Working with data and model artifacts#

When running a training job, you need to pass in the data used for training, and save the resulting model. Both the data and model can be considered artifacts in MLRun. In the context of an ML pipeline, the data is an input and the model is an output.

Consider the following snippet from a pipeline in the Build and run automated ML pipelines and CI/CD section of the docs:

# Ingest data

# Train a model using the auto_trainer hub function
train = mlrun.run_function(
    inputs={"dataset": ingest.outputs["dataset"]},
    params = {
        "model_class": "sklearn.ensemble.RandomForestClassifier",
        "train_test_split_size": 0.2,
        "label_columns": "label",
        "model_name": 'cancer',

### Deploy model

This snippet trains a model using the data provided into inputs and passes the model to the rest of the pipeline using the outputs.

Input data#

The inputs parameter is a dictionary of key-value mappings. In this case, the input is the dataset (which is actually an output from a previous step). Within the training job, you can access the dataset input as an MLRun Data items (essentially a smart data pointer that provides convenience methods).

For example, this Python training function is expecting a parameter called dataset that is of type DataItem. Within the function, you can get the training set as a Pandas dataframe via the following:

import mlrun

def train(context: mlrun.MLClientCtx, dataset: mlrun.DataItem, ...):
    df = dataset.as_df()

Notice how this maps to the parameter datasets that you passed into your inputs.

Output model#

The outputs parameter is a list of artifacts that were logged during the job. In this case, it is your newly trained model, however it could also be a dataset or plot. These artifacts are logged using the experiment tracking hooks via the MLRun execution context.

One way to log models is via MLRun auto-logging with apply_mlrun. This saves the model, test sets, visualizations, and more as outputs. Additionally, you can use manual hooks to save datasets and models. For example, this Python training function uses both auto logging and manual logging:

import mlrun
from mlrun.frameworks.sklearn import apply_mlrun
from sklearn import ensemble
import cloudpickle

def train(context: mlrun.MLClientCtx, dataset: mlrun.DataItem, ...):
    # Prep data using df
    df = dataset.as_df()
    X_train, X_test, y_train, y_test = ...
    # Apply auto logging
    model = ensemble.GradientBoostingClassifier(...)
    apply_mlrun(model=model, model_name=model_name, x_test=X_test, y_test=y_test)

    # Train, y_train)
    # Manual logging
    context.log_dataset(key="X_test_dataset", df=X_test)
    context.log_model(key="my_model", body=cloudpickle.dumps(model), model_file="model.pkl")

Once your artifact is logged, it can be accessed throughout the rest of the pipeline. For example, for the pipeline snippet from the Build and run automated ML pipelines and CI/CD section of the docs, you can access your model like the following:

# Train a model using the auto_trainer hub function
train = mlrun.run_function(
    inputs={"dataset": ingest.outputs["dataset"]},

# Get trained model
model = train.outputs["model"]

Notice how this maps to the parameter model that you passed into your outputs.

Model artifacts#

By storing multiple models, you can experiment with them,
and compare their performance, without having to worry about losing the previous results.

The simplest way to store a model named my_model is with the following code:

from pickle import dumps
model_data = dumps(model)
context.log_model(key='my_model', body=model_data, model_file='my_model.pkl')

You can also store any related metrics by providing a dictionary in the metrics parameter, such as metrics={'accuracy': 0.9}. Furthermore, any additional data that you would like to store along with the model can be specified in the extra_data parameter. For example extra_data={'confusion': confusion.target_path}

A convenient utility method, eval_model_v2, which calculates mode metrics is available in mlrun.utils.

See example below for a simple model trained using scikit-learn (normally, you would send the data as input to the function). The last two lines evaluate the model and log the model.

from sklearn import linear_model
from sklearn import datasets
from sklearn.model_selection import train_test_split
from pickle import dumps

from mlrun.execution import MLClientCtx
from mlrun.mlutils import eval_model_v2

def train_iris(context: MLClientCtx):

    # Basic scikit-learn iris SVM model
    X, y = datasets.load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42)
    model = linear_model.LogisticRegression(max_iter=10000), y_train)
    # Evaluate model results and get the evaluation metrics
    eval_metrics = eval_model_v2(context, X_test, y_test, model)
    # Log model
                      labels={"class": "sklearn.linear_model.LogisticRegression"})

Save the code above to The following code loads the function and runs it as a job. See the Quick start tutorial to learn how to create the project and set the artifact path.

from mlrun import code_to_function

gen_func = code_to_function(name='train_iris',

train_iris_func = project.set_function(gen_func).apply(auto_mount())

train_iris ='train_iris',

You can now use get_model to read the model and run it. This function gets the model file, metadata, and extra data. The input can be either the path of the model, or the directory where the model resides. If you provide a directory, the function searches for the model file (by default it searches for .pkl files)

The following example gets the model from models_path and test data in test_set with the expected label provided as a column of the test data. The name of the column containing the expected label is provided in label_column. The example then retrieves the models, runs the model with the test data and updates the model with the metrics and results of the test data.

from pickle import load

from mlrun.execution import MLClientCtx
from mlrun.datastore import DataItem
from mlrun.artifacts import get_model, update_model
from mlrun.mlutils import eval_model_v2

def test_model(context: MLClientCtx,
               models_path: DataItem,
               test_set: DataItem,
               label_column: str):

    if models_path is None:
        models_path = context.artifact_subpath("models")
    xtest = test_set.as_df()
    ytest = xtest.pop(label_column)

    model_file, model_obj, _ = get_model(models_path)
    model = load(open(model_file, 'rb'))

    extra_data = eval_model_v2(context, xtest, ytest.values, model)
    update_model(model_artifact=model_obj, extra_data=extra_data, 
                 metrics=context.results, key_prefix='validation-')

To run the code, place the code above in and use the following snippet. The model from the previous step is provided as the models_path:

from mlrun.platforms import auto_mount
gen_func = code_to_function(name='test_model',

func = project.set_function(gen_func).apply(auto_mount())

run ='test_model',
                params={'label_column': 'label'},
                inputs={'models_path': train_iris.outputs['model'],
                        'test_set': ''}),

Plot artifacts#

Storing plots is useful to visualize the data and to show any information regarding the model performance. For example, you can store scatter plots, histograms and cross-correlation of the data, and for the model store the ROC curve and confusion matrix.

The following code creates a confusion matrix plot using sklearn.metrics.plot_confusion_matrix and stores the plot in the artifact repository:

from mlrun.artifacts import PlotArtifact
from mlrun.mlutils import gcf_clear

confusion_matrix = metrics.plot_confusion_matrix(model,
                                                 values_format = '.2g',
confusion_matrix = context.log_artifact(PlotArtifact('confusion-matrix', body=confusion_matrix.figure_), 

You can use the update_dataset_meta function to associate the plot with the dataset by assigning the value of the extra_data parameter:

from mlrun.artifacts import update_dataset_meta

extra_data = {'confusion_matrix': confusion_matrix}
update_dataset_meta(dataset, extra_data=extra_data)