OpenNMT-tf uses TensorBoard to log information during the training. Simply start
tensorboard by setting the active log directory, e.g.:
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
OpenNMT-tf training can make use of multiple GPUs with in-graph replication. In this mode, the main section of 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
onmt-main train [...] --num_gpus 4
Note that evaluation and inference will run on a single device.
via TensorFlow asynchronous training¶
OpenNMT-tf also supports asynchronous distributed training with between-graph replication. In this mode, each graph replica processes a batch independently, compute the gradients, and asynchronously update a shared set of parameters.
To enable distributed training, the user should use the
train_and_eval run type and set on the command line:
- a chief worker host that runs a training loop and manages checkpoints, summaries, etc.
- a list of worker hosts that run a training loop
- a list of parameter server hosts that synchronize the parameters
Then a training instance should be started on each host with a selected task, e.g.:
CUDA_VISIBLE_DEVICES=0 onmt-main train_and_eval [...] \ --ps_hosts localhost:2222 \ --chief_host localhost:2223 \ --worker_hosts localhost:2224,localhost:2225 \ --task_type worker \ --task_index 1
will start the worker 1 on the current machine and first GPU. By setting
CUDA_VISIBLE_DEVICES correctly, asynchronous distributed training can be run on a single multi-GPU machine.
via Horovod (experimental)¶
OpenNMT-tf has an experimental support for Horovod, enabled when the flag
--horovod is passed to the command line. For example, this command starts a training on 4 GPUs (don’t use Horovod in that case, just use
mpirun -np 4 \ -H localhost:4 \ -bind-to none -map-by slot \ -x LD_LIBRARY_PATH -x PATH \ -mca pml ob1 -mca btl ^openib \ onmt-main train --model_type Transformer --config data.yml --auto_config --horovod
Additional parameters can be set in the training configuration:
params: # (optional) Horovod parameters. horovod: # (optional) Compression type for gradients (can be: "none", "fp16", default: "none"). compression: none # (optional) Average the reduced gradients (default: false). average_gradients: false
For more information on how to install and use Horovod, please see the GitHub repository.
Note : distributed training will also split the training directory
model_dir accross the instances. This could impact features that restore checkpoints like inference, manual export, or checkpoint averaging. The recommend approach to properly support these features while running distributed training is to store the
model_dir on a shared filesystem, e.g. by using HDFS.
Mixed precision training¶
Thanks to work from NVIDIA, OpenNMT-tf supports training models using FP16 computation. Mixed precision training is automatically enabled when the data type of the inputters is defined to be
tf.float16. See for example the predefined model
TransformerFP16, which is up to 1.8x faster than the FP32 version on compatible hardware:
onmt-main train_and_eval --model_type TransformerFP16 --auto_config --config data.yml
Additional training configurations are available to tune the loss scaling algorithm:
params: # (optional) For mixed precision training, the loss scaling to apply (a constant value or # an automatic scaling algorithm: "backoff", "logmax", default: "backoff") loss_scale: backoff # (optional) For mixed precision training, the additional parameters to pass the loss scale # (see the source file opennmt/optimizers/mixed_precision_wrapper.py). loss_scale_params: scale_min: 1.0 step_factor: 2.0
Maximizing the FP16 performance¶
Some extra steps may be required to ensure good FP16 performance:
- Mixed precision training requires at least Volta GPUs and CUDA 9.1. As TensorFlow versions 1.5 to 1.12 are shipped with CUDA 9.0, you should instead:
- install TensorFlow 1.13 or higher
- use NVIDIA’s TensorFlow Docker image
- compile TensorFlow manually
- Tensor Cores require the input dimensions to be a multiple of 8. You may need to tune your vocabulary size using
onmt-build-vocabwhich will ensure that
(vocab_size + 1) % 8 == 0(+ 1 is the
token that is automatically added during the training).
For more information about the implementation and get additional expert recommendation on how to maximize performance, see the OpenSeq2Seq’s documentation.
Converting between FP32 and FP16¶
If you want to convert an existing checkpoint to FP16 from FP32 (or vice-versa), see the script
onmt-convert-checkpoint. Typically, it is useful when you want to train using FP16 but still release a model in FP32, e.g.:
onmt-convert-checkpoint --model_dir ende-fp16/ --output_dir ende-fp32/ --target_dtype float32
The checkpoint generated in
ende-fp32/ can then be used in
export run types.
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 train_and_eval --model_type NMTSmall --auto_config --config data.yml # ... the training is interrupted or stopped ... # Continue from the latest checkpoint. onmt-main train_and_eval --model_type NMTSmall --auto_config --config data.yml
Note : If the train was stopped because
train_steps 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 script
onmt-update-vocabcan 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_pathcan 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)