Offline Training
Overview
One of the main challenges of recommendation model training is the storage and training of large-scale feature vectors. MindSpore Recommender provides a perfect solution for training large-scale feature vectors for offline scenarios.
Overall Architecture
The training architecture for large-scale feature vectors in recommendation models is shown in the figure below, in which the core adopts the technical scheme of distributed multi-level Embedding Cache. The distributed parallel technology of multi-machine and multi-card based on model parallelism implements large-scale and low-cost recommendation training of large models.
