product

Polyaxon v1.6: Manual ML runs managements

Today we introduce a new major enhacement to allow user to manually manage their experiments.

Improved manual management of runs

Most of the experiment tracking that users perform are in-cluster and only concern the run that’s being executed by the pod running on Kubernetes. However, several users are also launching local experiments within a notebook sessions both in-cluster or out of the cluster, and they generally like to track those experiments as well.

The tracking Run and module init have now a new flag called is_new that initializes a new run and creates a new instance, for example, if a user is running a notebook in-cluster, they can instrument several runs within that notebook:

from polyaxon import tracking

# First run
tracking.init(..., is_new=True)
tracking.log_metrics(metric1=0.1, metric2=3.0, ...)
tracking.log_artifact(...)
# Stopping the current run
tracking.end()


# Second run
tracking.init(..., is_new=True)
tracking.log_metrics(metric1=0.2, metric2=4.2, ...)
tracking.log_artifact(...)
# Stopping the current run
tracking.end()

Learn More about Polyaxon

This blog post just goes over a couple of features that we shipped since our last product update, there are several other features and fixes that are worth checking. To learn more about all the features, fixes, and enhancements, please visit the release notes.

Polyaxon continues to grow quickly and keeps improving and providing the simplest machine learning abstraction. We hope that these updates will improve your workflows and increase your productivity, and again, thank you for your continued feedback and support.

Subscribe to Polyaxon Blog

Get the latest posts delivered right to your inbox