wandb.log(): Log metrics over time in a training loop, such as accuracy and loss. By default, when you call
wandb.logit appends a new step to the
historyobject and updates the
history: An array of dictionary-like objects that tracks metrics over time. These time series values are shown as default line plots in the UI.
summary: By default, the final value of a metric logged with wandb.log(). You can set the summary for a metric manually to capture the highest accuracy or lowest loss instead of the final value. These values are used in the table, and plots that compare runs — for example, you could visualize at the final accuracy for all runs in your project.
wandblibrary is incredibly flexible. Here are some suggested guidelines.
diff.patchfile if there are any uncommitted changes.
requirements.txtfile will be uploaded and shown on the files tab of the run page, along with any files you save to the
wandbdirectory for the run.
wandb.watch(model)to see gradients of the weights as histograms in the UI.
wandb.logto see metrics from your model. If you log metrics like accuracy and loss from inside your training loop, you'll get live updating graphs in the UI.