I'm working on a project using Node.js with Express to filter Reddit posts based on pain points and identify potential issues. Currently, I'm using the Reddit API to fetch posts, but the filtering process is very slow, especially when dealing with a large number of posts. I've implemented a pain point classifier using a Zero Shot Classification model from HuggingFace, calling it through a separate Python API. However, my batch processing is taking an excessive amount of time — more than 5 minutes for just a batch of 20 posts! This means if I'm fetching posts from multiple subreddits, it can easily turn into over an hour of waiting. I'm on a tight budget and can't afford paid services like GummySearch, so I'm really hoping to find some optimization strategies. Could running this on a GPU improve the performance? What other tips do you have for speeding this process up?
2 Answers
First off, check if you're using the GPU in your setup. Since you have an NVIDIA 3050 RTX, that could lead to significant speedups for the HuggingFace model. Make sure to install the required libraries like CUDA and the appropriate version of PyTorch that supports GPU. Also, batching more efficiently — say, increasing your batch size and managing parallel requests carefully — could help. If possible, consider using asynchronous programming to improve the overall fetch and classify workflow instead of waiting for each batch to finish one at a time. Good luck!
A "pain point" generally refers to a specific problem or frustration faced by users, like struggles, annoyances, or things that make life harder. It's great that you're trying to identify these issues through Reddit posts! The more clarity you have on what kinds of pain points you're focusing on, the better your classifier's performance will be. Furthermore, analyze your classification logic — ensuring you're only sending relevant posts and filtering effectively before they reach the classifier could save time too.
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