6.3 Saving, loading and predicting

Key points

1. Serialization turns a model into bytes that can be stored and loaded later. All single-GPU cuML estimators support the standard Python methods.

   What NVIDIA says (2)

   “Single GPU Model Serialization # All single-GPU cuML estimators support serialization using standard Python libraries.”

   — cuML: Pickling cuML models for persistence

   “This notebook demonstrates how to save and load cuML models using various serialization methods, including pickle, joblib, and cross-platform deployment strategies.”

   — cuML: Pickling cuML models for persistence

2. Loading a pickle can execute code embedded in the file. Treat model files like programs.

   What NVIDIA says (2)

   “Security Warning # Only unpickle or deserialize models from trusted sources.”

   — cuML: Pickling cuML models for persistence

   “Malicious pickle data can execute arbitrary code during deserialization, potentially compromising your entire system.”

   — cuML: Pickling cuML models for persistence

3. ONNX is a portable model format. Exporting to it removes the cuML dependency at inference time.

   What NVIDIA says (2)

   “Use as_sklearn() to convert the cuML model to a scikit-learn estimator, then pass it to skl2onnx.convert_sklearn() .”

   — cuML: Pickling cuML models for persistence

   “The resulting .onnx file can be loaded with ONNX Runtime for inference on both CPU and GPU, with no cuML dependency at inference time.”

   — cuML: Pickling cuML models for persistence

Key terms

• Model serialization: Saving a trained model to a file so it can be loaded later, for example with pickle.
• ONNX: A portable model file format that runs without the original training library.

Sample question

How can you save a trained single-GPU cuML model and load it later to make predictions?