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__init__.py
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"""
The ``mlflow.paddle`` module provides an API for logging and loading paddle models.
This module exports paddle models with the following flavors:
Paddle (native) format
This is the main flavor that can be loaded back into paddle.
:py:mod:`mlflow.pyfunc`
Produced for use by generic pyfunc-based deployment tools and batch inference.
NOTE: The `mlflow.pyfunc` flavor is only added for paddle models that define `predict()`,
since `predict()` is required for pyfunc model inference.
"""
import os
import logging
import yaml
import mlflow
from mlflow import pyfunc
from mlflow.exceptions import MlflowException
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import ModelSignature
from mlflow.models.utils import ModelInputExample, _save_example
from mlflow.protos.databricks_pb2 import RESOURCE_ALREADY_EXISTS
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.environment import (
_mlflow_conda_env,
_validate_env_arguments,
_process_pip_requirements,
_process_conda_env,
_CONDA_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_CONSTRAINTS_FILE_NAME,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement
from mlflow.utils.docstring_utils import format_docstring, LOG_MODEL_PARAM_DOCS
from mlflow.utils.file_utils import write_to
from mlflow.utils.model_utils import _get_flavor_configuration
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.utils.autologging_utils import autologging_integration, safe_patch
FLAVOR_NAME = "paddle"
_logger = logging.getLogger(__name__)
def get_default_pip_requirements():
"""
:return: A list of default pip requirements for MLflow Models produced by this flavor.
Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment
that, at minimum, contains these requirements.
"""
return [_get_pinned_requirement("paddlepaddle", module="paddle")]
def get_default_conda_env():
"""
:return: The default Conda environment for MLflow Models produced by calls to
:func:`save_model()` and :func:`log_model()`.
"""
return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
def save_model(
pd_model,
path,
training=False,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a paddle model to a path on the local file system. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.paddle`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models
that define `predict()`, since `predict()` is required for pyfunc model inference.
:param pd_model: paddle model to be saved.
:param path: Local path where the model is to be saved.
:param training: Only valid when saving a model trained using the PaddlePaddle high level API.
If set to True, the saved model supports both re-training and
inference. If set to False, it only supports inference.
: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 }}
.. code-block:: python
:caption: Example
import mlflow.paddle
import paddle
from paddle.nn import Linear
import paddle.nn.functional as F
import numpy as np
import os
import random
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
def load_data():
# dataset on boston housing prediction
X, y = load_boston(return_X_y=True)
min_max_scaler = preprocessing.MinMaxScaler()
X_min_max = min_max_scaler.fit_transform(X)
X_normalized = preprocessing.scale(X_min_max, with_std=False)
X_train, X_test, y_train, y_test = train_test_split(
X_normalized, y, test_size=0.2, random_state=42)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
return np.concatenate(
(X_train, y_train), axis=1),np.concatenate((X_test, y_test), axis=1
)
class Regressor(paddle.nn.Layer):
def __init__(self):
super(Regressor, self).__init__()
self.fc = Linear(in_features=13, out_features=1)
@paddle.jit.to_static
def forward(self, inputs):
x = self.fc(inputs)
return x
model = Regressor()
model.train()
training_data, test_data = load_data()
opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
EPOCH_NUM = 10
BATCH_SIZE = 10
for epoch_id in range(EPOCH_NUM):
np.random.shuffle(training_data)
mini_batches = [training_data[k : k + BATCH_SIZE]
for k in range(0, len(training_data), BATCH_SIZE)]
for iter_id, mini_batch in enumerate(mini_batches):
x = np.array(mini_batch[:, :-1]).astype('float32')
y = np.array(mini_batch[:, -1:]).astype('float32')
house_features = paddle.to_tensor(x)
prices = paddle.to_tensor(y)
predicts = model(house_features)
loss = F.square_error_cost(predicts, label=prices)
avg_loss = paddle.mean(loss)
if iter_id%20==0:
print("epoch: {}, iter: {}, loss is: {}".format(
epoch_id, iter_id, avg_loss.numpy()))
avg_loss.backward()
opt.step()
opt.clear_grad()
mlflow.log_param('learning_rate', 0.01)
mlflow.paddle.log_model(model, "model")
sk_path_dir = './test-out'
mlflow.paddle.save_model(model, sk_path_dir)
print("Model saved in run %s" % mlflow.active_run().info.run_uuid)
"""
import paddle
_validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
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"
output_path = os.path.join(path, model_data_subpath)
if isinstance(pd_model, paddle.Model):
pd_model.save(output_path, training=training)
else:
paddle.jit.save(pd_model, output_path)
# `PyFuncModel` only works for paddle models that define `predict()`.
pyfunc.add_to_model(
mlflow_model,
loader_module="mlflow.paddle",
model_path=model_data_subpath,
env=_CONDA_ENV_FILE_NAME,
)
mlflow_model.add_flavor(
FLAVOR_NAME, pickled_model=model_data_subpath, paddle_version=paddle.__version__,
)
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 load_model(model_uri, model=None, dst_path=None, **kwargs):
"""
Load a paddle model from a local file or a run.
:param model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
- ``models:/<model_name>/<model_version>``
- ``models:/<model_name>/<stage>``
:param model: Required when loading a `paddle.Model` model saved with `training=True`.
:param dst_path: The local filesystem path to which to download the model artifact.
This directory must already exist. If unspecified, a local output
path will be created.
:param kwargs: The keyword arguments to pass to `paddle.jit.load`
or `model.load`.
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
:return: A paddle model.
.. code-block:: python
:caption: Example
import mlflow.paddle
pd_model = mlflow.paddle.load_model("runs:/96771d893a5e46159d9f3b49bf9013e2/pd_models")
# use Pandas DataFrame to make predictions
np_array = ...
predictions = pd_model(np_array)
"""
import paddle
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path)
flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
pd_model_artifacts_path = os.path.join(local_model_path, flavor_conf["pickled_model"])
if model is None:
return paddle.jit.load(pd_model_artifacts_path, **kwargs)
elif not isinstance(model, paddle.Model):
raise TypeError(
"Invalid object type `{}` for `model`, must be `paddle.Model`".format(type(model))
)
else:
contains_pdparams = _contains_pdparams(local_model_path)
if not contains_pdparams:
raise TypeError(
"This model can't be loaded via `model.load` because a '.pdparams' file "
"doesn't exist. Please leave `model` unspecified to load the model via "
"`paddle.jit.load` or set `training` to True when saving a model."
)
model.load(
pd_model_artifacts_path, **kwargs,
)
return model
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
def log_model(
pd_model,
artifact_path,
training=False,
conda_env=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Log a paddle model as an MLflow artifact for the current run. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.paddle`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models
that define `predict()`, since `predict()` is required for pyfunc model inference.
:param pd_model: paddle model to be saved.
:param artifact_path: Run-relative artifact path.
:param training: Only valid when saving a model trained using the PaddlePaddle high level API.
If set to True, the saved model supports both re-training and
inference. If set to False, it only supports inference.
:param conda_env: {{ conda_env }}
:param registered_model_name: 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: :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 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.
:param pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
.. code-block:: python
:caption: Example
import mlflow.paddle
def load_data():
...
class Regressor():
...
model = Regressor()
model.train()
training_data, test_data = load_data()
opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())
EPOCH_NUM = 10
BATCH_SIZE = 10
for epoch_id in range(EPOCH_NUM):
...
mlflow.log_param('learning_rate', 0.01)
mlflow.paddle.log_model(model, "model")
sk_path_dir = ...
mlflow.paddle.save_model(model, sk_path_dir)
"""
return Model.log(
artifact_path=artifact_path,
flavor=mlflow.paddle,
pd_model=pd_model,
conda_env=conda_env,
registered_model_name=registered_model_name,
signature=signature,
input_example=input_example,
await_registration_for=await_registration_for,
training=training,
pip_requirements=pip_requirements,
extra_pip_requirements=extra_pip_requirements,
)
def _load_pyfunc(path):
"""
Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``.
:param path: Local filesystem path to the MLflow Model with the ``paddle`` flavor.
"""
return _PaddleWrapper(load_model(path))
class _PaddleWrapper(object):
"""
Wrapper class that creates a predict function such that
predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)
"""
def __init__(self, pd_model):
self.pd_model = pd_model
def predict(self, data):
import pandas as pd
import numpy as np
if isinstance(data, pd.DataFrame):
inp_data = data.values.astype(np.float32)
elif isinstance(data, np.ndarray):
inp_data = data
elif isinstance(data, (list, dict)):
raise TypeError(
"The paddle flavor does not support List or Dict input types. "
"Please use a pandas.DataFrame or a numpy.ndarray"
)
else:
raise TypeError("Input data should be pandas.DataFrame or numpy.ndarray")
inp_data = np.squeeze(inp_data)
self.pd_model.eval()
predicted = self.pd_model(inp_data)
return pd.DataFrame(predicted.numpy())
def _contains_pdparams(path):
file_list = os.listdir(path)
return any(".pdparams" in file for file in file_list)
@autologging_integration(FLAVOR_NAME)
def autolog(
log_every_n_epoch=1, log_models=True, disable=False, exclusive=False, silent=False,
): # pylint: disable=unused-argument
"""
Enables (or disables) and configures autologging from PaddlePaddle to MLflow.
Autologging is performed when the `fit` method of `paddle.Model`_ is called.
.. _paddle.Model:
https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/Model_en.html
:param log_every_n_epoch: If specified, logs metrics once every `n` epochs. By default, metrics
are logged after every epoch.
:param log_models: If ``True``, trained models are logged as MLflow model artifacts.
If ``False``, trained models are not logged.
:param disable: If ``True``, disables the PaddlePaddle autologging integration.
If ``False``, enables the PaddlePaddle autologging integration.
:param exclusive: If ``True``, autologged content is not logged to user-created fluent runs.
If ``False``, autologged content is logged to the active fluent run,
which may be user-created.
:param silent: If ``True``, suppress all event logs and warnings from MLflow during PyTorch
Lightning autologging. If ``False``, show all events and warnings during
PaddlePaddle autologging.
.. code-block:: python
:caption: Example
import paddle
import mlflow
def show_run_data(run_id):
run = mlflow.get_run(run_id)
print("params: {}".format(run.data.params))
print("metrics: {}".format(run.data.metrics))
client = mlflow.tracking.MlflowClient()
artifacts = [f.path for f in client.list_artifacts(run.info.run_id, "model")]
print("artifacts: {}".format(artifacts))
class LinearRegression(paddle.nn.Layer):
def __init__(self):
super().__init__()
self.fc = paddle.nn.Linear(13, 1)
def forward(self, feature):
return self.fc(feature)
train_dataset = paddle.text.datasets.UCIHousing(mode="train")
eval_dataset = paddle.text.datasets.UCIHousing(mode="test")
model = paddle.Model(LinearRegression())
optim = paddle.optimizer.SGD(learning_rate=1e-2, parameters=model.parameters())
model.prepare(optim, paddle.nn.MSELoss(), paddle.metric.Accuracy())
mlflow.paddle.autolog()
with mlflow.start_run() as run:
model.fit(train_dataset, eval_dataset, batch_size=16, epochs=10)
show_run_data(run.info.run_id)
.. code-block:: text
:caption: Output
params: {
"learning_rate": "0.01",
"optimizer_name": "SGD",
}
metrics: {
"loss": 17.482044,
"step": 25.0,
"acc": 0.0,
"eval_step": 6.0,
"eval_acc": 0.0,
"eval_batch_size": 6.0,
"batch_size": 4.0,
"eval_loss": 24.717455,
}
artifacts: [
"model/MLmodel",
"model/conda.yaml",
"model/model.pdiparams",
"model/model.pdiparams.info",
"model/model.pdmodel",
"model/requirements.txt",
]
"""
import paddle
from mlflow.paddle._paddle_autolog import patched_fit
safe_patch(FLAVOR_NAME, paddle.Model, "fit", patched_fit, manage_run=True)