Dataset Versioning and Reproducibility

Training large language models is a resource-intensive process, often spanning weeks or months on expensive hardware clusters. Reproducibility is therefore not just a scientific ideal but an engineering necessity. If a training run produces unexpected results, or if you need to revisit a specific model checkpoint from months ago, you must be able to precisely reconstruct the exact dataset state used for that run. Simply storing terabytes of data isn't enough; you need practices for dataset versioning.

Unlike code, which is well-handled by systems like Git, datasets pose unique versioning challenges due to their sheer size. Directly storing multiple complete copies of multi-terabyte datasets in a version control system is impractical and prohibitively expensive. Furthermore, a "version" of a dataset isn't just the raw files; it encompasses the specific preprocessing code, filtering parameters, tokenization settings, and sampling strategies used to create the final training-ready data.

Why Version Datasets?

Effective dataset versioning provides several significant benefits in the context of LLM development:

1. Reproducibility: The primary goal. Being able to recreate the exact data used for a specific training run allows you to reliably reproduce results, debug issues (like loss spikes discussed in Chapter 24), and compare different experiments fairly.
2. Tracking Data Evolution: LLM datasets are rarely static. You might incorporate new data sources, refine cleaning heuristics, or fix bugs in your preprocessing pipeline. Versioning tracks these changes, allowing you to understand how data drift or modifications impact model performance over time.
3. Collaboration: Large projects involve multiple engineers potentially working on data preparation. Versioning provides a clear reference point, ensuring everyone is using the intended version of the data for their tasks.
4. Auditing and Compliance: In some contexts, it might be necessary to trace back the exact data used to train a specific model version for auditing or regulatory purposes.

Components of a Dataset Version

When we talk about versioning an LLM dataset, we typically need to track several interconnected components:

• Processed Data Files: The actual tokenized data files, often sharded, residing in a distributed file system or object store.
• Preprocessing Code: The specific version (e.g., Git commit hash) of the code used for cleaning, filtering, normalization, and tokenization.
• Configuration: Parameters used during preprocessing, such as quality thresholds, deduplication settings, vocabulary size, and special token definitions.
• Source Data Identifiers: Pointers or identifiers for the raw data sources used (e.g., Common Crawl snapshot IDs, web scrape dates, source dataset names and versions).
• Metadata: Checksums or hashes of the processed data files to ensure integrity, dataset statistics (document counts, token counts), and potentially information about data splits (train/validation).

Strategies for Dataset Versioning

Given the scale, versioning often involves managing metadata and pointers rather than copying the data itself. Here are common strategies:

1. Naming Conventions and Manifest Files

A fundamental approach involves using disciplined naming conventions for directories or storage prefixes that include version identifiers, timestamps, or relevant configuration hashes. For example, a processed dataset might reside in a path like s3://my-llm-datasets/processed/v2.1_vocab32k_cc-2023-03/.

Complementing this, you can create manifest files (e.g., in JSON or YAML format) that list all the data files belonging to a specific version, along with their checksums and essential metadata. This manifest file, being small, can be easily tracked using Git alongside the preprocessing code.

import hashlib
import json
import os
from glob import glob

def calculate_sha256(filepath):
  """Calculates the SHA256 hash of a file."""
  sha256_hash = hashlib.sha256()
  with open(filepath, "rb") as f:
    # Read and update hash string value in blocks of 4K
    for byte_block in iter(lambda: f.read(4096), b""):
      sha256_hash.update(byte_block)
  return sha256_hash.hexdigest()

def create_dataset_manifest(data_dir, manifest_path, version_info):
  """Creates a manifest file for files in a directory."""
  manifest = {
      "version_info": version_info,
      "files": []
  }
  
  # Example: Assuming data files are .arrow format
  data_files = sorted(glob(os.path.join(data_dir, "*.arrow"))) 
  
  print(f"Generating manifest for {len(data_files)} files in {data_dir}...")
  
  for filepath in data_files:
    filename = os.path.basename(filepath)
    checksum = calculate_sha256(filepath)
    manifest["files"].append({
        "filename": filename,
        "sha256": checksum,
        "size_bytes": os.path.getsize(filepath) 
    })
    
  with open(manifest_path, 'w') as f:
    json.dump(manifest, f, indent=2)
    
  print(f"Manifest saved to {manifest_path}")

# --- Example Usage ---
dataset_directory = "/path/to/processed_data/v2.1_vocab32k_cc-2023-03" 
output_manifest = "/path/to/repo/dataset_manifests/v2.1.json"
git_commit_hash = "a1b2c3d4e5f6" # Get this programmatically in a real pipeline

version_metadata = {
    "dataset_version": "2.1",
    "preprocessing_code_commit": git_commit_hash,
    "source_info": "Common Crawl March 2023 Snapshot",
    "tokenizer_vocab_size": 32000
}

# Ensure the output directory exists
os.makedirs(os.path.dirname(output_manifest), exist_ok=True) 

# create_dataset_manifest(dataset_directory, output_manifest, version_metadata) 
# Note: Uncomment the line above and replace paths to run; 
# this is illustrative and assumes data files exist at the specified path.
print("Illustrative manifest generation setup complete.")
print(f"Would process files in: {dataset_directory}")
print(f"Would save manifest to: {output_manifest}")
print(f"Associated metadata: {version_metadata}")

This manifest (v2.1.json) can then be committed to Git. To use this dataset version, your training pipeline would read the manifest, verify the checksums (optionally), and load the listed files from their storage location.

2. Dedicated Data Version Control Tools

Tools like DVC (Data Version Control), Pachyderm, and LakeFS are specifically designed to handle large data files in conjunction with Git. They operate on the principle of storing metadata and pointers in Git, while the actual data resides in external storage (like S3, GCS, HDFS, or even local drives).

DVC, for instance, works by creating small .dvc metafiles that contain information about the actual data files, including their hashes and storage location. These metafiles are committed to Git.

A typical workflow might look like this:

1. Track Data: dvc add s3://my-llm-datasets/processed/v2.1_vocab32k_cc-2023-03
   • This command analyzes the data, calculates hashes, creates a .dvc file (e.g., v2.1_vocab32k_cc-2023-03.dvc), and potentially uploads the data to a configured DVC remote storage if it's not already there.
2. Commit Metadata to Git: git add v2.1_vocab32k_cc-2023-03.dvc .gitignore; git commit -m "Add dataset v2.1"
   • The small .dvc file is added to Git, linking this specific data version to the codebase version.
3. Retrieve Data: dvc pull v2.1_vocab32k_cc-2023-03.dvc (or simply dvc pull)
   • On another machine or later time, checking out the Git commit and running dvc pull downloads the corresponding data files listed in the .dvc file from the remote storage.

These tools often provide features past basic versioning, such as data pipelines and experiment tracking integration.

Overview of how DVC separates Git tracking from large file storage.

3. Cloud Storage Versioning Features

Many cloud storage services (like Amazon S3 or Google Cloud Storage) offer built-in object versioning. Enabling this feature automatically keeps previous versions of objects when they are overwritten or deleted. While simple to enable, this approach often lacks the explicit connection to code versions and preprocessing steps provided by manifest files or dedicated tools like DVC. It primarily serves as a backup and recovery mechanism rather than a full-fledged dataset versioning system for complex ML workflows. It can be harder to identify exactly which set of object versions corresponds to a specific training run without additional tracking mechanisms.

Linking Datasets, Code, and Experiments

True reproducibility requires linking the versioned dataset with the specific code commit used for training and the resulting model artifacts and metrics. Experiment tracking platforms (like MLflow, Weights & Biases, Comet ML) are invaluable here. When logging an experiment, you should include:

• The Git commit hash of the training code.
• The identifier for the dataset version used (e.g., the path to the manifest file, the DVC tag, or the storage prefix).
• Hyperparameters used for the run.
• Output model checkpoint paths.
• Evaluation metrics.

This creates a complete, auditable record connecting the inputs (code, data, config) to the outputs (model, metrics).

In summary, managing massive datasets for LLM training requires more than just storage. Implementing clear dataset versioning practices, whether through disciplined naming and manifests or dedicated tools, is fundamental for reproducibility, debugging, collaboration, and building reliable large language models. It ensures that your significant investment in data preparation and training compute leads to understandable and repeatable outcomes.