def _save_model_with_loader_module_and_data_path(path,
loader_module,
data_path=None,
code_paths=None,
conda_env=None,
mlflow_model=Model()):
"""
Export model as a generic Python function model.
:param path: The path to which to save the Python model.
:param loader_module: The name of the Python module that is used to load the model
from ``data_path``. This module must define a method with the prototype
``_load_pyfunc(data_path)``.
:param data_path: Path to a file or directory containing model data.
:param code_paths: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files are *prepended* to the system
path before the model is loaded.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this decsribes the environment
this model should be run in.
:return: Model configuration containing model info.
"""
if os.path.exists(path):
raise MlflowException(message="Path '{}' already exists".format(path),
error_code=RESOURCE_ALREADY_EXISTS)
os.makedirs(path)
code = None
data = None
if data_path is not None:
model_file = _copy_file_or_tree(src=data_path,
dst=path,
dst_dir="data")
data = model_file
if code_paths is not None:
for code_path in code_paths:
_copy_file_or_tree(src=code_path, dst=path, dst_dir="code")
code = "code"
conda_env_subpath = "mlflow_env.yml"
if conda_env is None:
conda_env = get_default_conda_env()
elif not isinstance(conda_env, dict):
with open(conda_env, "r") as f:
conda_env = yaml.safe_load(f)
with open(os.path.join(path, conda_env_subpath), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
mlflow.pyfunc.add_to_model(mlflow_model,
loader_module=loader_module,
code=code,
data=data,
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, 'MLmodel'))
return mlflow_model
def save_model(path, loader_module=None, data_path=None, conda_env=None, mlflow_model=None, **kwargs):
"""Save the explanation locally using the MLflow model format.
This function is necessary for log_explanation to work properly.
:param path: The destination path for the saved explanation.
:type path: str
:param loader_module: The package that will be used to reload a serialized explanation. In this case,
always interpret_community.mlflow.
:type loader_module: str
:param data_path: The path to the serialized explanation files.
:type data_path: str
:param conda_env: The path to a YAML file with basic Python environment information.
:type conda_env: str
:param mlflow_model: In our case, always None.
:type mlflow_model: None
:return: The MLflow model representation of the explanation.
:rtype: mlflow.models.Model
"""
try:
import mlflow
from mlflow.models import Model
from mlflow.utils.file_utils import _copy_file_or_tree
from mlflow.pyfunc.model import get_default_conda_env
except ImportError as e:
raise Exception("Could not log_explanation to mlflow. Missing mlflow dependency, "
"pip install mlflow to resolve the error: {}.".format(e))
if os.path.exists(path):
raise Exception(
message="Path '{}' already exists".format(path))
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
data = None
if data_path is not None:
model_file = _copy_file_or_tree(src=data_path, dst=path, dst_dir="data")
data = model_file
conda_env_subpath = "mlflow_env.yml"
if conda_env is None:
conda_env = get_default_conda_env()
elif not isinstance(conda_env, dict):
with open(conda_env, "r") as f:
conda_env = yaml.safe_load(f)
with open(os.path.join(path, conda_env_subpath), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
mlflow.pyfunc.add_to_model(
mlflow_model, loader_module=loader_module, data=data, env=conda_env_subpath, **kwargs)
mlflow_model.save(os.path.join(path, 'MLmodel'))
return mlflow_model