Experiment tracking with mlflow
perceptrain allows to track runs and log hyperparameters, models and plots with tensorboard and mlflow. In the following, we demonstrate the integration with mlflow.
mlflow configuration
We have control over our tracking configuration by setting environment variables. First, let's look at the tracking URI. For the purpose of this demo we will be working with a local database, in a similar fashion as described here,
perceptrain can also read the following two environment variables to define the mlflow experiment name and run name
If no tracking URI is provided, mlflow stores run information and artifacts in the local ./mlflow directory and if no names are defined, the experiment and run will be named with random UUIDs.