To get started, first we need to install the mlfoundry library with the following command.

pip install mlfoundry

Now login to mlfoundry using the command below.

mlfoundry login --host https://app.example.yourdomain.com

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()
client.create_ml_repo('iris-demo')
run = client.create_run(ml_repo="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()

Creating an ML Repo via the UI:

1. Access the platform's dashboard or homepage and click on the "ML Repo" tab in the left panel/
2. Click on "New ML Repo"
3. Fill in Configuration Details:
   1. Name: Provide a name for the new ML repository to identify it easily.
   2. Description: Add a brief description to provide more context and information about the ML repository.
   3. Storage Integration: Choose a storage option from the dropdown list where the data in the ML repository will be stored. This options include all the storage integrated in different clusters.
4. After filling in the necessary details for the ML repository, click on the "Create" button

Congratulations! You have successfully created a new ML repository. It is now ready for use, and you can start populating it with your data, code, models, and other related resources.