Source code for mlflow.spacy

"""
The ``mlflow.spacy`` module provides an API for logging and loading spaCy models.
This module exports spacy models with the following flavors:

spaCy (native) format
    This is the main flavor that can be loaded back into spaCy.
:py:mod:`mlflow.pyfunc`
    Produced for use by generic pyfunc-based deployment tools and batch inference, this
    flavor is created only if spaCy's model pipeline has at least one
    `TextCategorizer <https://spacy.io/api/textcategorizer>`_.
"""
import logging
import os

import pandas as pd
import yaml

import mlflow
from mlflow import pyfunc
from mlflow.exceptions import MlflowException
from mlflow.models import Model, ModelSignature
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.utils import ModelInputExample, _save_example
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,
    _PYTHON_ENV_FILE_NAME,
    _PythonEnv,
)
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,
    _validate_and_copy_code_paths,
    _add_code_from_conf_to_system_path,
    _validate_and_prepare_target_save_path,
)

FLAVOR_NAME = "spacy"

_logger = logging.getLogger(__name__)


[docs]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("spacy")]
[docs]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())
[docs]@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) def save_model( spacy_model, path, conda_env=None, code_paths=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 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 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) _validate_and_prepare_target_save_path(path) model_data_subpath = "model.spacy" model_data_path = os.path.join(path, model_data_subpath) os.makedirs(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) # 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, code=code_dir_subpath, ) 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, 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( 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)) _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
[docs]@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) def log_model( spacy_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, pip_requirements=None, extra_pip_requirements=None, **kwargs, ): """ Log a spaCy model as an MLflow artifact for the current run. :param spacy_model: spaCy model to be saved. :param artifact_path: Run-relative artifact path. :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 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 pip_requirements: {{ pip_requirements }} :param extra_pip_requirements: {{ extra_pip_requirements }} :param kwargs: kwargs to pass to ``spacy.save_model`` method. :return: A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the metadata of the logged model. """ return Model.log( artifact_path=artifact_path, flavor=mlflow.spacy, registered_model_name=registered_model_name, spacy_model=spacy_model, conda_env=conda_env, code_paths=code_paths, signature=signature, input_example=input_example, pip_requirements=pip_requirements, extra_pip_requirements=extra_pip_requirements, **kwargs, )
def _load_model(path): import spacy path = os.path.abspath(path) return spacy.load(path) class _SpacyModelWrapper: def __init__(self, spacy_model): self.spacy_model = spacy_model def predict(self, dataframe): """ Only works for predicting using text categorizer. Not suitable for other pipeline components (e.g: parser) :param dataframe: pandas dataframe containing texts to be categorized expected shape is (n_rows,1 column) :return: dataframe with predictions """ if len(dataframe.columns) != 1: raise MlflowException("Shape of input dataframe must be (n_rows, 1column)") return pd.DataFrame( {"predictions": dataframe.iloc[:, 0].apply(lambda text: self.spacy_model(text).cats)} ) def _load_pyfunc(path): """ Load PyFunc implementation. Called by ``pyfunc.load_model``. :param path: Local filesystem path to the MLflow Model with the ``spacy`` flavor. """ return _SpacyModelWrapper(_load_model(path))
[docs]def load_model(model_uri, dst_path=None): """ Load a spaCy model from a local file (if ``run_id`` is ``None``) 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>`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html# artifact-locations>`_. :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. :return: A spaCy loaded model """ 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) _add_code_from_conf_to_system_path(local_model_path, flavor_conf) # Flavor configurations for models saved in MLflow version <= 0.8.0 may not contain a # `data` key; in this case, we assume the model artifact path to be `model.spacy` spacy_model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.spacy")) return _load_model(path=spacy_model_file_path)