Understanding the need for data versioning
Data versioning is particularly important in LLM projects due to the massive scale and complexity of language datasets. As an LLM engineer, you need to track changes in your datasets to ensure the reproducibility of your models and maintain a clear history of data modifications.
Let’s start by implementing a basic data versioning system using Python:
from datetime import datetime
import hashlib
import json
class DatasetVersion:
def __init__(self, data, metadata=None):
self.data = data
self.metadata = metadata or {}
self.timestamp = datetime.now().isoformat()
//creation timestamp for each version
self.version_hash = self._generate_hash()
def _generate_hash(self):
...
