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ai_performance_profiler [2025/05/29 03:11] – [Example 4: Combining Profiling and Caching] eagleeyenebulaai_performance_profiler [2025/05/29 03:21] (current) – [Key Features] eagleeyenebula
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 ===== Key Features ===== ===== Key Features =====
  
-1. **Execution Time Profiling (`profile_stage`)**+1. **Execution Time Profiling **(profile_stage)****
    * Dynamically analyze the time spent by functions or code blocks.    * Dynamically analyze the time spent by functions or code blocks.
    * Logs all execution times to a log file for a clear historical performance record.    * Logs all execution times to a log file for a clear historical performance record.
  
-2. **Caching (`cache_step`)**+2. **Caching **(cache_step)****
    * Built-in caching system to store computationally expensive function results.    * Built-in caching system to store computationally expensive function results.
-   * Uses `functools.lru_cache` with a configurable cache size for efficient memory utilization.+   * Uses **functools.lru_cache** with a configurable cache size for efficient memory utilization.
  
 3. **Integrated Logging** 3. **Integrated Logging**
-   * Automatically logs key performance metrics, such as execution time, to a file (`performance.log` by default).+   * Automatically logs key performance metrics, such as execution time, to a file (**performance.log** by default).
  
 4. **Modularity** 4. **Modularity**
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 5. **Lightweight and Extensible** 5. **Lightweight and Extensible**
-   * Minimal overhead compared to traditional profiling tools like cProfile or line_profiler.+   * Minimal overhead compared to traditional profiling tools like **cProfile** or **line_profiler**.
 ===== How It Works ===== ===== How It Works =====
  
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 1. **Initialization:** 1. **Initialization:**
-   * Instantiate the PerformanceProfiler class. Optionally specify the log file's name.+   * Instantiate the **PerformanceProfiler** class. Optionally specify the log file's name.
 <code> <code>
    python    python
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 2. **Profiling a Stage:**  2. **Profiling a Stage:** 
-   * Use profile_stage to wrap critical tasks, functions, or pipeline stages, while passing the required arguments.+   * Use **profile_stage** to wrap critical tasks, functions, or pipeline stages, while passing the required arguments.
 <code> <code>
    python    python
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 </code> </code>
 3. **Caching Expensive Steps:** 3. **Caching Expensive Steps:**
-   * Use cache_step as a decorator on computationally heavy functions to automatically cache results for repeated calls.+   * Use **cache_step** as a decorator on computationally heavy functions to automatically cache results for repeated calls.
 <code>  <code> 
    python    python
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 The Performance Profiler framework can be extended and customized for broader functionality: The Performance Profiler framework can be extended and customized for broader functionality:
  
-1. **Disk-Based Caching:** Utilize `diskcache` for persistent caching across sessions. +1. **Disk-Based Caching:** Utilize **diskcache** for persistent caching across sessions. 
-    +<code> 
-   ```python+   python
    from diskcache import Cache    from diskcache import Cache
  
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        time.sleep(5)        time.sleep(5)
        return x ** 2        return x ** 2
-   ```+</code>
  
 2. **Integration with Observability Platforms:**   2. **Integration with Observability Platforms:**  
-   Export logs in JSON format and feed metrics into monitoring systems like Grafana or Prometheus.+   * Export logs in **JSON** format and feed metrics into monitoring systems like **Grafana** or Prometheus.
  
-3. **Enhanced Logging:** Implement advanced logging techniques, such as log rotation and JSON-based formatted logs. +3. **Enhanced Logging:** Implement advanced logging techniques, such as log rotation and **JSON-based** formatted logs. 
- +<code> 
-   ```python+   python
    import logging    import logging
    from logging.handlers import RotatingFileHandler    from logging.handlers import RotatingFileHandler
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    logging.basicConfig(handlers=[handler], level=logging.INFO,     logging.basicConfig(handlers=[handler], level=logging.INFO, 
                        format="%(asctime)s - %(levelname)s - %(message)s")                        format="%(asctime)s - %(levelname)s - %(message)s")
-   ``` +</code>
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
-1. Focus profiling on **critical pipeline sections** to avoid introducing unnecessary profiling overhead.   +1. Focus profiling on **critical pipeline sections** to avoid introducing unnecessary profiling overhead.  
-2. Use `cache_step` exclusively for deterministic functions where input-output relationships don’t change.   +  
-3. Limit `lru_cache` size with the `maxsize` parameter to ensure memory-efficient caching.  +2. Use **cache_step** exclusively for deterministic functions where input-output relationships don’t change.  
  
----+3. Limit **lru_cache** size with the **maxsize** parameter to ensure memory-efficient caching.  
  
 ===== Conclusion ===== ===== Conclusion =====
  
 The **PerformanceProfiler** is a lightweight, extensible tool for optimizing pipeline performance. By profiling execution times and leveraging caching, it empowers developers to create efficient, scalable, and reliable workflows for AI systems. Its modularity and performance-oriented design make it essential for both small- and large-scale applications. The **PerformanceProfiler** is a lightweight, extensible tool for optimizing pipeline performance. By profiling execution times and leveraging caching, it empowers developers to create efficient, scalable, and reliable workflows for AI systems. Its modularity and performance-oriented design make it essential for both small- and large-scale applications.
 +
 +Designed with flexibility in mind, the **PerformanceProfiler** can be easily integrated into diverse environments, from experimental prototypes to production-grade pipelines. It provides actionable insights into process-level efficiency and helps reduce redundant computations through smart caching mechanisms. Developers can adapt the profiler to suit custom stages or components, ensuring consistent performance gains across evolving systems and workflows.
ai_performance_profiler.1748488317.txt.gz · Last modified: 2025/05/29 03:11 by eagleeyenebula