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ai_performance_profiler

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ai_performance_profiler [2025/05/29 03:05] – [Workflow] 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.
 <code> <code>
-   Instantiate the PerformanceProfiler class. Optionally specify the log file's name. 
-    
    python    python
    profiler = PerformanceProfiler(log_file="custom_log.log")    profiler = PerformanceProfiler(log_file="custom_log.log")
-</code>   ```+</code>
  
-2. **Profiling a Stage:**  +2. **Profiling a Stage:**  
 +   * Use **profile_stage** to wrap critical tasks, functions, or pipeline stages, while passing the required arguments.
 <code> <code>
-   Use profile_stage to wrap critical tasks, functions, or pipeline stages, while passing the required arguments. 
-    
    python    python
    result = profiler.profile_stage("Data Preprocessing", preprocess_function, input_data)    result = profiler.profile_stage("Data Preprocessing", preprocess_function, input_data)
 </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.
 <code>  <code> 
-   Use cache_step as a decorator on computationally heavy functions to automatically cache results for repeated calls. 
    python    python
    @profiler.cache_step    @profiler.cache_step
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        time.sleep(5)        time.sleep(5)
        return x ** 2        return x ** 2
-  
 </code> </code>
 4. **Analyze Logs:** 4. **Analyze Logs:**
-  
    * Review the generated log file (**performance.log** or user-specified file) to track profiling results.    * Review the generated log file (**performance.log** or user-specified file) to track profiling results.
  
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 Below are advanced usage scenarios of the Performance Profiler: Below are advanced usage scenarios of the Performance Profiler:
- 
---- 
- 
 ==== Example 1: Profiling Multiple Pipeline Stages ==== ==== Example 1: Profiling Multiple Pipeline Stages ====
  
 Profile a workflow consisting of multiple pipeline stages, such as data preprocessing, training, and evaluation. Profile a workflow consisting of multiple pipeline stages, such as data preprocessing, training, and evaluation.
- +<code> 
-```python+python
 from ai_performance_profiler import PerformanceProfiler from ai_performance_profiler import PerformanceProfiler
  
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     time.sleep(1)  # Simulate evaluation     time.sleep(1)  # Simulate evaluation
     return "Evaluation Metrics"     return "Evaluation Metrics"
- +</code> 
-# Profiling each stage+**Profiling each stage** 
 +<code>
 data = profiler.profile_stage("Preprocessing", preprocess, "Raw Data") data = profiler.profile_stage("Preprocessing", preprocess, "Raw Data")
 model = profiler.profile_stage("Training", train_model, data) model = profiler.profile_stage("Training", train_model, data)
 metrics = profiler.profile_stage("Evaluation", evaluate_model, model) metrics = profiler.profile_stage("Evaluation", evaluate_model, model)
-```+</code>
  
 **Log Output:** **Log Output:**
-```+<code>
 INFO: Stage 'Preprocessing' completed in 2.00 seconds. INFO: Stage 'Training' completed in 3.00 seconds. INFO: Stage 'Evaluation' completed in 1.00 seconds. INFO: Stage 'Preprocessing' completed in 2.00 seconds. INFO: Stage 'Training' completed in 3.00 seconds. INFO: Stage 'Evaluation' completed in 1.00 seconds.
-```  +</code>
- +
---- +
 ==== Example 2: Implementing Custom Caching Logic ==== ==== Example 2: Implementing Custom Caching Logic ====
  
 Use caching to optimize a computation-heavy function like determining Fibonacci numbers. Use caching to optimize a computation-heavy function like determining Fibonacci numbers.
- +<code> 
-```python+python
 @profiler.cache_step @profiler.cache_step
 def calculate_fibonacci(n): def calculate_fibonacci(n):
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         return n         return n
     return calculate_fibonacci(n - 1) + calculate_fibonacci(n - 2)     return calculate_fibonacci(n - 1) + calculate_fibonacci(n - 2)
- +</code> 
-# First call will compute the result+**First call will compute the result** 
 +<code>
 result = calculate_fibonacci(35) result = calculate_fibonacci(35)
- +</code> 
-# Subsequent calls with the same input will use the cache+**Subsequent calls with the same input will use the cache** 
 +<code>
 cached_result = calculate_fibonacci(35) cached_result = calculate_fibonacci(35)
-``` +</code>
- +
----+
  
 ==== Example 3: Profiling with Dynamic Arguments ==== ==== Example 3: Profiling with Dynamic Arguments ====
  
-Profile a function with dynamic arguments using `**kwargs`. +Profile a function with dynamic arguments using <code>**kwargs</code>. 
- +<code> 
-```python+python
 def dynamic_function(a, b, **kwargs): def dynamic_function(a, b, **kwargs):
     time.sleep(1)  # Simulate computation     time.sleep(1)  # Simulate computation
     return a + b + sum(kwargs.values())     return a + b + sum(kwargs.values())
- +</code> 
-# Profiling with keyword arguments+**Profiling with keyword arguments** 
 +<code>
 result = profiler.profile_stage("Dynamic Addition", dynamic_function, 10, 20, c=30, d=40) result = profiler.profile_stage("Dynamic Addition", dynamic_function, 10, 20, c=30, d=40)
-``` +</code>
- +
---- +
 ==== Example 4: Combining Profiling and Caching ==== ==== Example 4: Combining Profiling and Caching ====
  
 Simultaneously cache and profile a function. Simultaneously cache and profile a function.
- +<code> 
-```python+python
 @profiler.cache_step @profiler.cache_step
 def simulate_heavy_task(x): def simulate_heavy_task(x):
     time.sleep(1)     time.sleep(1)
     return x * 2     return x * 2
- +</code> 
-# Profile the cached function+**Profile the cached function** 
 +<code>
 result = profiler.profile_stage("Cached Task", simulate_heavy_task, 10) result = profiler.profile_stage("Cached Task", simulate_heavy_task, 10)
-``` +</code>
- +
----+
  
 ===== Extending the Framework ===== ===== Extending the Framework =====
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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.1748487941.txt.gz · Last modified: 2025/05/29 03:05 by eagleeyenebula