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.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)[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 decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default 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=3.8.2',
            'paddlepaddle=2.1.0'
        ]
    }
    

  • 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.

  • signature

    (Experimental) 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 – (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.

  • 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.

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)[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 decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default 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=3.8.2',
            'paddle=2.1.0'
        ]
    }
    

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

  • signature

    (Experimental) 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 – (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.

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)