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.autolog(log_every_n_epoch=1, log_models=True, disable=False, exclusive=False, silent=False, registered_model_name=None, extra_tags=None)[source]

Note

Autologging is known to be compatible with the following package versions: 2.4.1 <= paddlepaddle <= 2.6.2. Autologging may not succeed when used with package versions outside of this range.

Enables (or disables) and configures autologging from PaddlePaddle to MLflow.

Autologging is performed when the fit method of paddle.Model is called.

Parameters

log_every_n_epoch – If specified, logs metrics once every n epochs. By default, metrics are logged after every epoch.
log_models – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged.
disable – If True, disables the PaddlePaddle autologging integration. If False, enables the PaddlePaddle autologging integration.
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.
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.
registered_model_name – If given, each time a model is trained, it is registered as a new model version of the registered model with this name. The registered model is created if it does not already exist.
extra_tags – A dictionary of extra tags to set on each managed run created by autologging.

Example

import paddle
import mlflow
from mlflow import MlflowClient


def show_run_data(run_id):
    run = mlflow.get_run(run_id)
    print(f"params: {run.data.params}")
    print(f"metrics: {run.data.metrics}")
    client = MlflowClient()
    artifacts = [f.path for f in client.list_artifacts(run.info.run_id, "model")]
    print(f"artifacts: {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)

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",
]

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, dst_path=None, **kwargs)[source]

Load a paddle model from a local file or a run.

Parameters

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>
model – Required when loading a paddle.Model model saved with training=True.
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.
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, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=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 a 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=3.8.15",
        {
            "pip": [
                "paddle==x.y.z"
            ],
        },
    ],
}
```
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. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
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 –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
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.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

Returns

A ModelInfo instance that contains the metadata of the logged model.

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, code_paths=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=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 a 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=3.8.15",
        {
            "pip": [
                "paddle==x.y.z"
            ],
        },
    ],
}
```
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. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
mlflow_model – mlflow.models.Model this flavor is being added to.
signature –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
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.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

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_diabetes
from sklearn.model_selection import train_test_split
from sklearn import preprocessing


def load_data():
    # dataset on boston housing prediction
    X, y = load_diabetes(return_X_y=True, as_frame=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().__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(f"epoch: {epoch_id}, iter: {iter_id}, loss is: {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)