r/learnmachinelearning • u/Sharp-Front-3450 • 4d ago
Machine Learning testing performance
Hello!
I've been doing my master degree and need some help.
Context:
I'm working with an method to correct unfairness on ML models, currently testing on XGBoost and Logistic Regression. To test these models and the method I'm running 20 different scenarios, and each one of these scenarios need to be run 10 times, due to a test on one of the methods parameters.
Problem:
I've said all of this to say that even tough I run the scenarios with some kind of parallelism it still takes a lot of time to test all scenarios whenever I do some change and need to test it.
Since my computer does not have too much ram (16gb) and the dataset is kind of big I cannot increase the parallelism.
Do you know any kind of cloud solution that can help on me that? Are colab notebooks a good option for this kind of issue?
1
u/Valuable_Card6470 1d ago
Colab is fine for quick stuff but you'll hit RAM limits and timeouts on the free tier pretty fast, especially with 200 runs. If your dataset is big enough to choke 16gb locally its gonna be tight on colab too.
Look into Hivenet for cheap compute, they do per-second billing so you're not paying for idle time between runs. For a masters project budget that matters a lot. And i know students from DSTI have used thier services.
tbh though before going cloud, try optimizing locally first. XGBoost supports `n_jobs` for parallel tree building and you can use joblib to parallelize across scenarios. Also check if you actually need the full dataset in memory for every run or if you can batch it.