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 predict(model_uri, input_path, output_path, no_conda):
"""
Load a pandas DataFrame and runs a python_function model saved with MLflow against it.
Return the prediction results as a CSV-formatted pandas DataFrame.
"""
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)
model = load_pyfunc(local_model_path)
df = pandas.read_csv(input_path)
result = model.predict(df)
out_stream = sys.stdout
if output_path:
out_stream = open(output_path, 'w')
pandas.DataFrame(data=result).to_csv(out_stream, header=False, index=False)
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)
def predict(model_path, run_id, input_path, output_path, no_conda):
"""
Load a pandas DataFrame and runs a python_function model saved with MLflow against it.
Return the prediction results as a CSV-formatted pandas DataFrame.
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)
model = load_pyfunc(model_path)
df = pandas.read_csv(input_path)
result = model.predict(df)
out_stream = sys.stdout
if output_path:
out_stream = open(output_path, 'w')
pandas.DataFrame(data=result).to_csv(out_stream, header=False, index=False)