mlflow.paddle

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.

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.

mlflow.paddle.get_default_conda_env()[source]
Returns

The default Conda environment for MLflow Models produced by calls to save_model() and log_model().

mlflow.paddle.get_default_pip_requirements()[source]
Returns

A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.paddle.load_model(model_uri, model=None, **kwargs)[source]

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>

Parameters
  • model – Required when loading a paddle.Model model saved with training=True.

  • kwargs – The keyword arguments to pass to paddle.jit.load or model.load.

For more information about supported URI schemes, see Referencing Artifacts.

Returns

A paddle model.

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)
mlflow.paddle.log_model(pd_model, artifact_path, training=False, conda_env=None, registered_model_name=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list]] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None)[source]

Log a paddle model as an MLflow artifact for the current run. Produces an MLflow Model containing the following flavors:

  • mlflow.paddle

  • mlflow.pyfunc. NOTE: This flavor is only included for paddle models that define predict(), since predict() is required for pyfunc model inference.

Parameters
  • pd_model – paddle model to be saved.

  • artifact_path – Run-relative artifact path.

  • 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.

  • 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 get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
        "name": "mlflow-env",
        "channels": ["conda-forge"],
        "dependencies": [
            "python=3.7.0",
            {
                "pip": [
                    "paddle==x.y.z"
                ],
            },
        ],
    }
    

  • 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.

  • signature

    ModelSignature describes model input and output Schema. The model signature can be inferred 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)

  • 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.

  • 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.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["paddle", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

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)
mlflow.paddle.save_model(pd_model, path, training=False, conda_env=None, mlflow_model=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list]] = None, pip_requirements=None, extra_pip_requirements=None)[source]

Save a paddle model to a path on the local file system. Produces an MLflow Model containing the following flavors:

  • mlflow.paddle

  • mlflow.pyfunc. NOTE: This flavor is only included for paddle models that define predict(), since predict() is required for pyfunc model inference.

Parameters
  • pd_model – paddle model to be saved.

  • path – Local path where the model is to be saved.

  • 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.

  • 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 get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:

    {
        "name": "mlflow-env",
        "channels": ["conda-forge"],
        "dependencies": [
            "python=3.7.0",
            {
                "pip": [
                    "paddle==x.y.z"
                ],
            },
        ],
    }
    

  • mlflow_modelmlflow.models.Model this flavor is being added to.

  • signature

    ModelSignature describes model input and output Schema. The model signature can be inferred 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)

  • 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.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["paddle", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

  • extra_pip_requirements

    Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.

    Warning

    The following arguments can’t be specified at the same time:

    • conda_env

    • pip_requirements

    • extra_pip_requirements

    This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.

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)