How to archive and manage historical data in large model training?

Archiving and managing historical data in large model training is crucial for reproducibility, auditing, and optimizing future training processes. Historical data includes datasets, model checkpoints, hyperparameters, training logs, and evaluation metrics. Here’s how to handle it effectively:

1. Structured Storage

• Organize Data by Version: Use a version control system (e.g., DVC) or a structured directory hierarchy to store datasets, code, and configurations. For example:

  /project
    /data_v1.0
    /checkpoints_v1.0
    /configs/v1.0.json
  

• Metadata Tagging: Attach metadata (e.g., timestamp, dataset version, model architecture) to each archive for easy retrieval.

2. Data Archiving

• Compression: Compress large datasets (e.g., using tar.gz or zip) to save storage space.
• Cloud Storage: Store archived data in scalable cloud object storage (e.g., Tencent Cloud COS) with lifecycle policies to transition infrequently accessed data to cheaper tiers.
• Incremental Backups: Only archive changes (e.g., new checkpoints) to avoid redundancy.

3. Metadata Management

• Track Experiments: Use tools like MLflow, Weights & Biases, or TensorBoard to log hyperparameters, metrics, and artifacts. These tools provide dashboards to compare historical runs.
• Database for Indexing: Maintain a database (e.g., PostgreSQL) to index archives with searchable fields (e.g., dataset size, accuracy).

4. Data Integrity

• Checksums: Generate checksums (e.g., SHA-256) for archives to verify integrity during retrieval.
• Access Control: Restrict permissions to sensitive historical data using role-based access (e.g., Tencent Cloud CAM).

5. Retrieval and Reuse

• Versioned Retrieval: Retrieve specific versions of data/checkpoints when debugging or reproducing results. For example, "Use checkpoint_v1.2 for the ablation study."
• Automated Pipelines: Integrate archiving into CI/CD pipelines to automate data management (e.g., trigger archiving after training completes).

Example Workflow:

1. Training: A large language model is trained on dataset_v2.1, producing checkpoints and logs.
2. Archiving: The dataset, checkpoints, and configs are compressed and uploaded to Tencent Cloud COS with metadata (e.g., "model": "GPT-3", "accuracy": 0.92).
3. Tracking: MLflow logs hyperparameters and metrics, linking them to the COS archive.
4. Retrieval: Later, the team retrieves checkpoint_v2.1 from COS to fine-tune the model on new data.

Tencent Cloud Services:

• Tencent Cloud COS: Scalable object storage for archiving datasets and checkpoints.
• Tencent Cloud CLS (Cloud Log Service): Centralized logging for training metrics and system events.
• Tencent Cloud Database (e.g., TDSQL): Store structured metadata for efficient querying.

By implementing these practices, you ensure historical data is organized, secure, and accessible for future model iterations.