I’m running active learning experiments where the result is some model performance metric against dataset size. I’ve seen the below code example:
data = [[x, y] for (x, y) in zip(x_values, y_values)]
table = wandb.Table(data=data, columns = ["x", "y"])
wandb.log({"my_custom_plot_id" : wandb.plot.line(table,
"x", "y", title="Custom Y vs X Line Plot")})
but this seems to be for static graph generation once you have access to all the data.
How do I generate a live updating graph of model performance against dataset size?
Just to make sure I understand you correctly, are you trying to log a line of values at each timestep and want to see the progression of this line over time?