Exporting a SavedModel

OpenNMT-tf can export SavedModel packages 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 can be manually exported using the export run type:

onmt-main --config my_config.yml --auto_config export --output_dir ~/my-models/ende

Automatic evaluation during the training can also export models, see Training to learn more.

Running a SavedModel

Once a SavedModel is exported, OpenNMT-tf is no longer needed to run it. However, you will 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 ~/my-models/ende \
    --tag_set serve --signature_def serving_default

Some examples using exported models are available in the examples/serving directory.

Input preprocessing and tokenization

TensorFlow Serving only runs TensorFlow operations. Preprocessing functions such as the tokenization is sometimes not implemented in terms of TensorFlow ops (see Tokenization for more details). In this case, these functions should be run outside of the TensorFlow runtime, either by the client or a proxy server.


CTranslate2 is an optimized inference engine for OpenNMT models that is typically faster, lighter, and more customizable than the TensorFlow runtime.

Selected models can be exported to the CTranslate2 format directly from OpenNMT-tf by selecting the ctranslate2 export format.

When using the export command line, the --format option should be set:

onmt-main [...] export --output_dir ~/my-models/ende --format ctranslate2

When using the automatic model evaluation and export during the training, the export_format option should be configured in the eval block of the YAML configuration:

  scorers: bleu
  export_on_best: bleu
  export_format: ctranslate2

Model quantization can also be enabled by replacing ctranslate2 by one of the following export types:

  • ctranslate2_int8

  • ctranslate2_int16

  • ctranslate2_float16

TensorFlow Lite

TensorFlow Lite is a deep learning framework for fast inference of TensorFlow models on mobile devices.

Converting to TensorFlow Lite requires TensorFlow version 2.5+

Example export command:

onmt-main [...] export --export_dir ~/output --export_format tflite 

Exporting will create an opennmt.tflite model file in the export directory.

Compatible models

  • RNN models

  • Transformer

  • Transformer Relative

  • Transformer Shared Embeddings

Quantization is a way to decrease the model size, and the inference time.

Quantization Export Formats

  • Dynamic range quantization - tflite_dynamic_range

  • Float16 quantization - tflite_float16

Running a TFLite Model

Running requires using the same vocabulary files used for training.

  1. Convert the sentence to IDs with the vocabulary file.

  2. Run the model with the IDs to get a fixed size array. (TensorFlow Guide)

  3. Convert the resulting IDs to a sentence using the other vocabulary file.

Model Output

The model outputs a fixed size array, this can be specified in the data configuration file.

  tflite_output_size: 250