Quickstart

This page presents a minimal workflow to get you started in using OpenNMT-tf.

Step 0: Install OpenNMT-tf

We recommend using virtualenv to setup and configure the environment for this quickstart:

virtualenv pyenv
source pyenv/bin/activate
pip install tensorflow-gpu
pip install OpenNMT-tf

Step 1: Prepare the data

To get started, we propose to download a toy English-German dataset for machine translation containing 10k tokenized sentences:

wget https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz
tar xf toy-ende.tar.gz
cd toy-ende

The first step is to build the source and target word vocabularies from the training files:

onmt-build-vocab --size 50000 --save_vocab src-vocab.txt src-train.txt
onmt-build-vocab --size 50000 --save_vocab tgt-vocab.txt tgt-train.txt

Then, the data files should be declared in a YAML configuration file, let’s name it data.yml:

model_dir: run/

data:
  train_features_file: src-train.txt
  train_labels_file: tgt-train.txt
  eval_features_file: src-val.txt
  eval_labels_file: tgt-val.txt
  source_words_vocabulary: src-vocab.txt
  target_words_vocabulary: tgt-vocab.txt

Step 2: Train the model

onmt-main train_and_eval --model_type NMTSmall --auto_config --config data.yml

This command will start the training and evaluation loop of a small RNN-based sequence to sequence model. The --auto_config flag selects the best settings for this type of model.

The training will regularly produce checkpoints in the run/ directory. To monitor the training progress, some logs are displayed in the console. However, to visually monitor the training we suggest using TensorBoard:

tensorboard --logdir="run"

Step 3: Translate

onmt-main infer --auto_config --config data.yml --features_file src-test.txt

This command can be executed as soon as a checkpoint is saved by the training; the most recent checkpoint will be used by default. The predictions will be printed on the standard output.

That’s it! You successfully executed the 3 main steps to prepare, run, and evaluate an OpenNMT-tf training.

Going further

While this example gave you a quick overview of a typical OpenNMT-tf workflow, it will not produce state of the art results. The selected dataset and model are too small for this task.

To go further, here are some pointers: