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ai_performance_profiler

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ai_performance_profiler [2025/05/29 03:15] – [Extending the Framework] 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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 ===== 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.   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.   3. Limit **lru_cache** size with the **maxsize** parameter to ensure memory-efficient caching.  
  
ai_performance_profiler.1748488532.txt.gz · Last modified: 2025/05/29 03:15 by eagleeyenebula