Training

Monitoring

OpenNMT-tf uses TensorBoard to log information during the training. Simply start tensorboard by setting the active log directory, e.g.:

tensorboard --logdir="."

then open the URL displayed in the shell to monitor and visualize several data, including:

  • training and evaluation loss

  • training speed

  • learning rate

  • gradients norm

  • computation graphs

  • word embeddings

  • decoder sampling probability

Automatic evaluation

Evaluation can be run automatically when using the --with_eval flag:

onmt-main [...] train --with_eval

This minimally requires you to set some evaluation files in your data configuration, e.g.:

data:
  eval_features_file: ...
  eval_labels_file: ...

By default, it will run every 5000 training steps and report evaluation results in the console output and on TensorBoard.

Early stopping

Early stopping is useful to stop the training automatically when the model performance is not improving anymore.

For example, the following configuration stops the training when the BLEU score does not improve by more than 0.2 points in the last 4 evaluations:

eval:
  external_evaluators: bleu
  early_stopping:
    metric: bleu
    min_improvement: 0.2
    steps: 4

Replicated training

OpenNMT-tf training can make use of multiple GPUs with in-graph replication. In this mode, the graph is replicated over multiple devices and batches are processed in parallel. The resulting graph is equivalent to train with batches N times larger, where N is the number of used GPUs.

For example, if your machine has 4 GPUs, simply add the --num_gpus option:

onmt-main [...] train --num_gpus 4

Note that evaluation and inference will run on a single device.

Mixed precision training

Mixed precision can be enabled with the --mixed_precision flag:

onmt-main --model_type Transformer --auto_config --config data.yml --mixed_precision train

It uses TensorFlow’s auto_mixed_precision optimization which converts selected nodes in the graph to float16. During training, dynamic loss scaling is used.

Maximizing the FP16 performance

Some extra steps may be required to ensure good FP16 performance:

  • Mixed precision training requires a Volta GPU or above

  • Tensor Cores require the input dimensions to be a multiple of 8. You may need to tune your vocabulary size using --size_multiple 8 on onmt-build-vocab which will ensure that (vocab_size + 1) % 8 == 0 (+ 1 is the <unk> token that is automatically added during the training).

Retraining

Continuing from a stopped training

This is the most common case of retrainings: the training was interrupted but should run longer. In that case, simply launch the same command that you used for the initial training, e.g.:

# Start the training.
onmt-main --model_type NMTSmall --auto_config --config data.yml train

# ... the training is interrupted or stopped ...

# Continue from the latest checkpoint.
onmt-main --model_type NMTSmall --auto_config --config data.yml train

Note: If the train was stopped because max_step was reached, you should first increase this value before continuing.

Fine-tune an existing model

Retraining can also be useful to fine-tune an existing model. For example in machine translation, it is faster to adapt a generic model to a specific domain compared to starting a training from scratch.

OpenNMT-tf offers some features to make this process easier:

  • The run type update_vocab can be used to change the word vocabularies contained in a checkpoint while keeping learned weights of shared words (e.g. to add a domain terminology)

  • The command line argument --checkpoint_path can be used to load the weights of an existing checkpoint while starting from a fresh training state (i.e. with new learning rate schedule and optimizer variables)