Implementation:Mbzuai oryx Awesome LLM Post training Json Dump Checkpoint

Knowledge Sources	Awesome-LLM-Post-training Python json module
Domains	Data_Collection, Fault_Tolerance
Last Updated	2026-02-08 07:30 GMT

Overview

Concrete tool for periodically saving collected paper data to a JSON checkpoint file during deep collection.

Description

Within the main processing loop of deep_collection_sementic.py, the checkpoint logic triggers every 3 papers collected. It uses json.dump to write the entire accumulated data list to papers_temp.json with indented formatting. This provides crash recovery: if the script is interrupted, the most recent checkpoint preserves all data collected up to the last save point.

Usage

This checkpoint pattern is embedded in the main processing loop. It activates automatically when len(data) % 3 == 0. No explicit call is needed; it is part of the collection pipeline's fault tolerance mechanism.

Code Reference

Source Location

Repository: Awesome-LLM-Post-training
File: scripts/deep_collection_sementic.py
Lines: 123-128

Signature

# Checkpoint logic embedded in processing loop
# Triggered every 3 papers: if len(data) % 3 == 0
with open("papers_temp.json", "w", encoding="utf-8") as f:
    json.dump(data, f, indent=4)

Import

import json

I/O Contract

Inputs

Name	Type	Required	Description
data	list[dict]	Yes	Accumulated list of paper detail dictionaries from fetch_paper_details

Outputs

Name	Type	Description
papers_temp.json	File	JSON file containing all papers collected so far, formatted with indent=4

Usage Examples

Checkpoint Pattern in Collection Loop

import json

data = []
for idx, paper in enumerate(papers):
    paper_id = paper.get("paperId")
    if paper_id:
        paper_details = fetch_paper_details(paper_id)
        if paper_details:
            data.append(paper_details)

    # Save every 3 papers to avoid data loss
    if len(data) % 3 == 0:
        with open("papers_temp.json", "w", encoding="utf-8") as f:
            json.dump(data, f, indent=4)
        print(f"Saved {len(data)} papers (Temporary)")

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