Intercept Flair logging messages, parse them, and log to MLflow
Flair is an amazing NLP library. MLflow is an incredibly useful tool to keep track of machine learning parameters, metrics, and artifacts. However, Flair doesn't currently support MLflow, nor does it support a callback mechanism to get updates about training metrics.
The flairflow library is an attempt to work around these limitations
and enable MLflow logging in Flair.
It accomplishes this by adding a log handler to the logger used by Flair, intercepting and parsing these log messages, and forwarding them to MLflow. It's by no means elegant, but it works.
import flair, mlflow
from flairflow import FlairLogMLFLow
# set up Flair's trainer object...
# flairflow will log to mlflow for all flair messages
# in the context of this `with` statement
with mlflow.start_run(), FlairLogMLFLow():
result = trainer.train(base_path=path,
learning_rate=0.1,
mini_batch_size=32,
max_epochs=150)