wandb.log to log data from runs, such as scalars, images, video, histograms, plots, and tables.wandb.log({"train-loss": 0.5, "accuracy": 0.9}). This will save the loss and accuracy to the run's history and update the summary values for these metrics.wandb.run.summary["key"] = value.wandb.log({"example": wandb.Image("myimage.jpg")}) will log an example image which will be displayed nicely in the W&B UI. See the reference documentation for all of the different supported types or check out our guides to logging for examples, from 3D molecular structures and segmentation masks to PR curves and histograms. wandb.Tables can be used to logged structured data. See our guide to logging tables for details.wandb.log({"train": {"acc": 0.9}, "val": {"acc": 0.8}}), the metrics will be organized into train and val sections in the W&B UI.wandb.log, so logging related metrics together is encouraged. If it's inconvenient to log related metrics together calling wandb.log({"train-loss": 0.5, commit=False}) and then wandb.log({"accuracy": 0.9}) is equivalent to calling wandb.log({"train-loss": 0.5, "accuracy": 0.9}).wandb.log is not intended to be called more than a few times per second. If you want to log more frequently than that it's better to aggregate the data on the client side or you may get degraded performance.rowstr, ints, floats, Tensors, dicts, or any of the wandb.data_types.commitwandb.log just updates the current metrics dict with the row argument and metrics won't be saved until wandb.log is called with commit=True.stepsyncwandb.log.wandb.Errorwandb.initValueError