Adding MLFoundry to your code

After getting the api-key and logging in as described in the setup section. Follow the following steps.

Create a run and start tracking

Initialize a new run. This initialization will start tracking system metrics automatically. This run will now appear on the mlfoundry dashboard.

import mlfoundry

client = mlfoundry.get_client()
run = client.create_run(project_name="iris-demo", run_name="svm-model")

Track parameters

Save hyperparameters for your experiment.

run.log_params({"learning_rate": 0.001})

Track metrics

Save metrics for your model.

run.log_metrics({"accuracy": 0.7, "loss": 0.6})

End a run and stop tracking

After completion of your experiment, you can end a run. This function marks the run as “finished” and stops tracking system metrics.

run.end()