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.
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.
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