def save_model(h2o_model, path, conda_env=None, mlflow_model=Model(), settings=None):
"""
Save an H2O model to a path on the local file system.
:param h2o_model: H2O model to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this decribes the environment
this model should be run in. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pip': [
'h2o==3.20.0.8'
]
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
"""
import h2o
path = os.path.abspath(path)
if os.path.exists(path):
raise Exception("Path '{}' already exists".format(path))
model_data_subpath = "model.h2o"
model_data_path = os.path.join(path, model_data_subpath)
os.makedirs(model_data_path)
# Save h2o-model
h2o_save_location = h2o.save_model(model=h2o_model, path=model_data_path, force=True)
model_file = os.path.basename(h2o_save_location)
# Save h2o-settings
if settings is None:
settings = {}
settings['full_file'] = h2o_save_location
settings['model_file'] = model_file
settings['model_dir'] = model_data_path
with open(os.path.join(model_data_path, "h2o.yaml"), 'w') as settings_file:
yaml.safe_dump(settings, stream=settings_file)
conda_env_subpath = "conda.yaml"
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)
pyfunc.add_to_model(mlflow_model, loader_module="mlflow.h2o",
data=model_data_subpath, env=conda_env_subpath)
mlflow_model.add_flavor(FLAVOR_NAME, h2o_version=h2o.__version__, data=model_data_subpath)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(
keras_model,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
custom_objects=None,
keras_module=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
**kwargs,
):
"""
Save a Keras model to a path on the local file system.
:param keras_model: Keras model to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
: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 when the model is loaded.
:param mlflow_model: MLflow model config this flavor is being added to.
:param custom_objects: A Keras ``custom_objects`` dictionary mapping names (strings) to
custom classes or functions associated with the Keras model. MLflow saves
these custom layers using CloudPickle and restores them automatically
when the model is loaded with :py:func:`mlflow.keras.load_model` and
:py:func:`mlflow.pyfunc.load_model`.
:param keras_module: Keras module to be used to save / load the model
(``keras`` or ``tf.keras``). If not provided, MLflow will
attempt to infer the Keras module based on the given model.
:param kwargs: kwargs to pass to ``keras_model.save`` method.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example can be a Pandas DataFrame where the given
example will be serialized to json using the Pandas split-oriented
format, or a numpy array where the example will be serialized to json
by converting it to a list. Bytes are base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
.. code-block:: python
:caption: Example
import mlflow
# Build, compile, and train your model
keras_model = ...
keras_model_path = ...
keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
results = keras_model.fit(
x_train, y_train, epochs=20, batch_size = 128, validation_data=(x_val, y_val))
# Save the model as an MLflow Model
mlflow.keras.save_model(keras_model, keras_model_path)
"""
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
if keras_module is None:
def _is_plain_keras(model):
try:
import keras
# NB: Network is the first parent with save method
import keras.engine.network
return isinstance(model, keras.engine.network.Network)
except ImportError:
return False
def _is_tf_keras(model):
try:
# NB: Network is not exposed in tf.keras, we check for Model instead.
import tensorflow.keras.models
return isinstance(model, tensorflow.keras.models.Model)
except ImportError:
return False
if _is_plain_keras(keras_model):
keras_module = importlib.import_module("keras")
elif _is_tf_keras(keras_model):
keras_module = importlib.import_module("tensorflow.keras")
else:
raise MlflowException(
"Unable to infer keras module from the model, please specify "
"which keras module ('keras' or 'tensorflow.keras') is to be "
"used to save and load the model.")
elif type(keras_module) == str:
keras_module = importlib.import_module(keras_module)
# check if path exists
path = os.path.abspath(path)
_validate_and_prepare_target_save_path(path)
# construct new data folder in existing path
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_path)
code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# save custom objects if there are custom objects
if custom_objects is not None:
_save_custom_objects(data_path, custom_objects)
# save keras module spec to path/data/keras_module.txt
with open(os.path.join(data_path, _KERAS_MODULE_SPEC_PATH), "w") as f:
f.write(keras_module.__name__)
# Use the SavedModel format if `save_format` is unspecified
save_format = kwargs.get("save_format", "tf")
# save keras save_format to path/data/save_format.txt
with open(os.path.join(data_path, _KERAS_SAVE_FORMAT_PATH), "w") as f:
f.write(save_format)
# save keras model
# To maintain prior behavior, when the format is HDF5, we save
# with the h5 file extension. Otherwise, model_path is a directory
# where the saved_model.pb will be stored (for SavedModel format)
file_extension = ".h5" if save_format == "h5" else ""
model_subpath = os.path.join(data_subpath, _MODEL_SAVE_PATH)
model_path = os.path.join(path, model_subpath) + file_extension
if path.startswith("/dbfs/"):
# The Databricks Filesystem uses a FUSE implementation that does not support
# random writes. It causes an error.
with tempfile.NamedTemporaryFile(suffix=".h5") as f:
keras_model.save(f.name, **kwargs)
f.flush() # force flush the data
shutil.copyfile(src=f.name, dst=model_path)
else:
keras_model.save(model_path, **kwargs)
# update flavor info to mlflow_model
mlflow_model.add_flavor(
FLAVOR_NAME,
keras_module=keras_module.__name__,
keras_version=keras_module.__version__,
save_format=save_format,
data=data_subpath,
code=code_dir_subpath,
)
# append loader_module, data and env data to mlflow_model
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.keras",
data=data_subpath,
env=_CONDA_ENV_FILE_NAME,
code=code_dir_subpath,
)
# save mlflow_model to path/MLmodel
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
include_cloudpickle = custom_objects is not None
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements(
include_cloudpickle, keras_module)
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path, FLAVOR_NAME, fallback=default_reqs)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
_PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
def save_model(
tf_saved_model_dir,
tf_meta_graph_tags,
tf_signature_def_key,
path,
mlflow_model=None,
conda_env=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
):
"""
Save a *serialized* collection of TensorFlow graphs and variables as an MLflow model
to a local path. This method operates on TensorFlow variables and graphs that have been
serialized in TensorFlow's ``SavedModel`` format. For more information about ``SavedModel``
format, see the TensorFlow documentation:
https://www.tensorflow.org/guide/saved_model#save_and_restore_models.
:param tf_saved_model_dir: Path to the directory containing serialized TensorFlow variables and
graphs in ``SavedModel`` format.
:param tf_meta_graph_tags: A list of tags identifying the model's metagraph within the
serialized ``SavedModel`` object. For more information, see the
``tags`` parameter of the
``tf.saved_model.builder.savedmodelbuilder`` method.
:param tf_signature_def_key: A string identifying the input/output signature associated with the
model. This is a key within the serialized ``savedmodel``
signature definition mapping. For more information, see the
``signature_def_map`` parameter of the
``tf.saved_model.builder.savedmodelbuilder`` method.
:param path: Local path where the MLflow model is to be saved.
:param mlflow_model: MLflow model configuration to which to add the ``tensorflow`` flavor.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model. The
following is an *example* dictionary representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'tensorflow=1.8.0'
]
}
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
"""
_logger.info(
"Validating the specified TensorFlow model by attempting to load it in a new TensorFlow"
" graph...")
_validate_saved_model(
tf_saved_model_dir=tf_saved_model_dir,
tf_meta_graph_tags=tf_meta_graph_tags,
tf_signature_def_key=tf_signature_def_key,
)
_logger.info("Validation succeeded!")
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path),
DIRECTORY_NOT_EMPTY)
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
root_relative_path = _copy_file_or_tree(src=tf_saved_model_dir,
dst=path,
dst_dir=None)
model_dir_subpath = "tfmodel"
shutil.move(os.path.join(path, root_relative_path),
os.path.join(path, model_dir_subpath))
conda_env_subpath = "conda.yaml"
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_model.add_flavor(
FLAVOR_NAME,
saved_model_dir=model_dir_subpath,
meta_graph_tags=tf_meta_graph_tags,
signature_def_key=tf_signature_def_key,
)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.tensorflow",
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def save_model(pytorch_model,
path,
conda_env=None,
mlflow_model=Model(),
code_paths=None,
pickle_module=mlflow_pytorch_pickle_module,
**kwargs):
"""
Save a PyTorch model to a path on the local file system.
:param pytorch_model: PyTorch model to be saved. Must accept a single ``torch.FloatTensor`` as
input and produce a single output tensor. Any code dependencies of the
model's class, including the class definition itself, should be
included in one of the following locations:
- The package(s) listed in the model's Conda environment, specified
by the ``conda_env`` parameter.
- One or more of the files specified by the ``code_paths`` parameter.
:param path: Local path where the model is to be saved.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this decribes the environment
this model should be run in. At minimum, it should specify the dependencies
contained in ``mlflow.pytorch.DEFAULT_CONDA_ENV``. If `None`, the default
``mlflow.pytorch.DEFAULT_CONDA_ENV`` environment will be added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pytorch=0.4.1',
'torchvision=0.2.1'
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param code_paths: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files will be *prepended* to the system
path when the model is loaded.
:param pickle_module: The module that PyTorch should use to serialize ("pickle") the specified
``pytorch_model``. This will be passed as the ``pickle_module`` parameter
to ``torch.save()``. By default, this module will also be used to
deserialize ("unpickle") the PyTorch model at load time.
:param kwargs: kwargs to pass to ``torch.save`` method.
>>> import torch
>>> import mlflow
>>> import mlflow.pytorch
>>> # create model and set values
>>> pytorch_model = Model()
>>> pytorch_model_path = ...
>>> #train our model
>>> for epoch in range(500):
>>> y_pred = model(x_data)
>>> ...
>>> #save the model
>>> with mlflow.start_run() as run:
>>> mlflow.log_param("epochs", 500)
>>> mlflow.pytorch.save_model(pytorch_model, pytorch_model_path)
"""
if not isinstance(pytorch_model, torch.nn.Module):
raise TypeError("Argument 'pytorch_model' should be a torch.nn.Module")
path = os.path.abspath(path)
if os.path.exists(path):
raise RuntimeError("Path '{}' already exists".format(path))
os.makedirs(path)
model_data_subpath = "data"
model_data_path = os.path.join(path, model_data_subpath)
os.makedirs(model_data_path)
# Persist the pickle module name as a file in the model's `data` directory. This is necessary
# because the `data` directory is the only available parameter to `_load_pyfunc`, and it
# does not contain the MLmodel configuration; therefore, it is not sufficient to place
# the module name in the MLmodel
#
# TODO: Stop persisting this information to the filesystem once we have a mechanism for
# supplying the MLmodel configuration to `mlflow.pytorch._load_pyfunc`
pickle_module_path = os.path.join(model_data_path,
_PICKLE_MODULE_INFO_FILE_NAME)
with open(pickle_module_path, "w") as f:
f.write(pickle_module.__name__)
# Save pytorch model
model_path = os.path.join(model_data_path,
_SERIALIZED_TORCH_MODEL_FILE_NAME)
torch.save(pytorch_model,
model_path,
pickle_module=pickle_module,
**kwargs)
conda_env_subpath = "conda.yaml"
if conda_env is None:
conda_env = 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)
if code_paths is not None:
code_dir_subpath = "code"
for code_path in code_paths:
_copy_file_or_tree(src=code_path,
dst=path,
dst_dir=code_dir_subpath)
else:
code_dir_subpath = None
mlflow_model.add_flavor(FLAVOR_NAME,
model_data=model_data_subpath,
pytorch_version=torch.__version__)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.pytorch",
data=model_data_subpath,
pickle_module_name=pickle_module.__name__,
code=code_dir_subpath,
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(keras_model,
path,
conda_env=None,
mlflow_model=Model(),
custom_objects=None,
keras_module=None,
**kwargs):
"""
Save a Keras model to a path on the local file system.
:param keras_model: Keras model to be saved.
:param path: Local path where the model is to be saved.
: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. At minimum, it should specify the
dependencies contained in :func:`get_default_conda_env()`. If
``None``, the default :func:`get_default_conda_env()` environment is
added to the model. The following is an *example* dictionary
representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'keras=2.2.4',
'tensorflow=1.8.0'
]
}
:param mlflow_model: MLflow model config this flavor is being added to.
:param custom_objects: A Keras ``custom_objects`` dictionary mapping names (strings) to
custom classes or functions associated with the Keras model. MLflow saves
these custom layers using CloudPickle and restores them automatically
when the model is loaded with :py:func:`mlflow.keras.load_model` and
:py:func:`mlflow.pyfunc.load_model`.
:param keras_module: Keras module to be used to save / load the model
(``keras`` or ``tf.keras``). If not provided, MLflow will
attempt to infer the Keras module based on the given model.
:param kwargs: kwargs to pass to ``keras_model.save`` method.
>>> import mlflow
>>> # Build, compile, and train your model
>>> keras_model = ...
>>> keras_model_path = ...
>>> keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
>>> results = keras_model.fit(
... x_train, y_train, epochs=20, batch_size = 128, validation_data=(x_val, y_val))
... # Save the model as an MLflow Model
>>> mlflow.keras.save_model(keras_model, keras_model_path)
"""
if keras_module is None:
def _is_plain_keras(model):
try:
# NB: Network is the first parent with save method
import keras.engine.network
return isinstance(model, keras.engine.network.Network)
except ImportError:
return False
def _is_tf_keras(model):
try:
# NB: Network is not exposed in tf.keras, we check for Model instead.
import tensorflow.keras.models
return isinstance(model, tensorflow.keras.models.Model)
except ImportError:
return False
if _is_plain_keras(keras_model):
keras_module = importlib.import_module("keras")
elif _is_tf_keras(keras_model):
keras_module = importlib.import_module("tensorflow.keras")
else:
raise MlflowException(
"Unable to infer keras module from the model, please specify "
"which keras module ('keras' or 'tensorflow.keras') is to be "
"used to save and load the model.")
elif type(keras_module) == str:
keras_module = importlib.import_module(keras_module)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_path)
if custom_objects is not None:
_save_custom_objects(data_path, custom_objects)
with open(os.path.join(data_path, _KERAS_MODULE_SPEC_PATH), "w") as f:
f.write(keras_module.__name__)
model_subpath = os.path.join(data_subpath, _MODEL_SAVE_PATH)
keras_model.save(os.path.join(path, model_subpath), **kwargs)
mlflow_model.add_flavor(FLAVOR_NAME,
keras_module=keras_module.__name__,
keras_version=keras_module.__version__,
data=data_subpath)
conda_env_subpath = "conda.yaml"
if conda_env is None:
conda_env = get_default_conda_env(include_cloudpickle=custom_objects
is not None,
keras_module=keras_module)
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)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.keras",
data=data_subpath,
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(
statsmodels_model,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
remove_data: bool = False,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a statsmodels model to a path on the local file system.
:param statsmodels_model: statsmodels model (an instance of `statsmodels.base.model.Results`_)
to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
: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 when the model is loaded.
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param remove_data: bool. If False (default), then the instance is pickled without changes.
If True, then all arrays with length nobs are set to None before
pickling. See the remove_data method.
In some cases not all arrays will be set to None.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
"""
import statsmodels
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
_validate_and_prepare_target_save_path(path)
model_data_path = os.path.join(path, STATSMODELS_DATA_SUBPATH)
code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# Save a statsmodels model
statsmodels_model.save(model_data_path, remove_data)
if _save_model_called_from_autolog and not remove_data:
saved_model_size = os.path.getsize(model_data_path)
if saved_model_size >= _model_size_threshold_for_emitting_warning:
_logger.warning(
"The fitted model is larger than "
f"{_model_size_threshold_for_emitting_warning // (1024 * 1024)} MB, "
f"saving it as artifacts is time consuming.\n"
"To reduce model size, use `mlflow.statsmodels.autolog(log_models=False)` and "
"manually log model by "
'`mlflow.statsmodels.log_model(model, remove_data=True, artifact_path="model")`'
)
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.statsmodels",
data=STATSMODELS_DATA_SUBPATH,
env=_CONDA_ENV_FILE_NAME,
code=code_dir_subpath,
)
mlflow_model.add_flavor(
FLAVOR_NAME,
statsmodels_version=statsmodels.__version__,
data=STATSMODELS_DATA_SUBPATH,
code=code_dir_subpath,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
def log_model(
spark_model,
artifact_path,
conda_env=None,
dfs_tmpdir=None,
sample_input=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
):
"""
Log a Spark MLlib model as an MLflow artifact for the current run. This uses the
MLlib persistence format and produces an MLflow Model with the Spark flavor.
Note: If no run is active, it will instantiate a run to obtain a run_id.
:param spark_model: Spark model to be saved - MLflow can only save descendants of
pyspark.ml.Model which implement MLReadable and MLWritable.
:param artifact_path: Run relative artifact path.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If `None`, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pyspark=2.3.0'
]
}
:param dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local
filesystem if running in local mode. The model is written in this
destination and then copied into the model's artifact directory. This is
necessary as Spark ML models read from and write to DFS if running on a
cluster. If this operation completes successfully, all temporary files
created on the DFS are removed. Defaults to ``/tmp/mlflow``.
:param sample_input: A sample input used to add the MLeap flavor to the model.
This must be a PySpark DataFrame that the model can evaluate. If
``sample_input`` is ``None``, the MLeap flavor is not added.
:param registered_model_name: (Experimental) If given, create a model version under
``registered_model_name``, also creating a registered model if one
with the given name does not exist.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param await_registration_for: Number of seconds to wait for the model version to finish
being created and is in ``READY`` status. By default, the function
waits for five minutes. Specify 0 or None to skip waiting.
.. code-block:: python
:caption: Example
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
training = spark.createDataFrame([
(0, "a b c d e spark", 1.0),
(1, "b d", 0.0),
(2, "spark f g h", 1.0),
(3, "hadoop mapreduce", 0.0) ], ["id", "text", "label"])
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
model = pipeline.fit(training)
mlflow.spark.log_model(model, "spark-model")
"""
from py4j.protocol import Py4JJavaError
_validate_model(spark_model)
from pyspark.ml import PipelineModel
if not isinstance(spark_model, PipelineModel):
spark_model = PipelineModel([spark_model])
run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
run_root_artifact_uri = mlflow.get_artifact_uri()
# If the artifact URI is a local filesystem path, defer to Model.log() to persist the model,
# since Spark may not be able to write directly to the driver's filesystem. For example,
# writing to `file:/uri` will write to the local filesystem from each executor, which will
# be incorrect on multi-node clusters - to avoid such issues we just use the Model.log() path
# here.
if is_local_uri(run_root_artifact_uri):
return Model.log(
artifact_path=artifact_path,
flavor=mlflow.spark,
spark_model=spark_model,
conda_env=conda_env,
dfs_tmpdir=dfs_tmpdir,
sample_input=sample_input,
registered_model_name=registered_model_name,
signature=signature,
input_example=input_example,
await_registration_for=await_registration_for,
)
model_dir = os.path.join(run_root_artifact_uri, artifact_path)
# Try to write directly to the artifact repo via Spark. If this fails, defer to Model.log()
# to persist the model
try:
spark_model.save(posixpath.join(model_dir, _SPARK_MODEL_PATH_SUB))
except Py4JJavaError:
return Model.log(
artifact_path=artifact_path,
flavor=mlflow.spark,
spark_model=spark_model,
conda_env=conda_env,
dfs_tmpdir=dfs_tmpdir,
sample_input=sample_input,
registered_model_name=registered_model_name,
signature=signature,
input_example=input_example,
await_registration_for=await_registration_for,
)
# Otherwise, override the default model log behavior and save model directly to artifact repo
mlflow_model = Model(artifact_path=artifact_path, run_id=run_id)
with TempDir() as tmp:
tmp_model_metadata_dir = tmp.path()
_save_model_metadata(
tmp_model_metadata_dir,
spark_model,
mlflow_model,
sample_input,
conda_env,
signature=signature,
input_example=input_example,
)
mlflow.tracking.fluent.log_artifacts(tmp_model_metadata_dir, artifact_path)
if registered_model_name is not None:
mlflow.register_model(
"runs:/%s/%s" % (run_id, artifact_path),
registered_model_name,
await_registration_for,
)
def save_model(
fastai_learner,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
**kwargs,
):
"""
Save a fastai Learner to a path on the local file system.
:param fastai_learner: fastai Learner to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
: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 when the model is loaded.
:param mlflow_model: MLflow model config this flavor is being added to.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
:param kwargs: kwargs to pass to ``Learner.save`` method.
.. code-block:: python
:caption: Example
import os
import mlflow.fastai
# Create a fastai Learner model
model = ...
# Start MLflow session and save model to current working directory
with mlflow.start_run():
model.fit(epochs, learning_rate)
mlflow.fastai.save_model(model, 'model')
# Load saved model for inference
model_uri = "{}/{}".format(os.getcwd(), 'model')
loaded_model = mlflow.fastai.load_model(model_uri)
results = loaded_model.predict(predict_data)
"""
import fastai
from fastai.callback.all import ParamScheduler
from pathlib import Path
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
_validate_and_prepare_target_save_path(path)
model_data_subpath = "model.fastai"
model_data_path = os.path.join(path, model_data_subpath)
model_data_path = Path(model_data_path)
code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# ParamScheduler currently is not pickable
# hence it is been removed before export and added again after export
cbs = [c for c in fastai_learner.cbs if isinstance(c, ParamScheduler)]
fastai_learner.remove_cbs(cbs)
fastai_learner.export(model_data_path, **kwargs)
fastai_learner.add_cbs(cbs)
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.fastai",
data=model_data_subpath,
code=code_dir_subpath,
env=_CONDA_ENV_FILE_NAME,
)
mlflow_model.add_flavor(
FLAVOR_NAME,
fastai_version=fastai.__version__,
data=model_data_subpath,
code=code_dir_subpath,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
_PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
def save_model(keras_model,
path,
conda_env=None,
mlflow_model=None,
custom_objects=None,
keras_module=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
**kwargs):
"""
Save a Keras model to a path on the local file system.
:param keras_model: Keras model to be saved.
:param path: Local path where the model is to be saved.
: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. At minimum, it should specify the
dependencies contained in :func:`get_default_conda_env()`. If
``None``, the default :func:`get_default_conda_env()` environment is
added to the model. The following is an *example* dictionary
representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'keras=2.2.4',
'tensorflow=1.8.0'
]
}
:param mlflow_model: MLflow model config this flavor is being added to.
:param custom_objects: A Keras ``custom_objects`` dictionary mapping names (strings) to
custom classes or functions associated with the Keras model. MLflow saves
these custom layers using CloudPickle and restores them automatically
when the model is loaded with :py:func:`mlflow.keras.load_model` and
:py:func:`mlflow.pyfunc.load_model`.
:param keras_module: Keras module to be used to save / load the model
(``keras`` or ``tf.keras``). If not provided, MLflow will
attempt to infer the Keras module based on the given model.
:param kwargs: kwargs to pass to ``keras_model.save`` method.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
.. code-block:: python
:caption: Example
import mlflow
# Build, compile, and train your model
keras_model = ...
keras_model_path = ...
keras_model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
results = keras_model.fit(
x_train, y_train, epochs=20, batch_size = 128, validation_data=(x_val, y_val))
# Save the model as an MLflow Model
mlflow.keras.save_model(keras_model, keras_model_path)
"""
if keras_module is None:
def _is_plain_keras(model):
try:
import keras
if LooseVersion(keras.__version__) < LooseVersion("2.2.0"):
import keras.engine
return isinstance(model, keras.engine.Model)
else:
# NB: Network is the first parent with save method
import keras.engine.network
return isinstance(model, keras.engine.network.Network)
except ImportError:
return False
def _is_tf_keras(model):
try:
# NB: Network is not exposed in tf.keras, we check for Model instead.
import tensorflow.keras.models
return isinstance(model, tensorflow.keras.models.Model)
except ImportError:
return False
if _is_plain_keras(keras_model):
keras_module = importlib.import_module("keras")
elif _is_tf_keras(keras_model):
keras_module = importlib.import_module("tensorflow.keras")
else:
raise MlflowException(
"Unable to infer keras module from the model, please specify "
"which keras module ('keras' or 'tensorflow.keras') is to be "
"used to save and load the model.")
elif type(keras_module) == str:
keras_module = importlib.import_module(keras_module)
# check if path exists
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
# construct new data folder in existing path
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# save custom objects if there are custom objects
if custom_objects is not None:
_save_custom_objects(data_path, custom_objects)
# save keras module spec to path/data/keras_module.txt
with open(os.path.join(data_path, _KERAS_MODULE_SPEC_PATH), "w") as f:
f.write(keras_module.__name__)
# By default, Keras uses the SavedModel format -- specified by "tf"
# However, we choose to align with prior default of mlflow, HDF5
save_format = kwargs.get("save_format", "h5")
# save keras save_format to path/data/save_format.txt
with open(os.path.join(data_path, _KERAS_SAVE_FORMAT_PATH), "w") as f:
f.write(save_format)
# save keras model
# To maintain prior behavior, when the format is HDF5, we save
# with the h5 file extension. Otherwise, model_path is a directory
# where the saved_model.pb will be stored (for SavedModel format)
file_extension = ".h5" if save_format == "h5" else ""
model_subpath = os.path.join(data_subpath, _MODEL_SAVE_PATH)
model_path = os.path.join(path, model_subpath) + file_extension
if path.startswith("/dbfs/"):
# The Databricks Filesystem uses a FUSE implementation that does not support
# random writes. It causes an error.
with tempfile.NamedTemporaryFile(suffix=".h5") as f:
keras_model.save(f.name, **kwargs)
f.flush() # force flush the data
shutil.copyfile(src=f.name, dst=model_path)
else:
keras_model.save(model_path, **kwargs)
# update flavor info to mlflow_model
mlflow_model.add_flavor(
FLAVOR_NAME,
keras_module=keras_module.__name__,
keras_version=keras_module.__version__,
save_format=save_format,
data=data_subpath,
)
# save conda.yaml info to path/conda.yml
if conda_env is None:
conda_env = get_default_conda_env(include_cloudpickle=custom_objects
is not None,
keras_module=keras_module)
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)
# append loader_module, data and env data to mlflow_model
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.keras",
data=data_subpath,
env=_CONDA_ENV_SUBPATH)
# save mlflow_model to path/MLmodel
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def test_tensor_multi_named_schema_enforcement():
m = Model()
input_schema = Schema([
TensorSpec(np.dtype(np.uint64), (-1, 5), "a"),
TensorSpec(np.dtype(np.short), (-1, 2), "b"),
TensorSpec(np.dtype(np.float32), (2, -1, 2), "c"),
])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
inp = {
"a": np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], dtype=np.uint64),
"b": np.array([[0, 0], [1, 1], [2, 2]], dtype=np.short),
"c": np.array([[[0, 0], [1, 1]], [[2, 2], [3, 3]]], dtype=np.float32),
}
# test that missing column raises
inp1 = {k: v for k, v in inp.items()}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp1.pop("b"))
assert "Model is missing inputs" in str(ex)
# test that extra column is ignored
inp2 = {k: v for k, v in inp.items()}
inp2["x"] = 1
# test that extra column is removed
res = pyfunc_model.predict(inp2)
assert res == {k: v for k, v in inp.items() if k in {"a", "b", "c"}}
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test that variable axes are supported
inp3 = {
"a":
np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]],
dtype=np.uint64),
"b":
np.array([[0, 0], [1, 1]], dtype=np.short),
"c":
np.array([[[0, 0]], [[2, 2]]], dtype=np.float32),
}
res = pyfunc_model.predict(inp3)
assert _compare_exact_tensor_dict_input(res, inp3)
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test that type casting is not supported
inp4 = {k: v for k, v in inp.items()}
inp4["a"] = inp4["a"].astype(np.int32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp4)
assert "dtype of input int32 does not match expected dtype uint64" in str(
ex)
# test wrong shape
inp5 = {
"a": np.array([[0, 0, 0, 0]], dtype=np.uint),
"b": np.array([[0, 0], [1, 1]], dtype=np.short),
"c": np.array([[[0, 0]]], dtype=np.float32),
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp5)
assert "Shape of input (1, 4) does not match expected shape (-1, 5)" in str(
ex)
# test non-dictionary input
inp6 = [
np.array([[0, 0, 0, 0, 0]], dtype=np.uint64),
np.array([[0, 0], [1, 1]], dtype=np.short),
np.array([[[0, 0]]], dtype=np.float32),
]
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp6)
assert "Model is missing inputs ['a', 'b', 'c']." in str(ex)
# test empty ndarray does not work
inp7 = {k: v for k, v in inp.items()}
inp7["a"] = np.array([])
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp7)
assert "Shape of input (0,) does not match expected shape" in str(ex)
# test dictionary of str -> list does not work
inp8 = {k: list(v) for k, v in inp.items()}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp8)
assert "This model contains a tensor-based model signature with input names" in str(
ex)
assert (
"suggests a dictionary input mapping input name to a numpy array, but a dict"
" with value type <class 'list'> was found") in str(ex)
# test dataframe input fails at shape enforcement
pdf = pd.DataFrame(
data=[[1, 2, 3]],
columns=["a", "b", "c"],
)
pdf["a"] = pdf["a"].astype(np.uint64)
pdf["b"] = pdf["b"].astype(np.short)
pdf["c"] = pdf["c"].astype(np.float32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Shape of input (1,) does not match expected shape (-1, 5)" in str(
ex)
def save_model(
lgb_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a LightGBM model to a path on the local file system.
:param lgb_model: LightGBM model (an instance of `lightgbm.Booster`_) or
models that implement the `scikit-learn API`_ to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
"""
import lightgbm as lgb
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
model_data_subpath = "model.lgb" if isinstance(
lgb_model, lgb.Booster) else "model.pkl"
model_data_path = os.path.join(path, model_data_subpath)
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# Save a LightGBM model
_save_model(lgb_model, model_data_path)
lgb_model_class = _get_fully_qualified_class_name(lgb_model)
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.lightgbm",
data=model_data_subpath,
env=_CONDA_ENV_FILE_NAME,
)
mlflow_model.add_flavor(
FLAVOR_NAME,
lgb_version=lgb.__version__,
data=model_data_subpath,
model_class=lgb_model_class,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements(
include_cloudpickle=not isinstance(lgb_model, lgb.Booster))
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
def test_column_schema_enforcement():
m = Model()
input_schema = Schema([
ColSpec("integer", "a"),
ColSpec("long", "b"),
ColSpec("float", "c"),
ColSpec("double", "d"),
ColSpec("boolean", "e"),
ColSpec("string", "g"),
ColSpec("binary", "f"),
ColSpec("datetime", "h"),
])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
pdf = pd.DataFrame(
data=[[
1, 2, 3, 4, True, "x",
bytes([1]), "2021-01-01 00:00:00.1234567"
]],
columns=["b", "d", "a", "c", "e", "g", "f", "h"],
dtype=np.object,
)
pdf["a"] = pdf["a"].astype(np.int32)
pdf["b"] = pdf["b"].astype(np.int64)
pdf["c"] = pdf["c"].astype(np.float32)
pdf["d"] = pdf["d"].astype(np.float64)
pdf["h"] = pdf["h"].astype(np.datetime64)
# test that missing column raises
with pytest.raises(MlflowException) as ex:
res = pyfunc_model.predict(pdf[["b", "d", "a", "e", "g", "f", "h"]])
assert "Model is missing inputs" in str(ex)
# test that extra column is ignored
pdf["x"] = 1
# test that columns are reordered, extra column is ignored
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
expected_types = dict(
zip(input_schema.input_names(), input_schema.pandas_types()))
# MLflow datetime type in input_schema does not encode precision, so add it for assertions
expected_types["h"] = np.dtype("datetime64[ns]")
actual_types = res.dtypes.to_dict()
assert expected_types == actual_types
# Test conversions
# 1. long -> integer raises
pdf["a"] = pdf["a"].astype(np.int64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
# 2. integer -> long works
pdf["b"] = pdf["b"].astype(np.int32)
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
pdf["b"] = pdf["b"].astype(np.int64)
# 3. unsigned int -> long works
pdf["b"] = pdf["b"].astype(np.uint32)
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
pdf["b"] = pdf["b"].astype(np.int64)
# 4. unsigned int -> int raises
pdf["a"] = pdf["a"].astype(np.uint32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
# 5. double -> float raises
pdf["c"] = pdf["c"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 6. float -> double works, double -> float does not
pdf["d"] = pdf["d"].astype(np.float32)
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
assert "Incompatible input types" in str(ex)
pdf["d"] = pdf["d"].astype(np.float64)
pdf["c"] = pdf["c"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 7. int -> float raises
pdf["c"] = pdf["c"].astype(np.int32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 8. int -> double works
pdf["d"] = pdf["d"].astype(np.int32)
pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
# 9. long -> double raises
pdf["d"] = pdf["d"].astype(np.int64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["d"] = pdf["d"].astype(np.float64)
# 10. any float -> any int raises
pdf["a"] = pdf["a"].astype(np.float32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
# 10. any float -> any int raises
pdf["a"] = pdf["a"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
pdf["b"] = pdf["b"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["b"] = pdf["b"].astype(np.int64)
pdf["b"] = pdf["b"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
pdf["b"] = pdf["b"].astype(np.int64)
assert "Incompatible input types" in str(ex)
# 11. objects work
pdf["b"] = pdf["b"].astype(np.object)
pdf["d"] = pdf["d"].astype(np.object)
pdf["e"] = pdf["e"].astype(np.object)
pdf["f"] = pdf["f"].astype(np.object)
pdf["g"] = pdf["g"].astype(np.object)
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
# 12. datetime64[D] (date only) -> datetime64[x] works
pdf["h"] = pdf["h"].astype("datetime64[D]")
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
pdf["h"] = pdf["h"].astype("datetime64[s]")
# 13. np.ndarrays can be converted to dataframe but have no columns
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf.values)
assert "Model is missing inputs" in str(ex)
# 14. dictionaries of str -> list/nparray work
arr = np.array([1, 2, 3])
d = {
"a":
arr.astype("int32"),
"b":
arr.astype("int64"),
"c":
arr.astype("float32"),
"d":
arr.astype("float64"),
"e": [True, False, True],
"g": ["a", "b", "c"],
"f": [bytes(0), bytes(1), bytes(1)],
"h":
np.array(["2020-01-01", "2020-02-02", "2020-03-03"],
dtype=np.datetime64),
}
res = pyfunc_model.predict(d)
assert res.dtypes.to_dict() == expected_types
# 15. dictionaries of str -> list[list] fail
d = {
"a": [arr.astype("int32")],
"b": [arr.astype("int64")],
"c": [arr.astype("float32")],
"d": [arr.astype("float64")],
"e": [[True, False, True]],
"g": [["a", "b", "c"]],
"f": [[bytes(0), bytes(1), bytes(1)]],
"h": [
np.array(["2020-01-01", "2020-02-02", "2020-03-03"],
dtype=np.datetime64)
],
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(d)
assert "Incompatible input types" in str(ex)
# 16. conversion to dataframe fails
d = {
"a": [1],
"b": [1, 2],
"c": [1, 2, 3],
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(d)
assert "This model contains a column-based signature, which suggests a DataFrame input." in str(
ex)
def save_model(tf_saved_model_dir,
tf_meta_graph_tags,
tf_signature_def_key,
path,
mlflow_model=Model(),
conda_env=None):
"""
Save a *serialized* collection of TensorFlow graphs and variables as an MLflow model
to a local path. This method operates on TensorFlow variables and graphs that have been
serialized in TensorFlow's ``SavedModel`` format. For more information about ``SavedModel``
format, see the TensorFlow documentation:
https://www.tensorflow.org/guide/saved_model#save_and_restore_models.
:param tf_saved_model_dir: Path to the directory containing serialized TensorFlow variables and
graphs in ``SavedModel`` format.
:param tf_meta_graph_tags: A list of tags identifying the model's metagraph within the
serialized ``SavedModel`` object. For more information, see the
``tags`` parameter of the
``tf.saved_model.builder.savedmodelbuilder`` method.
:param tf_signature_def_key: A string identifying the input/output signature associated with the
model. This is a key within the serialized ``savedmodel``
signature definition mapping. For more information, see the
``signature_def_map`` parameter of the
``tf.saved_model.builder.savedmodelbuilder`` method.
:param path: Local path where the MLflow model is to be saved.
:param mlflow_model: MLflow model configuration to which to add the ``tensorflow`` flavor.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model. The
following is an *example* dictionary representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'tensorflow=1.8.0'
]
}
"""
_logger.info(
"Validating the specified TensorFlow model by attempting to load it in a new TensorFlow"
" graph...")
_validate_saved_model(tf_saved_model_dir=tf_saved_model_dir,
tf_meta_graph_tags=tf_meta_graph_tags,
tf_signature_def_key=tf_signature_def_key)
_logger.info("Validation succeeded!")
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path),
DIRECTORY_NOT_EMPTY)
os.makedirs(path)
root_relative_path = _copy_file_or_tree(src=tf_saved_model_dir,
dst=path,
dst_dir=None)
model_dir_subpath = "tfmodel"
shutil.move(os.path.join(path, root_relative_path),
os.path.join(path, model_dir_subpath))
conda_env_subpath = "conda.yaml"
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_model.add_flavor(FLAVOR_NAME,
saved_model_dir=model_dir_subpath,
meta_graph_tags=tf_meta_graph_tags,
signature_def_key=tf_signature_def_key)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.tensorflow",
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(pytorch_model,
path,
conda_env=None,
mlflow_model=None,
code_paths=None,
pickle_module=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
requirements_file=None,
extra_files=None,
**kwargs):
"""
Save a PyTorch model to a path on the local file system.
:param pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of
``torch.nn.Module``) or scripted model prepared via ``torch.jit.script``
or ``torch.jit.trace``.
The model accept a single ``torch.FloatTensor`` as
input and produce a single output tensor.
If saving an eager model, any code dependencies of the
model's class, including the class definition itself, should be
included in one of the following locations:
- The package(s) listed in the model's Conda environment, specified
by the ``conda_env`` parameter.
- One or more of the files specified by the ``code_paths`` parameter.
:param path: Local path where the model is to be saved.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model. The
following is an *example* dictionary representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pytorch=0.4.1',
'torchvision=0.2.1'
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
: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 when the model is loaded.
:param pickle_module: The module that PyTorch should use to serialize ("pickle") the specified
``pytorch_model``. This is passed as the ``pickle_module`` parameter
to ``torch.save()``. By default, this module is also used to
deserialize ("unpickle") the PyTorch model at load time.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param requirements_file: A string containing the path to requirements file. Remote URIs
are resolved to absolute filesystem paths.
For example, consider the following ``requirements_file`` string -
requirements_file = "s3://my-bucket/path/to/my_file"
In this case, the ``"my_file"`` requirements file is downloaded from S3.
If ``None``, no requirements file is added to the model.
:param extra_files: A list containing the paths to corresponding extra files. Remote URIs
are resolved to absolute filesystem paths.
For example, consider the following ``extra_files`` list -
extra_files = ["s3://my-bucket/path/to/my_file1",
"s3://my-bucket/path/to/my_file2"]
In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3.
If ``None``, no extra files are added to the model.
:param kwargs: kwargs to pass to ``torch.save`` method.
.. code-block:: python
:caption: Example
import torch
import mlflow
import mlflow.pytorch
# Create model and set values
pytorch_model = Model()
pytorch_model_path = ...
# train our model
for epoch in range(500):
y_pred = pytorch_model(x_data)
...
# Save the model
with mlflow.start_run() as run:
mlflow.log_param("epochs", 500)
mlflow.pytorch.save_model(pytorch_model, pytorch_model_path)
# Saving scripted model
scripted_pytorch_model = torch.jit.script(model)
mlflow.pytorch.save_model(scripted_pytorch_model, pytorch_model_path)
"""
import torch
pickle_module = pickle_module or mlflow_pytorch_pickle_module
if not isinstance(pytorch_model, torch.nn.Module):
raise TypeError("Argument 'pytorch_model' should be a torch.nn.Module")
if code_paths is not None:
if not isinstance(code_paths, list):
raise TypeError(
"Argument code_paths should be a list, not {}".format(
type(code_paths)))
path = os.path.abspath(path)
if os.path.exists(path):
raise RuntimeError("Path '{}' already exists".format(path))
if mlflow_model is None:
mlflow_model = Model()
os.makedirs(path)
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
model_data_subpath = "data"
model_data_path = os.path.join(path, model_data_subpath)
os.makedirs(model_data_path)
# Persist the pickle module name as a file in the model's `data` directory. This is necessary
# because the `data` directory is the only available parameter to `_load_pyfunc`, and it
# does not contain the MLmodel configuration; therefore, it is not sufficient to place
# the module name in the MLmodel
#
# TODO: Stop persisting this information to the filesystem once we have a mechanism for
# supplying the MLmodel configuration to `mlflow.pytorch._load_pyfunc`
pickle_module_path = os.path.join(model_data_path,
_PICKLE_MODULE_INFO_FILE_NAME)
with open(pickle_module_path, "w") as f:
f.write(pickle_module.__name__)
# Save pytorch model
model_path = os.path.join(model_data_path,
_SERIALIZED_TORCH_MODEL_FILE_NAME)
if isinstance(pytorch_model, torch.jit.ScriptModule):
torch.jit.ScriptModule.save(pytorch_model, model_path)
else:
torch.save(pytorch_model,
model_path,
pickle_module=pickle_module,
**kwargs)
torchserve_artifacts_config = {}
if requirements_file:
if not isinstance(requirements_file, str):
raise TypeError("Path to requirements file should be a string")
with TempDir() as tmp_requirements_dir:
_download_artifact_from_uri(
artifact_uri=requirements_file,
output_path=tmp_requirements_dir.path())
rel_path = os.path.basename(requirements_file)
torchserve_artifacts_config[_REQUIREMENTS_FILE_KEY] = {
"path": rel_path
}
shutil.move(tmp_requirements_dir.path(rel_path), path)
if extra_files:
torchserve_artifacts_config[_EXTRA_FILES_KEY] = []
if not isinstance(extra_files, list):
raise TypeError("Extra files argument should be a list")
with TempDir() as tmp_extra_files_dir:
for extra_file in extra_files:
_download_artifact_from_uri(
artifact_uri=extra_file,
output_path=tmp_extra_files_dir.path())
rel_path = posixpath.join(
_EXTRA_FILES_KEY,
os.path.basename(extra_file),
)
torchserve_artifacts_config[_EXTRA_FILES_KEY].append(
{"path": rel_path})
shutil.move(
tmp_extra_files_dir.path(),
posixpath.join(path, _EXTRA_FILES_KEY),
)
conda_env_subpath = "conda.yaml"
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)
if code_paths is not None:
code_dir_subpath = "code"
for code_path in code_paths:
_copy_file_or_tree(src=code_path,
dst=path,
dst_dir=code_dir_subpath)
else:
code_dir_subpath = None
mlflow_model.add_flavor(
FLAVOR_NAME,
model_data=model_data_subpath,
pytorch_version=torch.__version__,
**torchserve_artifacts_config,
)
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.pytorch",
data=model_data_subpath,
pickle_module_name=pickle_module.__name__,
code=code_dir_subpath,
env=conda_env_subpath,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def log_model(artifact_path,
loader_module=None,
data_path=None,
code_path=None,
conda_env=None,
python_model=None,
artifacts=None):
"""
Create a custom Pyfunc model, incorporating custom inference logic and data dependencies.
For information about the workflows that this method supports, see :ref:`Workflows for
creating custom pyfunc models <pyfunc-create-custom-workflows>` and
:ref:`Which workflow is right for my use case? <pyfunc-create-custom-selecting-workflow>`.
You cannot specify the parameters for the first workflow: ``loader_module``, ``data_path``
and the parameters for the second workflow: ``python_model``, ``artifacts`` together.
:param artifact_path: The run-relative artifact path to which to log the Python model.
:param loader_module: The name of the Python module that will be used to load the model
from ``data_path``. This module must define a method with the prototype
``_load_pyfunc(data_path)``. If not *None*, this module and its
dependencies must be included in one of the following locations:
- The MLflow library.
- Package(s) listed in the model's Conda environment, specified by
the ``conda_env`` parameter.
- One or more of the files specified by the ``code_path`` parameter.
:param data_path: Path to a file or directory containing model data.
:param code_path: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files will be *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. This decribes the environment this model should
be run in. If ``python_model`` is not *None*, the Conda environment must
at least specify the dependencies contained in
:data:`mlflow.pyfunc.DEFAULT_CONDA_ENV`. If `None`, the default
:data:`mlflow.pyfunc.DEFAULT_CONDA_ENV` environment will be added to the
model. The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'cloudpickle==0.5.8'
]
}
:param python_model: An instance of a subclass of :class:`~PythonModel`. This class will be
serialized using the CloudPickle library. Any dependencies of the class
should be included in one of the following locations:
- The MLflow library.
- Package(s) listed in the model's Conda environment, specified by
the ``conda_env`` parameter.
- One or more of the files specified by the ``code_path`` parameter.
Note: If the class is imported from another module, as opposed to being
defined in the ``__main__`` scope, the defining module should also be
included in one of the listed locations.
:param artifacts: A dictionary containing ``<name, artifact_uri>`` entries. Remote artifact URIs
will be resolved to absolute filesystem paths, producing a dictionary of
``<name, absolute_path>`` entries. ``python_model`` can reference these
resolved entries as the ``artifacts`` property of the ``context`` parameter
in :func:`PythonModel.load_context() <mlflow.pyfunc.PythonModel.load_context>`
and :func:`PythonModel.predict() <mlflow.pyfunc.PythonModel.predict>`.
For example, consider the following ``artifacts`` dictionary::
{
"my_file": "s3://my-bucket/path/to/my/file"
}
In this case, the ``"my_file"`` artifact will be downloaded from S3. The
``python_model`` can then refer to ``"my_file"`` as an absolute filesystem
path via ``context.artifacts["my_file"]``.
If *None*, no artifacts will be added to the model.
"""
with TempDir() as tmp:
local_path = tmp.path(artifact_path)
run_id = active_run().info.run_id
save_model(path=local_path,
model=Model(artifact_path=artifact_path, run_id=run_id),
loader_module=loader_module,
data_path=data_path,
code_path=code_path,
conda_env=conda_env,
python_model=python_model,
artifacts=artifacts)
log_artifacts(local_path, artifact_path)
def save_model(tf_saved_model_dir,
tf_meta_graph_tags,
tf_signature_def_key,
path,
mlflow_model=Model(),
conda_env=None):
"""
Save a *serialized* collection of Tensorflow graphs and variables as an MLflow model
to a local path. This method operates on Tensorflow variables and graphs that have been
serialized in Tensorflow's `SavedModel` format. For more information about the `SavedModel`,
see the following Tensorflow documentation: https://www.tensorflow.org/guide/saved_model#
save_and_restore_models.
:param tf_saved_model_dir: Path to the directory containing serialized Tensorflow variables and
graphs in `SavedModel` format.
:param tf_meta_graph_tags: A list of tags identifying the model's metagraph within the
serialized `savedmodel` object. for more information, see the `tags`
parameter of the `tf.saved_model.builder.savedmodelbuilder` method:
https://www.tensorflow.org/api_docs/python/tf/saved_model/builder/
savedmodelbuilder#add_meta_graph
:param tf_signature_def_key: A string identifying the input/output signature associated with the
model. this is a key within the serialized `savedmodel`'s signature
definition mapping. for more information, see the
`signature_def_map` parameter of the
`tf.saved_model.builder.savedmodelbuilder` method.
:param path: Local path where the MLflow model is to be saved.
:param mlflow_model: MLflow model configuration to which this flavor will be added.
:param conda_env: Path to a Conda environment file. If provided, defines an environment for the
model. At minimum, it should specify python, tensorflow, and mlflow with
appropriate versions.
"""
eprint(
"Validating the specified Tensorflow model by attempting to load it in a new Tensorflow"
" graph...")
_validate_saved_model(tf_saved_model_dir=tf_saved_model_dir,
tf_meta_graph_tags=tf_meta_graph_tags,
tf_signature_def_key=tf_signature_def_key)
eprint("Validation succeeded!")
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path),
DIRECTORY_NOT_EMPTY)
os.makedirs(path)
root_relative_path = _copy_file_or_tree(src=tf_saved_model_dir,
dst=path,
dst_dir=None)
model_dir_subpath = "tfmodel"
shutil.move(os.path.join(path, root_relative_path),
os.path.join(path, model_dir_subpath))
mlflow_model.add_flavor(FLAVOR_NAME,
saved_model_dir=model_dir_subpath,
meta_graph_tags=tf_meta_graph_tags,
signature_def_key=tf_signature_def_key)
model_conda_env = None
if conda_env:
model_conda_env = os.path.basename(os.path.abspath(conda_env))
_copy_file_or_tree(src=conda_env, dst=path)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.tensorflow",
env=model_conda_env)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(
gluon_model,
path,
mlflow_model=None,
conda_env=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
):
"""
Save a Gluon model to a path on the local file system.
:param gluon_model: Gluon model to be saved. Must be already hybridized.
:param path: Local path where the model is to be saved.
:param mlflow_model: MLflow model config this flavor is being added to.
:param conda_env: Either a dictionary representation of a Conda environment or
the path to a Conda environment yaml file.
If provided, this decribes the environment this model should be
run in. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`mlflow.gluon.get_default_conda_env()` environment is added to
the model. The following is an *example* dictionary representation of a
Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'mxnet=1.5.0'
]
}
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
.. code-block:: python
:caption: Example
from mxnet.gluon import Trainer
from mxnet.gluon.contrib import estimator
from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
from mxnet.gluon.nn import HybridSequential
from mxnet.metric import Accuracy
import mlflow
# Build, compile, and train your model
gluon_model_path = ...
net = HybridSequential()
with net.name_scope():
...
net.hybridize()
net.collect_params().initialize()
softmax_loss = SoftmaxCrossEntropyLoss()
trainer = Trainer(net.collect_params())
est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer)
est.fit(train_data=train_data, epochs=100, val_data=validation_data)
# Save the model as an MLflow Model
mlflow.gluon.save_model(net, gluon_model_path)
"""
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# The epoch argument of the export method does not play any role in selecting
# a specific epoch's paramaters, and is there only for display purposes.
gluon_model.export(os.path.join(data_path, _MODEL_SAVE_PATH))
with open(os.path.join(path, "architecture.txt"), "w") as fp:
fp.write(str(gluon_model))
conda_env_subpath = "conda.yaml"
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)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.gluon",
env=conda_env_subpath)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def _save_model_with_class_artifacts_params(path,
python_model,
artifacts=None,
conda_env=None,
code_paths=None,
mlflow_model=Model()):
"""
:param path: The path to which to save the Python model.
:param python_model: An instance of a subclass of :class:`~PythonModel`. ``python_model``
defines how the model loads artifacts and how it performs inference.
:param artifacts: A dictionary containing ``<name, artifact_uri>`` entries.
Remote artifact URIs
are resolved to absolute filesystem paths, producing a dictionary of
``<name, absolute_path>`` entries. ``python_model`` can reference these
resolved entries as the ``artifacts`` property of the ``context``
attribute. If ``None``, no artifacts are added to the model.
:param conda_env: Either a dictionary representation of a Conda environment or the
path to a Conda environment yaml file. If provided, this decribes the
environment this model should be run in. At minimum, it should specify
the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model.
: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 mlflow_model: The model configuration to which to add the ``mlflow.pyfunc`` flavor.
"""
if os.path.exists(path):
raise MlflowException(message="Path '{}' already exists".format(path),
error_code=RESOURCE_ALREADY_EXISTS)
os.makedirs(path)
custom_model_config_kwargs = {
CONFIG_KEY_CLOUDPICKLE_VERSION: cloudpickle.__version__,
}
if isinstance(python_model, PythonModel):
saved_python_model_subpath = "python_model.pkl"
with open(os.path.join(path, saved_python_model_subpath), "wb") as out:
cloudpickle.dump(python_model, out)
custom_model_config_kwargs[
CONFIG_KEY_PYTHON_MODEL] = saved_python_model_subpath
else:
raise MlflowException(message=(
"`python_model` must be a subclass of `PythonModel`. Instead, found an"
" object of type: {python_model_type}".format(
python_model_type=type(python_model))),
error_code=INVALID_PARAMETER_VALUE)
if artifacts:
saved_artifacts_config = {}
with TempDir() as tmp_artifacts_dir:
tmp_artifacts_config = {}
saved_artifacts_dir_subpath = "artifacts"
for artifact_name, artifact_uri in artifacts.items():
tmp_artifact_path = _download_artifact_from_uri(
artifact_uri=artifact_uri,
output_path=tmp_artifacts_dir.path())
tmp_artifacts_config[artifact_name] = tmp_artifact_path
saved_artifact_subpath = os.path.join(
saved_artifacts_dir_subpath,
os.path.relpath(path=tmp_artifact_path,
start=tmp_artifacts_dir.path()))
saved_artifacts_config[artifact_name] = {
CONFIG_KEY_ARTIFACT_RELATIVE_PATH: saved_artifact_subpath,
CONFIG_KEY_ARTIFACT_URI: artifact_uri,
}
shutil.move(tmp_artifacts_dir.path(),
os.path.join(path, saved_artifacts_dir_subpath))
custom_model_config_kwargs[
CONFIG_KEY_ARTIFACTS] = saved_artifacts_config
conda_env_subpath = "conda.yaml"
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)
saved_code_subpath = None
if code_paths is not None:
saved_code_subpath = "code"
for code_path in code_paths:
_copy_file_or_tree(src=code_path,
dst=path,
dst_dir=saved_code_subpath)
mlflow.pyfunc.add_to_model(model=mlflow_model,
loader_module=__name__,
code=saved_code_subpath,
env=conda_env_subpath,
**custom_model_config_kwargs)
mlflow_model.save(os.path.join(path, 'MLmodel'))
def save_model(
spark_model,
path,
mlflow_model=None,
conda_env=None,
dfs_tmpdir=None,
sample_input=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
):
"""
Save a Spark MLlib Model to a local path.
By default, this function saves models using the Spark MLlib persistence mechanism.
Additionally, if a sample input is specified using the ``sample_input`` parameter, the model
is also serialized in MLeap format and the MLeap flavor is added.
:param spark_model: Spark model to be saved - MLflow can only save descendants of
pyspark.ml.Model which implement MLReadable and MLWritable.
:param path: Local path where the model is to be saved.
:param mlflow_model: MLflow model config this flavor is being added to.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If `None`, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pyspark=2.3.0'
]
}
:param dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local
filesystem if running in local mode. The model is be written in this
destination and then copied to the requested local path. This is necessary
as Spark ML models read from and write to DFS if running on a cluster. All
temporary files created on the DFS are removed if this operation
completes successfully. Defaults to ``/tmp/mlflow``.
:param sample_input: A sample input that is used to add the MLeap flavor to the model.
This must be a PySpark DataFrame that the model can evaluate. If
``sample_input`` is ``None``, the MLeap flavor is not added.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
.. code-block:: python
:caption: Example
from mlflow import spark
from pyspark.ml.pipeline.PipelineModel
# your pyspark.ml.pipeline.PipelineModel type
model = ...
mlflow.spark.save_model(model, "spark-model")
"""
_validate_model(spark_model)
from pyspark.ml import PipelineModel
if not isinstance(spark_model, PipelineModel):
spark_model = PipelineModel([spark_model])
if mlflow_model is None:
mlflow_model = Model()
# Spark ML stores the model on DFS if running on a cluster
# Save it to a DFS temp dir first and copy it to local path
if dfs_tmpdir is None:
dfs_tmpdir = DFS_TMP
tmp_path = _tmp_path(dfs_tmpdir)
spark_model.save(tmp_path)
sparkml_data_path = os.path.abspath(os.path.join(path, _SPARK_MODEL_PATH_SUB))
# We're copying the Spark model from DBFS to the local filesystem if (a) the temporary DFS URI
# we saved the Spark model to is a DBFS URI ("dbfs:/my-directory"), or (b) if we're running
# on a Databricks cluster and the URI is schemeless (e.g. looks like a filesystem absolute path
# like "/my-directory")
copying_from_dbfs = is_valid_dbfs_uri(tmp_path) or (
databricks_utils.is_in_cluster() and posixpath.abspath(tmp_path) == tmp_path
)
if copying_from_dbfs and databricks_utils.is_dbfs_fuse_available():
tmp_path_fuse = dbfs_hdfs_uri_to_fuse_path(tmp_path)
shutil.move(src=tmp_path_fuse, dst=sparkml_data_path)
else:
_HadoopFileSystem.copy_to_local_file(tmp_path, sparkml_data_path, remove_src=True)
_save_model_metadata(
dst_dir=path,
spark_model=spark_model,
mlflow_model=mlflow_model,
sample_input=sample_input,
conda_env=conda_env,
signature=signature,
input_example=input_example,
)
def save_model(fastai_learner,
path,
conda_env=None,
mlflow_model=Model(),
**kwargs):
"""
Save a fastai Learner to a path on the local file system.
:param fastai_learner: fastai Learner to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this describes the environment
this model should be run in. At minimum, it should specify the
dependencies contained in :func:`get_default_conda_env()`. If
``None``, the default :func:`get_default_conda_env()` environment is
added to the model. The following is an *example* dictionary
representation of a Conda environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'fastai=1.0.60',
]
}
:param mlflow_model: MLflow model config this flavor is being added to.
:param kwargs: kwargs to pass to ``Learner.save`` method.
"""
import fastai
from pathlib import Path
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
model_data_subpath = "model.fastai"
model_data_path = os.path.join(path, model_data_subpath)
model_data_path = Path(model_data_path)
os.makedirs(path)
# Save an Learner
fastai_learner.export(model_data_path, **kwargs)
conda_env_subpath = "conda.yaml"
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)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.fastai",
data=model_data_subpath,
env=conda_env_subpath)
mlflow_model.add_flavor(FLAVOR_NAME,
fastai_version=fastai.__version__,
data=model_data_subpath)
mlflow_model.save(os.path.join(path, "MLmodel"))
def save_model(
xgb_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
):
"""
Save an XGBoost model to a path on the local file system.
:param xgb_model: XGBoost model (an instance of `xgboost.Booster`_) to be saved.
Note that models that implement the `scikit-learn API`_ are not supported.
:param path: Local path where the model is to be saved.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this describes the environment
this model should be run in. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pip': [
'xgboost==0.90'
]
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
"""
import xgboost as xgb
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
model_data_subpath = "model.xgb"
model_data_path = os.path.join(path, model_data_subpath)
# Save an XGBoost model
xgb_model.save_model(model_data_path)
conda_env_subpath = "conda.yaml"
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)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.xgboost",
data=model_data_subpath,
env=conda_env_subpath)
mlflow_model.add_flavor(FLAVOR_NAME,
xgb_version=xgb.__version__,
data=model_data_subpath)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def save_model(
sk_model,
path,
conda_env=None,
mlflow_model=None,
serialization_format=SERIALIZATION_FORMAT_CLOUDPICKLE,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
):
"""
Save a scikit-learn model to a path on the local file system. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.sklearn`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for scikit-learn models
that define `predict()`, since `predict()` is required for pyfunc model inference.
:param sk_model: scikit-learn model to be saved.
:param path: Local path where the model is to be saved.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If `None`, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'scikit-learn=0.19.2'
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param serialization_format: The format in which to serialize the model. This should be one of
the formats listed in
``mlflow.sklearn.SUPPORTED_SERIALIZATION_FORMATS``. The Cloudpickle
format, ``mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE``,
provides better cross-system compatibility by identifying and
packaging code dependencies with the serialized model.
:param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: (Experimental) Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
.. code-block:: python
:caption: Example
import mlflow.sklearn
from sklearn.datasets import load_iris
from sklearn import tree
iris = load_iris()
sk_model = tree.DecisionTreeClassifier()
sk_model = sk_model.fit(iris.data, iris.target)
# Save the model in cloudpickle format
# set path to location for persistence
sk_path_dir_1 = ...
mlflow.sklearn.save_model(
sk_model, sk_path_dir_1,
serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE)
# save the model in pickle format
# set path to location for persistence
sk_path_dir_2 = ...
mlflow.sklearn.save_model(sk_model, sk_path_dir_2,
serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_PICKLE)
"""
import sklearn
if serialization_format not in SUPPORTED_SERIALIZATION_FORMATS:
raise MlflowException(
message=
("Unrecognized serialization format: {serialization_format}. Please specify one"
" of the following supported formats: {supported_formats}.".
format(
serialization_format=serialization_format,
supported_formats=SUPPORTED_SERIALIZATION_FORMATS,
)),
error_code=INVALID_PARAMETER_VALUE,
)
if os.path.exists(path):
raise MlflowException(message="Path '{}' already exists".format(path),
error_code=RESOURCE_ALREADY_EXISTS)
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
model_data_subpath = "model.pkl"
_save_model(
sk_model=sk_model,
output_path=os.path.join(path, model_data_subpath),
serialization_format=serialization_format,
)
conda_env_subpath = "conda.yaml"
if conda_env is None:
conda_env = get_default_conda_env(
include_cloudpickle=serialization_format ==
SERIALIZATION_FORMAT_CLOUDPICKLE)
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)
# `PyFuncModel` only works for sklearn models that define `predict()`.
if hasattr(sk_model, "predict"):
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.sklearn",
model_path=model_data_subpath,
env=conda_env_subpath,
)
mlflow_model.add_flavor(
FLAVOR_NAME,
pickled_model=model_data_subpath,
sklearn_version=sklearn.__version__,
serialization_format=serialization_format,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def save_model(
pr_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a Prophet model to a path on the local file system.
:param pr_model: Prophet model (an instance of Prophet() forecaster that has been fit
on a temporal series.
:param path: Local path where the serialized model (as JSON) is to be saved.
:param conda_env: {{ conda_env }}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
model = Prophet().fit(df)
train = model.history
predictions = model.predict(model.make_future_dataframe(30))
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
"""
import prophet
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException(f"Path '{path}' already exists")
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
model_data_path = os.path.join(path, _MODEL_BINARY_FILE_NAME)
_save_model(pr_model, model_data_path)
model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME}
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.prophet",
env=_CONDA_ENV_FILE_NAME,
**model_bin_kwargs)
flavor_conf = {
_MODEL_TYPE_KEY: pr_model.__class__.__name__,
**model_bin_kwargs,
}
mlflow_model.add_flavor(
FLAVOR_NAME,
prophet_version=prophet.__version__,
**flavor_conf,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
# cannot use inferred requirements due to prophet's build process
# as the package installation of pystan requires Cython to be present
# in the path. Prophet's installation itself requires imports of
# existing libraries, preventing the execution of a batched pip install
# and instead using a a strictly defined list of dependencies.
# NOTE: if Prophet .whl build architecture is changed, this should be
# modified to a standard inferred approach.
default_reqs = get_default_pip_requirements()
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
def save_model(
onnx_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
onnx_execution_providers=None,
):
"""
Save an ONNX model to a path on the local file system.
:param onnx_model: ONNX model to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example can be a Pandas DataFrame where the given
example will be serialized to json using the Pandas split-oriented
format, or a numpy array where the example will be serialized to json
by converting it to a list. Bytes are base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
:param onnx_execution_providers: List of strings defining onnxruntime execution providers.
Defaults to example:
``['CUDAExecutionProvider', 'CPUExecutionProvider']``
This uses GPU preferentially over CPU.
See onnxruntime API for further descriptions:
https://onnxruntime.ai/docs/execution-providers/
"""
import onnx
if onnx_execution_providers is None:
onnx_execution_providers = ONNX_EXECUTION_PROVIDERS
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException(message="Path '{}' already exists".format(path),
error_code=RESOURCE_ALREADY_EXISTS)
os.makedirs(path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
model_data_subpath = "model.onnx"
model_data_path = os.path.join(path, model_data_subpath)
# Save onnx-model
onnx.save_model(onnx_model, model_data_path)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.onnx",
data=model_data_subpath,
env=_CONDA_ENV_FILE_NAME)
mlflow_model.add_flavor(
FLAVOR_NAME,
onnx_version=onnx.__version__,
data=model_data_subpath,
providers=onnx_execution_providers,
)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
def save_model(sk_model,
path,
conda_env=None,
mlflow_model=Model(),
serialization_format=SERIALIZATION_FORMAT_CLOUDPICKLE):
"""
Save a scikit-learn model to a path on the local file system.
:param sk_model: scikit-learn model to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this decribes the environment
this model should be run in. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If `None`, the default
:func:`get_default_conda_env()`` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'scikit-learn=0.19.2'
]
}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param serialization_format: The format in which to serialize the model. This should be one of
the formats listed in
``mlflow.sklearn.SUPPORTED_SERIALIZATION_FORMATS``. The Cloudpickle
format, ``mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE``,
provides better cross-system compatibility by identifying and
packaging code dependencies with the serialized model.
>>> import mlflow.sklearn
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> sk_model = tree.DecisionTreeClassifier()
>>> sk_model = sk_model.fit(iris.data, iris.target)
>>> #Save the model in cloudpickle format
>>> #set path to location for persistence
>>> sk_path_dir_1 = ...
>>> mlflow.sklearn.save_model(
>>> sk_model, sk_path_dir_1,
>>> serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_CLOUDPICKLE)
>>>
>>> #Save the model in pickle format
>>> #set path to location for persistence
>>> sk_path_dir_2 = ...
>>> mlflow.sklearn.save_model(sk_model, sk_path_dir_2,
>>> serialization_format=mlflow.sklearn.SERIALIZATION_FORMAT_PICKLE)
"""
import sklearn
if serialization_format not in SUPPORTED_SERIALIZATION_FORMATS:
raise MlflowException(message=(
"Unrecognized serialization format: {serialization_format}. Please specify one"
" of the following supported formats: {supported_formats}.".format(
serialization_format=serialization_format,
supported_formats=SUPPORTED_SERIALIZATION_FORMATS)),
error_code=INVALID_PARAMETER_VALUE)
if os.path.exists(path):
raise MlflowException(message="Path '{}' already exists".format(path),
error_code=RESOURCE_ALREADY_EXISTS)
os.makedirs(path)
model_data_subpath = "model.pkl"
_save_model(sk_model=sk_model,
output_path=os.path.join(path, model_data_subpath),
serialization_format=serialization_format)
conda_env_subpath = "conda.yaml"
if conda_env is None:
conda_env = get_default_conda_env(
include_cloudpickle=serialization_format ==
SERIALIZATION_FORMAT_CLOUDPICKLE)
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)
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.sklearn",
data=model_data_subpath,
env=conda_env_subpath)
mlflow_model.add_flavor(FLAVOR_NAME,
pickled_model=model_data_subpath,
sklearn_version=sklearn.__version__,
serialization_format=serialization_format)
mlflow_model.save(os.path.join(path, "MLmodel"))
def log_model(spark_model,
artifact_path,
conda_env=None,
dfs_tmpdir=None,
sample_input=None):
"""
Log a Spark MLlib model as an MLflow artifact for the current run. This uses the
MLlib persistence format, and the logged model will have the Spark flavor.
:param spark_model: PipelineModel to be saved.
:param artifact_path: Run relative artifact path.
:param conda_env: Either a dictionary representation of a Conda environment or the path to a
Conda environment yaml file. If provided, this decribes the environment
this model should be run in. At minimum, it should specify the dependencies
contained in ``mlflow.spark.DEFAULT_CONDA_ENV``. If `None`, the default
``mlflow.spark.DEFAULT_CONDA_ENV`` environment will be added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pyspark=2.3.0'
]
}
:param dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local
filesystem if running in local mode. The model will be writen in this
destination and then copied into the model's artifact directory. This is
necessary as Spark ML models read from and write to DFS if running on a
cluster. If this operation completes successfully, all temporary files
created on the DFS are removed. Defaults to ``/tmp/mlflow``.
:param sample_input: A sample input used to add the MLeap flavor to the model.
This must be a PySpark DataFrame that the model can evaluate. If
``sample_input`` is ``None``, the MLeap flavor is not added.
>>> from pyspark.ml import Pipeline
>>> from pyspark.ml.classification import LogisticRegression
>>> from pyspark.ml.feature import HashingTF, Tokenizer
>>> training = spark.createDataFrame([
... (0, "a b c d e spark", 1.0),
... (1, "b d", 0.0),
... (2, "spark f g h", 1.0),
... (3, "hadoop mapreduce", 0.0) ], ["id", "text", "label"])
>>> tokenizer = Tokenizer(inputCol="text", outputCol="words")
>>> hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
>>> lr = LogisticRegression(maxIter=10, regParam=0.001)
>>> pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
>>> model = pipeline.fit(training)
>>> mlflow.spark.log_model(model, "spark-model")
"""
_validate_model(spark_model)
run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
run_root_artifact_uri = mlflow.get_artifact_uri()
# If the artifact URI is a local filesystem path, defer to Model.log() to persist the model,
# since Spark may not be able to write directly to the driver's filesystem. For example,
# writing to `file:/uri` will write to the local filesystem from each executor, which will
# be incorrect on multi-node clusters - to avoid such issues we just use the Model.log() path
# here.
if mlflow.tracking.utils._is_local_uri(run_root_artifact_uri):
return Model.log(artifact_path=artifact_path,
flavor=mlflow.spark,
spark_model=spark_model,
conda_env=conda_env,
dfs_tmpdir=dfs_tmpdir,
sample_input=sample_input)
# If Spark cannot write directly to the artifact repo, defer to Model.log() to persist the
# model
model_dir = os.path.join(run_root_artifact_uri, artifact_path)
try:
spark_model.save(os.path.join(model_dir, _SPARK_MODEL_PATH_SUB))
except Py4JJavaError:
return Model.log(artifact_path=artifact_path,
flavor=mlflow.spark,
spark_model=spark_model,
conda_env=conda_env,
dfs_tmpdir=dfs_tmpdir,
sample_input=sample_input)
# Otherwise, override the default model log behavior and save model directly to artifact repo
mlflow_model = Model(artifact_path=artifact_path, run_id=run_id)
with TempDir() as tmp:
tmp_model_metadata_dir = tmp.path()
_save_model_metadata(tmp_model_metadata_dir, spark_model, mlflow_model,
sample_input, conda_env)
mlflow.tracking.fluent.log_artifacts(tmp_model_metadata_dir,
artifact_path)
def save_model(
gluon_model,
path,
mlflow_model=None,
conda_env=None,
code_paths=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a Gluon model to a path on the local file system.
:param gluon_model: Gluon model to be saved. Must be already hybridized.
:param path: Local path where the model is to be saved.
:param mlflow_model: MLflow model config this flavor is being added to.
:param conda_env: {{ conda_env }}
: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 when the model is loaded.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example can be a Pandas DataFrame where the given
example will be serialized to json using the Pandas split-oriented
format, or a numpy array where the example will be serialized to json
by converting it to a list. Bytes are base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
.. code-block:: python
:caption: Example
from mxnet.gluon import Trainer
from mxnet.gluon.contrib import estimator
from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
from mxnet.gluon.nn import HybridSequential
from mxnet.metric import Accuracy
import mlflow
# Build, compile, and train your model
gluon_model_path = ...
net = HybridSequential()
with net.name_scope():
...
net.hybridize()
net.collect_params().initialize()
softmax_loss = SoftmaxCrossEntropyLoss()
trainer = Trainer(net.collect_params())
est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer)
est.fit(train_data=train_data, epochs=100, val_data=validation_data)
# Save the model as an MLflow Model
mlflow.gluon.save_model(net, gluon_model_path)
"""
import mxnet as mx
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_path)
code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# The epoch argument of the export method does not play any role in selecting
# a specific epoch's parameters, and is there only for display purposes.
gluon_model.export(os.path.join(data_path, _MODEL_SAVE_PATH))
pyfunc.add_to_model(mlflow_model,
loader_module="mlflow.gluon",
env=_CONDA_ENV_FILE_NAME,
code=code_dir_subpath)
mlflow_model.add_flavor(FLAVOR_NAME,
mxnet_version=mx.__version__,
code=code_dir_subpath)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
def save_model(path,
loader_module=None,
data_path=None,
code_path=None,
conda_env=None,
model=Model(),
python_model=None,
artifacts=None):
"""
Create a custom Pyfunc model, incorporating custom inference logic and data dependencies.
For information about the workflows that this method supports, please see :ref:`"workflows for
creating custom pyfunc models" <pyfunc-create-custom-workflows>` and
:ref:`"which workflow is right for my use case?" <pyfunc-create-custom-selecting-workflow>`.
Note that the parameters for the first workflow: ``loader_module``, ``data_path`` and the
parameters for the second workflow: ``python_model``, ``artifacts``, cannot be
specified together.
:param path: The path to which to save the Python model.
:param loader_module: The name of the Python module that will be used to load the model
from ``data_path``. This module must define a method with the prototype
``_load_pyfunc(data_path)``. If not *None*, this module and its
dependencies must be included in one of the following locations:
- The MLflow library.
- Package(s) listed in the model's Conda environment, specified by
the ``conda_env`` parameter.
- One or more of the files specified by the ``code_path`` parameter.
:param data_path: Path to a file or directory containing model data.
:param code_path: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files will be *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. This decribes the environment this model should
be run in. If ``python_model`` is not ``None``, the Conda environment must
at least specify the dependencies contained in
:data:`mlflow.pyfunc.DEFAULT_CONDA_ENV`. If `None`, the default
:data:`mlflow.pyfunc.DEFAULT_CONDA_ENV` environment will be added to the
model. The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'cloudpickle==0.5.8'
]
}
:param python_model: An instance of a subclass of :class:`~PythonModel`. This class will be
serialized using the CloudPickle library. Any dependencies of the class
should be included in one of the following locations:
- The MLflow library.
- Package(s) listed in the model's Conda environment, specified by
the ``conda_env`` parameter.
- One or more of the files specified by the ``code_path`` parameter.
Note: If the class is imported from another module, as opposed to being
defined in the ``__main__`` scope, the defining module should also be
included in one of the listed locations.
:param artifacts: A dictionary containing ``<name, artifact_uri>`` entries. Remote artifact URIs
will be resolved to absolute filesystem paths, producing a dictionary of
``<name, absolute_path>`` entries. ``python_model`` can reference these
resolved entries as the ``artifacts`` property of the ``context`` parameter
in :func:`PythonModel.load_context() <mlflow.pyfunc.PythonModel.load_context>`
and :func:`PythonModel.predict() <mlflow.pyfunc.PythonModel.predict>`.
For example, consider the following ``artifacts`` dictionary::
{
"my_file": "s3://my-bucket/path/to/my/file"
}
In this case, the ``"my_file"`` artifact will be downloaded from S3. The
``python_model`` can then refer to ``"my_file"`` as an absolute filesystem
path via ``context.artifacts["my_file"]``.
If *None*, no artifacts will be added to the model.
"""
first_argument_set = {
"loader_module": loader_module,
"data_path": data_path,
}
second_argument_set = {
"artifacts": artifacts,
"python_model": python_model,
}
first_argument_set_specified = any(
[item is not None for item in first_argument_set.values()])
second_argument_set_specified = any(
[item is not None for item in second_argument_set.values()])
if first_argument_set_specified and second_argument_set_specified:
raise MlflowException(message=(
"The following sets of parameters cannot be specified together: {first_set_keys}"
" and {second_set_keys}. All parameters in one set must be `None`. Instead, found"
" the following values: {first_set_entries} and {second_set_entries}"
.format(first_set_keys=first_argument_set.keys(),
second_set_keys=second_argument_set.keys(),
first_set_entries=first_argument_set,
second_set_entries=second_argument_set)),
error_code=INVALID_PARAMETER_VALUE)
elif (loader_module is None) and (python_model is None):
raise MlflowException(
message=
"Either `loader_module` or `python_model` must be specified!",
error_code=INVALID_PARAMETER_VALUE)
if first_argument_set_specified:
return _save_model_with_loader_module_and_data_path(
path=path,
loader_module=loader_module,
data_path=data_path,
code_paths=code_path,
conda_env=conda_env,
mlflow_model=model)
elif second_argument_set_specified:
return mlflow.pyfunc.model._save_model_with_class_artifacts_params(
path=path,
python_model=python_model,
artifacts=artifacts,
conda_env=conda_env,
code_paths=code_path,
mlflow_model=model)
def save_model(spark_model,
path,
mlflow_model=Model(),
conda_env=None,
dfs_tmpdir=None,
sample_input=None):
"""
Save a Spark MLlib Model to a local path.
By default, this function saves models using the Spark MLlib persistence mechanism.
Additionally, if a sample input is specified using the ``sample_input`` parameter, the model
is also serialized in MLeap format and the MLeap flavor is added.
:param spark_model: Spark model to be saved - MLFlow can only save descendants of
pyspark.ml.Model which implement MLReadable and MLWritable.
:param path: Local path where the model is to be saved.
:param mlflow_model: MLflow model config this flavor is being added to.
: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. At minimum, it should specify the dependencies
contained in :func:`get_default_conda_env()`. If `None`, the default
:func:`get_default_conda_env()` environment is added to the model.
The following is an *example* dictionary representation of a Conda
environment::
{
'name': 'mlflow-env',
'channels': ['defaults'],
'dependencies': [
'python=3.7.0',
'pyspark=2.3.0'
]
}
:param dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local
filesystem if running in local mode. The model is be written in this
destination and then copied to the requested local path. This is necessary
as Spark ML models read from and write to DFS if running on a cluster. All
temporary files created on the DFS are removed if this operation
completes successfully. Defaults to ``/tmp/mlflow``.
:param sample_input: A sample input that is used to add the MLeap flavor to the model.
This must be a PySpark DataFrame that the model can evaluate. If
``sample_input`` is ``None``, the MLeap flavor is not added.
>>> from mlflow import spark
>>> from pyspark.ml.pipeline.PipelineModel
>>>
>>> #your pyspark.ml.pipeline.PipelineModel type
>>> model = ...
>>> mlflow.spark.save_model(model, "spark-model")
"""
_validate_model(spark_model)
from pyspark.ml import PipelineModel
if not isinstance(spark_model, PipelineModel):
spark_model = PipelineModel([spark_model])
# Spark ML stores the model on DFS if running on a cluster
# Save it to a DFS temp dir first and copy it to local path
if dfs_tmpdir is None:
dfs_tmpdir = DFS_TMP
tmp_path = _tmp_path(dfs_tmpdir)
spark_model.save(tmp_path)
sparkml_data_path = os.path.abspath(
os.path.join(path, _SPARK_MODEL_PATH_SUB))
_HadoopFileSystem.copy_to_local_file(tmp_path,
sparkml_data_path,
remove_src=True)
_save_model_metadata(dst_dir=path,
spark_model=spark_model,
mlflow_model=mlflow_model,
sample_input=sample_input,
conda_env=conda_env)
def save_model(
spacy_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a spaCy model to a path on the local file system.
:param spacy_model: spaCy model to be saved.
:param path: Local path where the model is to be saved.
:param conda_env: {{ conda_env }}
:param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
:param signature: :py:class:`ModelSignature <mlflow.models.ModelSignature>`
describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
from datasets with valid model input (e.g. the training dataset with target
column omitted) and valid model output (e.g. model predictions generated on
the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
:param input_example: Input example provides one or several instances of valid
model input. The example can be used as a hint of what data to feed the
model. The given example will be converted to a Pandas DataFrame and then
serialized to json using the Pandas split-oriented format. Bytes are
base64-encoded.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
"""
import spacy
_validate_env_arguments(conda_env, pip_requirements,
extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException(
"Unable to save MLflow model to {path} - path '{path}' "
"already exists".format(path=path))
model_data_subpath = "model.spacy"
model_data_path = os.path.join(path, model_data_subpath)
os.makedirs(model_data_path)
if mlflow_model is None:
mlflow_model = Model()
if signature is not None:
mlflow_model.signature = signature
if input_example is not None:
_save_example(mlflow_model, input_example, path)
# Save spacy-model
spacy_model.to_disk(path=model_data_path)
# Save the pyfunc flavor if at least one text categorizer in spaCy pipeline
if any([
isinstance(pipe_component[1], spacy.pipeline.TextCategorizer)
for pipe_component in spacy_model.pipeline
]):
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.spacy",
data=model_data_subpath,
env=_CONDA_ENV_FILE_NAME,
)
else:
_logger.warning(
"Generating only the spacy flavor for the provided spacy model. This means the model "
"can be loaded back via `mlflow.spacy.load_model`, but cannot be loaded back using "
"pyfunc APIs like `mlflow.pyfunc.load_model` or via the `mlflow models` CLI commands. "
"MLflow will only generate the pyfunc flavor for spacy models containing a pipeline "
"component that is an instance of spacy.pipeline.TextCategorizer.")
mlflow_model.add_flavor(FLAVOR_NAME,
spacy_version=spacy.__version__,
data=model_data_subpath)
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
model_data_path,
FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(
conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME),
"\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME),
"\n".join(pip_requirements))
