Comet.ml

Comet is a meta machine learning platform designed to help AI practitioners and teams build reliable machine learning models for real-world applications by streamlining the machine learning model lifecycle. By using Comet, users can track, compare, explain and reproduce their machine learning experiments. Comet can also greatly accelerate hyperparameter search, by providing a module for the Bayesian exploration of hyperparameter space.

Using Comet on our clusters

Availability

Since it requires an internet connection, Comet has restricted availability on compute nodes, depending on the cluster:

Cluster	Comet Availability	Note
Narval	Yes ✅	module load httpproxy required
Rorqual	Yes ✅	module load httpproxy required
TamIA	Yes ✅	module load httpproxy required
Fir	Yes ✅	httpproxy not required
Nibi	Yes ✅	httpproxy not required
Trillium	No ❌	internet access is disabled on compute nodes
Vulcan	Yes ✅	httpproxy not required
Killarney	Yes ✅	httpproxy not required

Best practices

Best practices

Avoid logging metrics (e.g. loss, accuracy) at a high frequency. This can cause Comet to throttle your experiment, which can make your job duration harder to predict. As a rule of thumb, please log metrics (or request new hyperparameters) at an interval >= 1 minute.