Edit run configs
To overwrite single values logged with the log_configs() method, assign a new value to the same attribute path.
For example, to edit a run's learning rate:
from neptune_scale import Run
# Create a new run
run = Run(run_id="seagull-s68kj78")
# Log the learning rate
run.log_configs({"parameters/learning_rate": 0.001})
...
# Update the value
run.log_configs({"parameters/learning_rate": 0.002})
run.close()
If the run is no longer active in your Python session, resume it first:
run = Run(
run_id="seagull-s68kj78", # a run with this ID already exists
resume=True,
)
# Attribute "parameters/learning_rate": 0.001" was logged previously
run.log_configs({"parameters/learning_rate": 0.002})
run.close()
Updating an experiment with new configs
Instead of modifying individual runs, you can map runs to a single experiment and create a new run each time a configuration changes. To learn more, see Fork an experiment.