Tips N Tricks #4: Using joblib to speed up almost any function (example 1)

TL;DR

Learn how to improve the speed of your pipelines using multi-processing, demonstrated with a practical example in Python.

Transcript

hello everyone and welcome to the new to video in this video I'm going to show you with only one example how you can improve the speed of your pipelines using multi processing so if you remember the bank only AI competition in that we had the park' files and we had to read from every line and we saved mhm pickles so that step to a little bit of tim... Read More

Key Insights

• ✖️ By using multi-processing, pipelines can be significantly accelerated, especially in scenarios with computationally intensive tasks.
• 🐎 The joblib library is a powerful tool for parallelizing functions and optimizing the speed of data processing pipelines.
• 💾 Reading and writing data from/to files can be a bottleneck in pipelines, but this can be improved through techniques like using pickle to save intermediate results.
• ✖️ The choice of back-end, such as multi-processing or multi-threading, can impact the performance of a parallelized pipeline.
• 💯 Dividing data and assigning it to different cores or processors can further enhance the speed of pipelines.
• 📚 Monitoring progress during parallel processing can be achieved using libraries like tqdm.
• 😑 The use of generator expressions can help optimize performance while avoiding memory overhead.

Questions & Answers

Q: How can multi-processing improve the speed of pipelines?

Multi-processing allows tasks to be divided and executed simultaneously, utilizing multiple cores or processors. This parallelization reduces execution time and improves overall pipeline speed.

Q: What libraries are used in the video example?

The video example uses the joblib library for multi-processing, as well as pandas for working with DataFrames and tqdm for progress monitoring.

Q: What was the purpose of reading the parker files in the example?

The parker files contained data that needed to be processed. Reading these files and performing operations on them was a step in the pipeline that needed improvement in terms of speed.

Q: How was the speed improvement achieved in the example?

The speed improvement was achieved by parallelizing the calculation of square roots for each value in a DataFrame column. The joblib library was utilized to distribute this computation across multiple cores or processors, reducing the overall execution time.

Summary & Key Takeaways

• The video demonstrates how to use multi-processing to improve the speed of pipelines in Python.

• By using the joblib library and specifying input parallel and delayed, functions can be parallelized.

• The example shows how to calculate the square root of each value in a DataFrame column and save the results.