Optimizing Python Performance for Data Science on Intel Evo Laptops

Question

Hi everyone!

I’ve been working with Python for Data Science, and I wanted to get your thoughts on optimizing performance, especially with large datasets and machine learning models. I’m using a laptop with an Intel Evo[https://www.lenovo.com/sg/en/faqs/intel/intel-evo-platform/] processor, and I’ve noticed that tasks like training neural networks or processing big datasets can sometimes be slow.
Challenges:

1. Data Processing: Operations like merging large datasets in pandas can be slow. I’ve tried vectorizing some loops, but performance is still an issue.
2. Machine Learning: Training larger models in TensorFlow or scikit-learn seems slower on my Intel Evo laptop than I expected. Are there any optimizations to better utilize multi-core CPUs or hardware acceleration?
3. Memory Management: Large datasets sometimes cause slowdowns. Any Python libraries or tips for handling memory more efficiently?

Questions:
• Has anyone used an Intel Evo laptop for Python or data science? Any tips for maximizing performance?
• What libraries or coding techniques have you found effective for speeding up your Python code?

Looking forward to your insights!

Answer 1

Reported as spam. This is a cleverly-disguised ad meant to increase traffic on the website.

Answer 2

I'm not sure is this post would potentially increase traffic on the website, however, if you mean want to increase performance on Intel CPUs with scikit-learn, you can try Intel Extension for Scikit-learn.

https://intel.github.io/scikit-learn-intelex/latest/index.html

Just make sure the hardware is supported.

Answer 3

Wong Hei Ming a number of similar posts in this style have been appearing. A new user posts a question that conspicuously includes a link to specs on a Lenovo laptop. Often it is the same laptop. Often the question could have been asked without regard to which computer is being used. So far in every case the question is their only activity on Sololearn apart from opening a course or two, and the OPs have never followed up any further to questions.

Answer 4

As your data gets larger, it is harder to manage these massive operations. At some point you need to look at storing your data in a SQL database.

The benefits of using a database, like MySQL, are that the major operations can be performed inside the database. You extract summary data or filtered data and let python chart that. In a perfect work, the SQL query will fetch the results you want and Python will simply take care of displaying that data.

The other consideration is memory. How much RAM does your program require vs how much RAM does your PC have available. You can use task manager while the app is running to get a feel for how much RAM you are using.

As far as using more cores, I'm not strong in that area. But you might have trouble using async techniques on single operations. There may be some specific advice in groups dedicated to TensorFlow. SoloLearn students might not have the expertise. But I always start with memory requirements as that's usually where the issue is.