Hacker News: Optimizing Jupyter Notebooks for LLMs

Jan 21, 2025

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Source URL: https://www.alexmolas.com/2025/01/15/ipynb-for-llm.html
Source: Hacker News
Title: Optimizing Jupyter Notebooks for LLMs

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The text discusses optimizing Jupyter Notebooks for use with Large Language Models (LLMs), highlighting an experience of unexpected cost surges due to the verbose nature of .ipynb files. It provides practical solutions for reducing associated costs by converting notebooks to Python scripts and removing unnecessary data.

Detailed Description:
The author shares a personal experience utilizing LLM-assisted coding, emphasizing the convenience of accessing multiple models through OpenRouter. However, they encountered significant budget increases, prompting an investigation into costs tied to LLM calls. This investigation revealed that the structure of Jupyter Notebook files was contributing to high token counts, primarily due to:

– **Code and Outputs**: Each cell retains input, output, and error messages, making each notebook more data-heavy.
– **Rich Metadata**: Information about the execution state, timing, and formatting is embedded in each cell.
– **Base64-encoded Images**: Visual content generated in notebooks is stored as base64 strings, which can add substantial weight to the overall file size.

Key takeaways from the experience include practical recommendations for professionals involved in AI and infrastructure security:

– **Cost Awareness**: Users should monitor spending and utilize tools like OpenRouter for transparent cost tracking.
– **File Management**: Converting notebooks to Python scripts helps to streamline data and minimize costs related to token usage when interacting with LLMs. The author shared an effective bash script to both convert files and eliminate heavy base64 content, resulting in a 94% cost reduction.
– **Caution with Content**: Understanding the hidden content within Jupyter notebooks is crucial, as it can unintentionally inflate interaction costs with LLMs.

This insight is particularly relevant for AI practitioners, data scientists, and infrastructure professionals, shedding light on resource optimization strategies in a landscape where costs of AI operations can quickly escalate.

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