Training with the feature store#

In this section

Creating an offline dataset#

An offline dataset is a specific instance of the feature vector definition. To create this instance, use the feature store's get_offline_features(<feature_vector>, <target>) function on the feature vector using the store://<project_name>/<feature_vector> reference and an offline target (as in Parquet, CSV, etc.).

You can add a time-based filter condition when running get_offline_feature with a given vector. You can also filter with the query argument on all the other features as you like. See get_offline_features().


import mlrun.feature_store as fstore

feature_vector = '<feature_vector_name>'
offline_fv = fstore.get_offline_features(feature_vector=feature_vector, target=ParquetTarget())

Behind the scenes, get_offline_features() runs a local or Kubernetes job (can be specific by the run_config parameter) to retrieve all the relevant data from the feature sets, merge them and return it to the specified target which can be a local parquet, AZ Blob store or any other type of available storage.

Once instantiated with a target, the feature vector holds a reference to the instantiated dataset and references it as its current offline source.

You can also use MLRun's log_dataset() to log the specific dataset to the project as a specific dataset resource.


Training your model using the feature store is a fairly simple task. (The offline dataset can also be used for your EDA.)

To retrieve a feature vector's offline dataset, use MLRun's data item mechanism, referencing the feature vector and specifying to receive it as a DataFrame.

df = mlrun.get_dataitem(f'store://feature-vectors/{project}/patient-deterioration').as_df()

When trying to retrieve the dataset in your training function, you can put the feature vector reference as an input to the function and use the as_df() function to retrieve it automatically.

# A sample MLRun training function
def my_training_function(context, # MLRun context
                         dataset, # our feature vector reference
    # retrieve the dataset
    df = dataset.as_df()

    # The rest of your training code...

And now you can create the MLRun function and run it locally or over the kubernetes cluster:

# Creating the training MLRun function with the code
fn = mlrun.code_to_function('training', 

# Creating the task to run the function with its dataset
task = mlrun.new_task('training', 
                      inputs={'dataset': f'store://feature-vectors/{project}/{feature_vector_name}'}) # The feature vector is given as an input to the function

# Running the function over the kubernetes cluster # Set local=True to run locally