A run is used to represent a single invocation of a job, a script or a ML experiment. You can create a run at the beginning of your script or notebook, log parameters, metrics, artifacts, models, tags and finally end the run. This provides an easy to keep track of all data related to job runs or ML experiments. A quick code snippet to create a run and end it:
from truefoundry.ml import get_client

client = get_client()
run = client.create_run(ml_repo="iris-demo", run_name="svm-model")
# Your code here.
run.end()
You can organize multiple runs under a single ml_repo. For example, the run svm-model will be created under the ml_repo iris-demo. You can view these runs in the TrueFoundry dashboard.

TrueFoundry Dashboard

Accessing Runs in TrueFoundry

To interact with runs in TrueFoundry, you can use the provided methods in the TrueFoundryClient class. Here are the different possibilities to access runs:

FAQs