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]))
