OpenNMT-tf periodically exports models for inference in other environments, for example with TensorFlow Serving. A model export contains all information required for inference: the graph definition, the weights, and external assets such as vocabulary files. It typically looks like this on disk:

├── assets
│   ├── src-vocab.txt
│   └── tgt-vocab.txt
├── saved_model.pb
└── variables
    └── variables.index

Models are automatically exported during the training or manually with the export run type.

When using an exported model, you need to know the input and output nodes of your model. You can use the saved_model_cli script provided by TensorFlow for inspection, e.g.:

saved_model_cli show --dir toy-ende/export/latest/1507109306/ --tag_set serve --signature_def serving_default

Some examples using exported models are available in the examples/ directory:

  • examples/serving to serve a model with TensorFlow Serving
  • examples/cpp to run inference with the TensorFlow C++ API

Note: because the Python function used in tf.py_func is not serialized in the graph, model exports do not support in-graph tokenization and text inputs are expected to be tokenized.