Decorators and auto-logging

While it is possible to log results and artifacts using the MLRun execution context, it is often more convenient to use the mlrun.handler() decorator.

Basic example

Assume you have the following code in train.py

import pandas as pd
from sklearn.svm import SVC

def train_and_predict(train_data,
                      predict_input,
                      label_column='label'):

    x = train_data.drop(label_column, axis=1)
    y = train_data[label_column]

    clf = SVC()
    clf.fit(x, y)

    return list(clf.predict(predict_input))

With the mlrun.handler the python function itself would not change, and logging of the inputs and outputs would be automatic. The resultant code is as follows:

import pandas as pd
from sklearn.svm import SVC
import mlrun

@mlrun.handler(labels={'framework':'scikit-learn'},
               outputs=['prediction:dataset'],
               inputs={"train_data": pd.DataFrame,
                       "predict_input": pd.DataFrame})
def train_and_predict(train_data,
                      predict_input,
                      label_column='label'):

    x = train_data.drop(label_column, axis=1)
    y = train_data[label_column]

    clf = SVC()
    clf.fit(x, y)

    return list(clf.predict(predict_input))

To run the code, use the following example:

import mlrun
project = mlrun.get_or_create_project("mlrun-example", context="./", user_project=True)

trainer = project.set_function("train.py", name="train_and_predict", kind="job", image="mlrun/mlrun", handler="train_and_predict")

trainer_run = project.run_function(
    "train_and_predict", 
    inputs={"train_data": mlrun.get_sample_path('data/iris/iris_dataset.csv'),
            "predict_input": mlrun.get_sample_path('data/iris/iris_to_predict.csv')
           }
)

The outcome is a run with:

1. A label with key “framework” and value “scikit-learn”.

2. Two inputs “train_data” and “predict_input” created from Pandas DataFrame.

3. An artifact called “prediction” of type “dataset”. The contents of the dataset will be the return value (in this case the prediction result).

Labels

The decorator gives you the option to set labels for the run. The labels parameter is a dictionary with keys and values to set for the labels.

Input type parsing

The mlrun.handler decorator can also parse the input types, if they are specified. An equivalent definition is as follows:

@mlrun.handler(labels={'framework':'scikit-learn'},
               outputs=['prediction:dataset'])
def train_and_predict(train_data: pd.DataFrame,
                      predict_input: pd.DataFrame,
                      label_column='label'):

...

Note: If the inputs does not have a type input, the decorator assumes the parameter type in mlrun.datastore.DataItem. If you specify inputs=False, all the run inputs are assumed to be of type mlrun.datastore.DataItem. You also have the option to specify a dictionary where each key is the name of the input and the value is the type.

Logging return values as artifacts

If you specify the outputs parameter, the return values will be logged as the run artifacts. outputs expects a list; the length of the list must match the number of returned values.

The simplest option is to specify a list of strings. Each string contains the name of the artifact. You can also specify the artifact type by adding a colon after the artifact name followed by the type ('name:artifact_type'). The following are valid artifact types:

• dataset

• directory

• file

• object

• plot

• result

If you use only the name without the type, the following mapping is used:

Python type	Artifact type
pandas.DataFrame	Dataset
pandas.Series	Dataset
numpy.ndarray	Dataset
dict	Result
list	Result
tuple	Result
str	Result
int	Result
float	Result
bytes	Object
bytearray	Object
matplotlib.pyplot.Figure	Plot
plotly.graph_objs.Figure	Plot
bokeh.plotting.Figure	Plot

Another option is to specify a tuple in the form of (name, artifact_type) or (name, artifact_type, arguments). Refer to the mlrun.handler() for more details.