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Log parameters and model configuration

You can define a namespace for storing any parameters or hyperparameters.

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 parameters in the All metadata section of the run.

tip

You can display parameters in the experiments table and custom dashboards.