Ensuring the traceability of the model training process is critical for large model audits, as it allows auditors to verify the integrity, reproducibility, and compliance of the model development lifecycle. Here’s how to achieve it, along with examples and relevant cloud services:
Track all code, scripts, hyperparameters, and configurations used during training. Tools like Git (with branches for experiments) or DVC (Data Version Control) help maintain a history of changes.
Example: Store the model training script (train.py), dataset preprocessing code, and YAML/JSON config files (defining learning rate, batch size, etc.) in a Git repository with commit hashes for each modification.
Document the origin, preprocessing steps, and versioning of training data. Use metadata to track data sources, transformations, and licenses.
Example: Log the dataset’s source (e.g., "Common Crawl 2023"), preprocessing steps (e.g., tokenization, deduplication), and version (e.g., dataset_v2.1). Cloud services like Tencent Cloud COS (Cloud Object Storage) can store datasets with versioning enabled.
Record every experiment, including model architecture, hyperparameters, training metrics, and outcomes. Tools like MLflow, Weights & Biases, or TensorBoard help log and compare experiments.
Example: Log the model’s loss/accuracy curves, optimizer settings, and training duration for each run. In Tencent Cloud TI-ONE (AI Platform), experiment tracking is built-in, allowing auditors to review historical training runs.
Save intermediate and final model checkpoints (weights, biases, optimizer states) with timestamps and metadata.
Example: Store model checkpoints (model_epoch_10.pt) along with logs in a structured directory (e.g., /experiments/exp1/checkpoints/). Tencent Cloud COS can host these artifacts with access controls.
Document the software environment (OS, libraries, hardware) using tools like Docker, Conda, or virtual environments.
Example: Provide a Dockerfile or environment.yml specifying Python 3.9, PyTorch 2.0, and CUDA 11.8. Tencent Cloud TI-ONE supports containerized training for consistent environments.
Maintain logs of who accessed or modified training data, code, or models. Use role-based access control (RBAC) to restrict unauthorized changes.
Example: Log all SSH/API access to the training cluster and dataset storage. Tencent Cloud CAM (Cloud Access Management) enables fine-grained permission control.
Validate model outputs at each training stage (e.g., loss trends, overfitting checks) and document deviations.
Example: Flag sudden spikes in loss or accuracy drops, and store validation reports. Tencent Cloud TI-ONE provides automated monitoring for training jobs.
By implementing these practices—especially with Tencent Cloud’s TI-ONE, COS, and CAM services—organizations can ensure full traceability for large model audits, meeting regulatory and compliance requirements.