Log parameters and model configuration
You can define a namespace for storing parameters, hyperparameters, configs, or other single values.
In the below examples, the parameters are stored in a namespace called parameters. Inside that namespace, an attribute is created for each parameter.
from neptune_scale import Run
run = Run(...)
run.log_configs(
{
"parameters/learning_rate": 0.001,
"parameters/batch_size": 64,
},
)
You can find your logged configs in the All metadata section of the run.
tip
You can compare configs, scores, and other metadata in the experiments table, side-by-side tab, and custom dashboards.