Quick Start
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:
- Access the platform's dashboard or homepage and click on the "ML Repo" tab in the left panel/
- Click on "New ML Repo"
- Fill in Configuration Details:
- Name: Provide a name for the new ML repository to identify it easily.
- Description: Add a brief description to provide more context and information about the ML repository.
- 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.
- 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.
Updated about 2 months ago