How does DeepSeek handle model versioning?

DeepSeek handles model versioning through a systematic approach designed to maintain clarity, reproducibility, and consistency across updates. The system uses semantic versioning (e.g., MAJOR.MINOR.PATCH) to categorize changes based on their impact. Major versions indicate breaking changes, such as architectural overhauls, while minor versions represent feature additions like new training data or optimizations. Patch versions address bug fixes or minor adjustments. Each version is immutable once published, ensuring that experiments or deployments relying on a specific version remain stable. For example, a model labeled v2.1.0 might introduce a new preprocessing step (minor update), while v3.0.0 could switch from PyTorch to TensorFlow (major change).

The versioning process integrates with CI/CD pipelines to automate testing and validation. When a model update is proposed, automated tests check performance metrics, compatibility with existing APIs, and adherence to predefined thresholds (e.g., accuracy or latency). If tests pass, the model is assigned a new version and stored in a registry with metadata detailing training data, hyperparameters, and dependencies. For instance, a model trained on a dataset updated in 2024 might include a dataset hash in its metadata to ensure traceability. This automation reduces human error and ensures only validated models are deployed.

DeepSeek also supports rollback capabilities and environment reproducibility. Developers can revert to earlier versions if a new model underperforms, using version tags to retrieve specific iterations. To maintain consistency, each version is packaged with environment specifications, such as Docker containers or dependency lists, ensuring the model runs correctly across different setups. For example, a model trained with CUDA 11.6 would include a requirements.txt file specifying exact library versions. API endpoints often include version identifiers (e.g., /predict/v1.2.3), allowing clients to lock to specific versions without disruption. This combination of clear version labels, automated checks, and environment management ensures reliable updates and troubleshooting.