HugeCTR API Documentation
- Python Interface
- About the HugeCTR Python Interface
- High-level Training API
- Layers
- Model
- Model class
- add method
- compile method
- fit method
- summary method
- graph_to_json method
- construct_from_json method
- load_dense_weights method
- load_dense_optimizer_states method
- load_sparse_weights method
- load_sparse_optimizer_states method
- freeze_dense method
- freeze_embedding method
- unfreeze_dense method
- unfreeze_embedding method
- reset_learning_rate_scheduler method
- set_source method
- Model
- Low-level Training API
- LearningRateScheduler
- DataReader
- EmbeddingTraingCache
- Model
- get_learning_rate_scheduler method
- get_embedding_training_cache method
- get_data_reader_train method
- get_data_reader_eval method
- start_data_reading method
- set_learning_rate method
- train method
- get_current_loss method
- eval method
- get_eval_metrics method
- get_incremental_model method
- dump_incremental_model_2kafka method
- save_params_to_files method
- check_out_tensor method
- export_predictions method
- Inference API
- Data Generator API
- Data Source API
- Layer Classes and Methods
- Input Layer
- Sparse Embedding
- Embedding Types Detail
- Dense Layers
- Dense Layers Usage
- FullyConnected Layer
- FusedFullyConnected Layer
- MLP Layer
- MultiCross Layer
- FmOrder2 Layer
- WeightMultiply Layer
- ElementwiseMultiply Layer
- BatchNorm Layer
- LayerNorm Layer
- Concat Layer
- Reshape Layer
- Slice Layer
- Dropout Layer
- ELU Layer
- ReLU Layer
- Sigmoid Layer
- Interaction Layer
- Add Layer
- ReduceSum Layer
- BinaryCrossEntropyLoss
- CrossEntropyLoss
- MultiCrossEntropyLoss
- Embedding Collection
- GroupDenseLayer