Tracking progress of joblib.Parallel execution

Tracking progress of joblib.Parallel execution

joblib.Parallel is a powerful library for parallelizing computations in Python. If you want to track the progress of the tasks being executed in parallel using joblib, you can achieve this by combining joblib.Parallel with a progress bar library like tqdm.

Here's how you can use tqdm to track the progress of tasks executed in parallel using joblib.Parallel:

  • Install the joblib and tqdm libraries if you haven't already:

    pip install joblib tqdm

  • Import the required libraries:

import time from tqdm import tqdm from joblib import Parallel, delayed
  • Define the function that represents the task you want to parallelize:
def process_item(item): # Simulate some work time.sleep(1) return f"Processed {item}"
  • Parallelize the task using joblib.Parallel and track the progress using tqdm:
num_jobs = 10 items = range(num_jobs) # Wrap the Parallel call with tqdm to track progress results = Parallel(n_jobs=-1)( delayed(process_item)(item) for item in tqdm(items, total=num_jobs) ) for result in results: print(result)

In this example, the process_item function represents the task you want to parallelize. The Parallel function from joblib is used to execute the tasks in parallel. The tqdm function is used to wrap the list comprehension that generates the parallel tasks, providing a progress bar that tracks the completion of tasks.

Adjust num_jobs and the process_item function according to your use case.

Please note that tracking progress in parallel computations is inherently complex, and the level of accuracy may depend on the nature of the tasks and the processing time of each task. The example provided here simplifies the progress tracking for demonstration purposes.

Examples

  1. How to track the progress of joblib.Parallel execution?

    • Description: Use tqdm to create a progress bar for joblib.Parallel execution, allowing you to monitor the progress of parallelized tasks.

    • Code:

      !pip install joblib tqdm # Ensure joblib and tqdm are installed 
      import joblib from tqdm import tqdm import numpy as np def square(x): return x ** 2 inputs = np.arange(1, 11) # Wrap joblib.Parallel with tqdm results = joblib.Parallel(n_jobs=4)( joblib.delayed(square)(x) for x in tqdm(inputs, desc="Processing") ) print("Results:", results)

  2. How to display progress for joblib.Parallel execution with custom callbacks?

    • Description: Implement a custom callback to track the progress of joblib.Parallel execution.
    • Code:
      import joblib from tqdm import tqdm class ProgressCallback(object): def __init__(self, total): self._tqdm = tqdm(total=total, desc="Processing") def __call__(self, _): self._tqdm.update(1) def process_task(x): import time time.sleep(1) # Simulate work return x * 2 data = range(10) progress = ProgressCallback(len(data)) results = joblib.Parallel(n_jobs=4)( joblib.delayed(process_task)(x) for x in data )

  3. How to use joblib.Parallel with nested progress tracking?

    • Description: Implement nested progress tracking for joblib.Parallel to visualize progress for nested tasks.
    • Code:
      import joblib from tqdm import tqdm # Outer progress outer_data = range(5) def process_outer(x): # Inner progress inner_data = range(5) inner_results = joblib.Parallel(n_jobs=2)( joblib.delayed(lambda y: y * 2)(y) for y in tqdm(inner_data, desc=f"Inner loop {x}") ) return sum(inner_results) outer_results = joblib.Parallel(n_jobs=2)( joblib.delayed(process_outer)(x) for x in tqdm(outer_data, desc="Outer loop") ) print("Results:", outer_results)

  4. How to monitor memory usage during joblib.Parallel execution?

    • Description: Use a custom callback to monitor memory usage during joblib.Parallel execution to avoid exceeding memory limits.

    • Code:

      !pip install psutil tqdm # Install necessary packages 
      import joblib import psutil from tqdm import tqdm # Custom callback to monitor memory usage class MemoryMonitor(object): def __init__(self, total_tasks): self._tqdm = tqdm(total=total_tasks, desc="Task Progress") def __call__(self, _): memory_used = psutil.Process().memory_info().rss self._tqdm.set_postfix(memory=f"{memory_used / (1024 * 1024):.2f} MB") self._tqdm.update(1) def process_task(x): import time time.sleep(1) # Simulate work return x * 2 data = range(10) monitor = MemoryMonitor(len(data)) results = joblib.Parallel(n_jobs=4)( joblib.delayed(process_task)(x) for x in data ) print("Results:", results)

  5. How to use joblib.Parallel with detailed error tracking?

    • Description: Implement error handling to track errors during joblib.Parallel execution, providing detailed feedback.
    • Code:
      import joblib from tqdm import tqdm def error_prone_task(x): if x % 2 == 0: raise ValueError("Even number detected!") return x * 2 data = range(10) results = [] errors = [] with tqdm(total=len(data), desc="Processing") as pbar: for res in joblib.Parallel(n_jobs=4, backend='threading')( joblib.delayed(error_prone_task)(x) for x in data ): try: results.append(res) except Exception as e: errors.append(e) finally: pbar.update(1) print("Results:", results) print("Errors:", errors)

  6. How to track the execution time of joblib.Parallel tasks?

    • Description: Measure the execution time for each task in joblib.Parallel to identify bottlenecks and optimize performance.
    • Code:
      import time import joblib from tqdm import tqdm def timed_task(x): start_time = time.time() time.sleep(1) # Simulate work duration = time.time() - start_time return x * 2, duration data = range(10) results = [] times = [] with tqdm(total=len(data), desc="Processing") as pbar: for res, duration in joblib.Parallel(n_jobs=4)( joblib.delayed(timed_task)(x) for x in data ): results.append(res) times.append(duration) pbar.update(1) print("Results:", results) print("Execution times:", times)

  7. How to visualize joblib.Parallel execution with progress bars?

    • Description: Use tqdm to visualize joblib.Parallel execution with progress bars for better user feedback.
    • Code:
      import joblib from tqdm import tqdm def task(x): import time time.sleep(1) # Simulate work return x * 2 data = range(10) results = joblib.Parallel(n_jobs=4)( joblib.delayed(task)(x) for x in tqdm(data, desc="Parallel execution") ) print("Results:", results)

  8. How to track joblib.Parallel execution with logging for debugging?

    • Description: Implement logging to track joblib.Parallel execution, allowing you to debug and trace job execution.

    • Code:

      !pip install joblib tqdm # Ensure necessary libraries are installed 
      import joblib import logging from tqdm import tqdm # Set up logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s') def task(x): import time time.sleep(1) # Simulate work logging.info(f"Task {x} completed.") return x * 2 data = range(10) results = joblib.Parallel(n_jobs=4)( joblib.delayed(task)(x) for x in tqdm(data, desc="Processing tasks") ) print("Results:", results)

  9. How to use joblib.Parallel with progress tracking and retries?

    • Description: Implement progress tracking with joblib.Parallel, including automatic retries for failed tasks.
    • Code:
      import joblib from tqdm import tqdm import time class RetryableTask(object): def __init__(self, x): self.x = x self.retries = 0 def __call__(self): if self.retries < 3: self.retries += 1 if self.x % 2 == 0: raise Exception("Retryable error") return self.x * 2 else: raise Exception("Too many retries") data = [RetryableTask(x) for x in range(10)] results = [] with tqdm(total=len(data), desc="Retryable tasks") as pbar: for task in data: try: result = joblib.Parallel(n_jobs=4)( joblib.delayed(task)() )[0] results.append(result) except Exception as e: print(f"Task failed: {e}") pbar.update(1) print("Results:", results)

  10. How to use joblib.Parallel with progress tracking and cleanup?

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