata
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Saves metadata to a file.
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* **metadata**: Data to be saved as metadata.
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### 5. load_metadata
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Loads metadata from a file.
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### 6. log_error
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Logs error to a file.
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### 7. save_artifact
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Saves artifact to a file.
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* **artifact**: The artifact to be saved.
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* **artifact_name**: Name of the artifact.
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### 8. load_artifact
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Loads artifact from a file.
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* **artifact_name**: Name of the artifact.
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### 9. log_event
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Logs an event to a file.
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* **event**: The event to be logged.
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* **event_type**: Type of the event (optional, defaults to "INFO").
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### 10. run_async
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Runs the structure asynchronously.
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### 11. save_metadata_async
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Saves metadata to a file asynchronously.
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### 12. load_metadata_async
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Loads metadata from a file asynchronously.
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### 13. log_error_async
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Logs error to a file asynchronously.
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### 14. save_artifact_async
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Saves artifact to a file asynchronously.
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### 15. load_artifact_async
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Loads artifact from a file asynchronously.
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### 16. log_event_async
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Logs an event to a file asynchronously.
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### 17. asave_to_file
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Saves data to a file asynchronously.
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### 18. aload_from_file
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Loads data from a file asynchronously.
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### 19. run_concurrent
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Runs the structure concurrently.
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### 20. compress_data
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Compresses data.
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### 21. decompres_data
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Decompresses data.
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### 22. run_batched
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Runs batched data.
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## Examples:
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### Example 1: Saving Metadata
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```python
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base_structure = BaseStructure(name="ExampleStructure")
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metadata = {"key1": "value1", "key2": "value2"}
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base_structure.save_metadata(metadata)
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```
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### Example 2: Loading Artifact
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```python
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artifact_name = "example_artifact"
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artifact_data = base_structure.load_artifact(artifact_name)
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```
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### Example 3: Running Concurrently
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```python
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concurrent_data = [data1, data2, data3]
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results = base_structure.run_concurrent(batched_data=concurrent_data)
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```
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## Note:
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The `BaseStructure` class is designed to provide a modular and extensible structure for managing metadata, logs, errors, and batched operations while running machine learning models. The class's methods offer asynchronous and concurrent execution capabilities, thus optimizing the performance of the associated applications and models. The module's attributes and methods cater to a wide range of use cases, making it an essential foundational component for machine learning and data-based applications.
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# Conclusion:
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The `BaseStructure` module offers a robust and flexible foundation for managing machine learning model metadata, error logs, and event tracking, including asynchronous, concurrent, and batched operations. By leveraging the inherent capabilities of this class, developers can enhance the reliability, scalability, and performance of machine learning-based applications.
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## References:
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- [Python Concurrent Programming with `asyncio`](https://docs.python.org/3/library/asyncio.html)
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- [Understanding Thread Pool Executor in Python](https://docs.python.org/3/library/concurrent.futures.html#executor-objects)
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- [Documentation on `gzip` Module for Data Compression](https://docs.python.org/3/library/gzip.html)
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---
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The above documentation provides detailed information about the `BaseStructure` module, including its functionality, attributes, methods, usage examples, and references to relevant resources for further exploration. This comprehensive documentation aims to deepen the users' understanding of the module's purpose and how it can be effectively utilized in practice.
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Please let me know if you need further elaboration on any specific aspect or functionality of the `BaseStructure` module.
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