#Merlin project m repo how to#

FeatureStore ( feast_repo_path ) # Define the fields expected in requests request_schema = Schema () # Fetch user features, use them to a compute user vector with retrieval model, # and find candidate items closest to the user vector with nearest neighbor search user_features = request_schema. load_model ( ranking_model_path ) feature_store = feast.

load_model ( retrieval_model_path ) ranking_model = tf. Merlin Systems can also build more complex serving pipelines that integrate multiple models and external tools (like feature stores and nearest neighbor search): # Load artifacts for the pipeline retrieval_model = tf. Building a Four-Stage Recommender Pipeline

#Merlin project m repo how to#

How to train and evaluate a ranking model with Merlin Models and then how to serve it as an ensemble on Triton Inference Server.įor training models with XGBoost and Implicit, and then serving with Systems, you can visit these examples. Refer to the Merlin Example Notebooks for exploring notebooks that demonstrate tritonserver -model-repository =/export_path/ export ( export_path )Īfter you export your ensemble, you reference the directory to run an instance of Triton Inference Server to host your ensemble. column_names > TransformWorkflow ( workflow ) > PredictTensorflow ( model ) ) # Export artifacts to disk ensemble = Ensemble ( pipeline, workflow. remove_inputs () # Define ensemble pipeline pipeline = ( workflow.

load_model ( tf_model_path ) # Remove target/label columns from feature processing workflowk workflow = workflow. load ( nvtabular_workflow_path ) model = tf. To combine a feature engineering workflow and a Tensorflow model into an inference pipeline: import tensorflow as tf from import Ensemble from import PredictTensorflow, TransformWorkflow from nvtabular.workflow import Workflow # Load saved NVTabular workflow and TensorFlow model workflow = Workflow. Merlin Systems uses the Merlin Operator DAG API, the same API used in NVTabular for feature engineering, to create serving ensembles. Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems like feature stores, nearest neighbor search, and exploration strategies into end-to-end recommendation pipelines that can be served with Triton Inference Server.