def serve(model_path, run_id, port): """ Serve a PythonFunction model saved with MLflow. If a run_id is specified, MODEL_PATH is treated as an artifact path within that run; otherwise it is treated as a local path. """ if run_id: model_path = _get_model_log_dir(model_path, run_id) app = scoring_server.init(load_pyfunc(model_path)) app.run(port=port)
def serve(model_uri, port, host, no_conda): """ Serve a pyfunc model saved with MLflow by launching a webserver on the specified host and port. For information about the input data formats accepted by the webserver, see the following documentation: https://www.mlflow.org/docs/latest/models.html#pyfunc-deployment. """ local_model_path = _download_artifact_from_uri(artifact_uri=model_uri) model_env_file = _load_model_env(path=local_model_path) if not no_conda and model_env_file is not None: conda_env_path = os.path.join(local_model_path, model_env_file) return _rerun_in_conda(conda_env_path) app = scoring_server.init(load_pyfunc(local_model_path)) app.run(port=port, host=host)
def serve(model_path, run_id, port, host, no_conda): """ Serve a PythonFunction model saved with MLflow. If a ``run_id`` is specified, ``model-path`` is treated as an artifact path within that run; otherwise it is treated as a local path. """ if run_id: model_path = _get_model_log_dir(model_path, run_id) model_env_file = _load_model_env(model_path) if not no_conda and model_env_file is not None: conda_env_path = os.path.join(model_path, model_env_file) return _rerun_in_conda(conda_env_path) app = scoring_server.init(load_pyfunc(model_path)) app.run(port=port, host=host)
def serve(model_path, run_id, port, host, no_conda): """ Serve a pyfunc model saved with MLflow by launching a webserver on the specified host and port. For information about the input data formats accepted by the webserver, see the following documentation: https://www.mlflow.org/docs/latest/models.html#pyfunc-deployment. If a ``run_id`` is specified, ``model-path`` is treated as an artifact path within that run; otherwise it is treated as a local path. """ if run_id: model_path = _get_model_log_dir(model_path, run_id) model_env_file = _load_model_env(model_path) if not no_conda and model_env_file is not None: conda_env_path = os.path.join(model_path, model_env_file) return _rerun_in_conda(conda_env_path) app = scoring_server.init(load_pyfunc(model_path)) app.run(port=port, host=host)
import os from flask import request from mlflow.pyfunc import scoring_server, load_model from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics app = scoring_server.init(load_model(os.getenv('MODEL_PATH'))) metrics = GunicornInternalPrometheusMetrics(app, defaults_prefix=os.getenv('STATSD_PREFIX')) metrics.register_default( metrics.counter( 'by_path_counter', 'Request count by request paths', labels={'path': lambda: request.path} ) )
from mlflow.pyfunc import scoring_server from mlflow import pyfunc app = scoring_server.init(pyfunc.load_pyfunc("/opt/ml/model/"))
import os from mlflow.pyfunc import scoring_server from mlflow.pyfunc import load_model app = scoring_server.init( load_model(os.environ[scoring_server._SERVER_MODEL_PATH]))