TensorBoard integration for Marian NMT.
marian-tensorboard generates charts for TensorBoard or Azure ML Metrics from
Marian's training logs.
It started as a project at MTMA 2022 and conceptually at MTM 2019.
Using PyPI:
pip install marian-tensorboard
With optional extras for MLflow or Azure ML support:
pip install marian-tensorboard[mlflow] # requires Python >= 3.10
pip install marian-tensorboard[azureml]
Locally:
git clone https://github.com/marian-nmt/marian-tensorboard
cd marian-tensorboard
virtualenv -p python3 venv
source ./venv/bin/activate
python3 setup.py install
Both will add new marian-tensorboard command.
marian-tensorboard -f examples/train.encs.*.log
Open a web browser at https://localhost:6006. The script will update the
TensorBoard charts every --update-freq seconds unless --offline is used.
marian-tensorboard -f path/to/train.log [-t tb azureml]
Then on Azure Machine Learning VM go to the Metrics tab or start a TensorBoard server under the Endpoints tab.
Note that logging into Azure ML Metrics is automatically enabled if Azure ML
Run ID is detected. Specify -t azureml to disable TensorBoard logging.
If Azure ML is enabled, the script will not start an own TensorBoard server
instance.
- Amr Hendy
- Kevin Duh
- Roman Grundkiewicz
- Marcin Junczys-Dowmunt
See CHANGELOG.md.
See LICENSE.md.