valohai.metadata.logger()

When your training code is running on the Valohai platform, it is a good idea to store some metadata or metrics for the model training performance.

Everything you print() during a remote execution is collected and stored by Valohai as raw logs.

To save the explicitly tracked metrics – the execution metadata – you need to print them out as a JSON object.

The valohai-utils custom logger automatically handles the grouping of the metrics into a single JSON object.

import valohai

for i in range(3):
    with valohai.metadata.logger() as logger:
        logger.log("iteration", i)
        logger.log("accuracy", 0.001)
        logger.log("loss", 12.456)

This example would print:

{"iteration": 0, "accuracy": 0.001, "loss": 12.456}
{"iteration": 1, "accuracy": 0.001, "loss": 12.456}
{"iteration": 2, "accuracy": 0.001, "loss": 12.456}

It is important to use the with block so that the logger knows how to group the metrics of a single iteration.

The metrics of a single iteration, epoch or similar, need to be grouped into a single JSON object printout. If printed out as separated objects, the platform doesn’t understand them as a meaningful set.