Logging in nnU-Net v2

Prefer the migrated reference page for the recommended path:

• Logging reference

Introduction

Logging in nnU-Net is intentionally simple and centralized in nnunetv2/training/logging/nnunet_logger.py.

The trainer talks to one object, MetaLogger, and MetaLogger fans out logs to:

• LocalLogger (always enabled): the source of truth for training curves, checkpoint logging state, and progress.png
• optional external loggers (currently WandbLogger)

This keeps training code clean while still allowing external tracking backends.

Default behaviour

Without any setup, nnU-Net uses only LocalLogger.

Per epoch, it stores:

• mean_fg_dice and ema_fg_dice (EMA is computed automatically)
• dice_per_class_or_region
• train_losses, val_losses
• lrs
• epoch_start_timestamps, epoch_end_timestamps

From these values, progress.png is updated in the fold output folder. On checkpoint save/load, the local logging state is also saved/restored, so curves continue correctly after resume.

How to enable W&B

1. Install W&B:

pip install wandb

2. Enable the backend via environment variables:

export nnUNet_wandb_enabled=1
export nnUNet_wandb_project=nnunet
export nnUNet_wandb_mode=online   # or offline

3. Run training normally:

nnUNetv2_train DATASET_NAME_OR_ID 3d_fullres 0

Notes:

• nnUNet_wandb_enabled accepts 0/1 and false/true (case-insensitive). Other values raise an error.
• When resuming (--c), W&B resume metadata in fold_x/wandb/latest-run is reused and duplicate older steps are skipped.

How to integrate a custom logger

Add a new logger class with the same minimal interface used by MetaLogger:

• update_config(self, config: dict)
• log(self, key, value, step: int)
• log_summary(self, key, value)

Example skeleton:

class MyLogger:
    def __init__(self, output_folder, resume):
        self.output_folder = output_folder
        self.resume = resume

    def update_config(self, config: dict):
        ...

    def log(self, key, value, step: int):
        ...

    def log_summary(self, key, value):
        ...

Then register it in MetaLogger.__init__ (for example behind an env var switch), similar to how WandbLogger is added.

Important integration detail:

• MetaLogger.log(...) always writes to LocalLogger first.
• If you introduce a brand-new per-epoch key, also add that key to LocalLogger.my_fantastic_logging, otherwise the local assertion will fail.