wandb.log(dict) to log a dictionary of metrics, media, or custom objects to a step. Each time you log, we increment the step by default, so you can see how your models and data evolve over time.wandb.run.summary["best_accuracy"] = best_accuracywandb.log, like this: wandb.log({"acc'": 0.9, "loss": 0.1}) and they will both be available to plot against in the UIwandb.log({'acc': 0.9, 'epoch': 3, 'batch': 117}). To set the default x-axis for a given metric use Run.define_metric()​wandb.log supports the logging of a wide variety of data types, from media like images and videos to tables and charts.wandb Python library.wandb.log is tracked over time, forming the "history" of a run. By default, each call to wandb.log is a new step and all of our charts and panels use the history step as the x-axis.wandb.log({'loss': 0.1, 'epoch': 1, 'batch': 3}). In the UI you can switch between x-axes in the chart settings.wandb.log() as follows:step, W&B will collect the keys and values from each call in one unified dictionary. As soon you call wandb.log() with a different value for step than the previous one, W&B will write all the collected keys and values to the history, and start collection over again. Note that this means you should only use this with consecutive values for step: 0, 1, 2, .... This feature doesn't let you write to absolutely any history step that you'd like, only the "current" one and the "next" one.commit=False in wandb.log to accumulate metrics, just be sure to eventually call wandb.log with commit=True (the default) to persist the metrics.summary dictionary. For values that are logged with wandb.log, we automatically set summary to the last value added. You can also add metrics or media to the summary directly or overwrite the default values. If a summary metric is modified, the previous value is lost.define_metricdefine_metric to set a custom x axis or capture a custom summary of a metric.wandb.summary. For example, you might want to capture the maximum accuracy or the minimum loss value, instead of the final value.step. Sometimes, you might want to log to a previous step, or use a different x-axis."train/" to the x-axis "train/step":summary argument in define_metric which accepts the following values: "min", "max", "mean" ,"best", "last" and "none". The "best" parameter can only be used in conjunction with the optional objective argument which accepts values "minimize" and "maximize". Here's an example of capturing the lowest value of loss and the maximum value of accuracy in the summary, instead of the default summary behavior, which uses the final value from history.