This will automatically log all the training and evaluation metrics to the W&B dashboard along with models at every model saving step and evaluation step are with the appropriate tags and metadata.
These config variables from the yaml file are used to instantiate the `WandbLogger` object with the project name, entity name (the logged in user by default), directory to store metadata (`./wandb` by default) and more. During the training process, the `log_metrics` function is called to log training and evaluation metrics at the training and evaluation steps respectively from the rank 0 process only.
At every model saving step, the WandbLogger, logs the model using the `log_model` function along with relavant metadata and tags showing the epoch in which the model is saved, the model is best or not and so on.
All the logging mentioned above is integrated into the `program.train` function and will generate dashboards like this -
