Source code for mlflow.pytorch

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

PyTorch (native) format
    This is the main flavor that can be loaded back into PyTorch.
:py:mod:`mlflow.pyfunc`
    Produced for use by generic pyfunc-based deployment tools and batch inference.
"""
import importlib
import logging
import os
import yaml

import cloudpickle
import numpy as np
import pandas as pd
from packaging.version import Version
import posixpath

import mlflow
import shutil
import mlflow.pyfunc.utils as pyfunc_utils
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.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST
from mlflow.pytorch import pickle_module as mlflow_pytorch_pickle_module
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.annotations import experimental
from mlflow.utils.environment import _mlflow_conda_env, _log_pip_requirements
from mlflow.utils.file_utils import _copy_file_or_tree, TempDir
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 = "pytorch"

_SERIALIZED_TORCH_MODEL_FILE_NAME = "model.pth"
_TORCH_STATE_DICT_FILE_NAME = "state_dict.pth"
_PICKLE_MODULE_INFO_FILE_NAME = "pickle_module_info.txt"
_EXTRA_FILES_KEY = "extra_files"
_REQUIREMENTS_FILE_KEY = "requirements_file"

_logger = logging.getLogger(__name__)


[docs]def get_default_conda_env(): """ :return: The default Conda environment as a dictionary for MLflow Models produced by calls to :func:`save_model()` and :func:`log_model()`. .. code-block:: python :caption: Example import mlflow.pytorch # Log PyTorch model with mlflow.start_run() as run: mlflow.pytorch.log_model(model, "model") # Fetch the associated conda environment env = mlflow.pytorch.get_default_conda_env() print("conda env: {}".format(env)) .. code-block:: text :caption: Output conda env {'name': 'mlflow-env', 'channels': ['conda-forge'], 'dependencies': ['python=3.7.5', {'pip': ['torch==1.5.1', 'torchvision==0.6.1', 'mlflow', 'cloudpickle==1.6.0']}]} """ import torch import torchvision return _mlflow_conda_env( additional_pip_deps=[ "torch=={}".format(torch.__version__), "torchvision=={}".format(torchvision.__version__), # We include CloudPickle in the default environment because # it's required by the default pickle module used by `save_model()` # and `log_model()`: `mlflow.pytorch.pickle_module`. "cloudpickle=={}".format(cloudpickle.__version__), ] )
[docs]def log_model( pytorch_model, artifact_path, conda_env=None, code_paths=None, pickle_module=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, requirements_file=None, extra_files=None, **kwargs ): """ Log a PyTorch model as an MLflow artifact for the current run. :param pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of ``torch.nn.Module``) or scripted model prepared via ``torch.jit.script`` or ``torch.jit.trace``. The model accept a single ``torch.FloatTensor`` as input and produce a single output tensor. If saving an eager model, any code dependencies of the model's class, including the class definition itself, should be included in one of the following locations: - The package(s) listed in the model's Conda environment, specified by the ``conda_env`` parameter. - One or more of the files specified by the ``code_paths`` parameter. :param artifact_path: Run-relative artifact path. :param conda_env: Path to a Conda environment file. If provided, this decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in :func:`get_default_conda_env()`. If ``None``, the default :func:`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=3.7.0', 'pytorch=0.4.1', 'torchvision=0.2.1' ] } :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 pickle_module: The module that PyTorch should use to serialize ("pickle") the specified ``pytorch_model``. This is passed as the ``pickle_module`` parameter to ``torch.save()``. By default, this module is also used to deserialize ("unpickle") the PyTorch model at load time. :param 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. :param signature: (Experimental) :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: (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 can be a Pandas DataFrame where the given example will be 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. :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 requirements_file: A string containing the path to requirements file. Remote URIs are resolved to absolute filesystem paths. For example, consider the following ``requirements_file`` string - requirements_file = "s3://my-bucket/path/to/my_file" In this case, the ``"my_file"`` requirements file is downloaded from S3. If ``None``, no requirements file is added to the model. :param extra_files: A list containing the paths to corresponding extra files. Remote URIs are resolved to absolute filesystem paths. For example, consider the following ``extra_files`` list - extra_files = ["s3://my-bucket/path/to/my_file1", "s3://my-bucket/path/to/my_file2"] In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3. If ``None``, no extra files are added to the model. :param kwargs: kwargs to pass to ``torch.save`` method. .. code-block:: python :caption: Example import numpy as np import torch import mlflow.pytorch class LinearNNModel(torch.nn.Module): def __init__(self): super(LinearNNModel, self).__init__() self.linear = torch.nn.Linear(1, 1) # One in and one out def forward(self, x): y_pred = self.linear(x) return y_pred def gen_data(): # Example linear model modified to use y = 2x # from https://github.com/hunkim/PyTorchZeroToAll # X training data, y labels X = torch.arange(1.0, 25.0).view(-1, 1) y = torch.from_numpy(np.array([x * 2 for x in X])).view(-1, 1) return X, y # Define model, loss, and optimizer model = LinearNNModel() criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001) # Training loop epochs = 250 X, y = gen_data() for epoch in range(epochs): # Forward pass: Compute predicted y by passing X to the model y_pred = model(X) # Compute the loss loss = criterion(y_pred, y) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step() # Log the model with mlflow.start_run() as run: mlflow.pytorch.log_model(model, "model") # convert to scripted model and log the model scripted_pytorch_model = torch.jit.script(model) mlflow.pytorch.log_model(scripted_pytorch_model, "scripted_model") # Fetch the logged model artifacts print("run_id: {}".format(run.info.run_id)) for artifact_path in ["model/data", "scripted_model/data"]: artifacts = [f.path for f in MlflowClient().list_artifacts(run.info.run_id, artifact_path)] print("artifacts: {}".format(artifacts)) .. code-block:: text :caption: Output run_id: 1a1ec9e413ce48e9abf9aec20efd6f71 artifacts: ['model/data/model.pth', 'model/data/pickle_module_info.txt'] artifacts: ['scripted_model/data/model.pth', 'scripted_model/data/pickle_module_info.txt'] .. figure:: ../_static/images/pytorch_logged_models.png PyTorch logged models """ pickle_module = pickle_module or mlflow_pytorch_pickle_module Model.log( artifact_path=artifact_path, flavor=mlflow.pytorch, pytorch_model=pytorch_model, conda_env=conda_env, code_paths=code_paths, pickle_module=pickle_module, registered_model_name=registered_model_name, signature=signature, input_example=input_example, await_registration_for=await_registration_for, requirements_file=requirements_file, extra_files=extra_files, **kwargs, )
[docs]def save_model( pytorch_model, path, conda_env=None, mlflow_model=None, code_paths=None, pickle_module=None, signature: ModelSignature = None, input_example: ModelInputExample = None, requirements_file=None, extra_files=None, **kwargs ): """ Save a PyTorch model to a path on the local file system. :param pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of ``torch.nn.Module``) or scripted model prepared via ``torch.jit.script`` or ``torch.jit.trace``. The model accept a single ``torch.FloatTensor`` as input and produce a single output tensor. If saving an eager model, any code dependencies of the model's class, including the class definition itself, should be included in one of the following locations: - The package(s) listed in the model's Conda environment, specified by the ``conda_env`` parameter. - One or more of the files specified by the ``code_paths`` parameter. :param path: Local path where the model is to be saved. :param 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 :func:`get_default_conda_env()`. If ``None``, the default :func:`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=3.7.0', 'pytorch=0.4.1', 'torchvision=0.2.1' ] } :param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. :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 pickle_module: The module that PyTorch should use to serialize ("pickle") the specified ``pytorch_model``. This is passed as the ``pickle_module`` parameter to ``torch.save()``. By default, this module is also used to deserialize ("unpickle") the PyTorch model at load time. :param signature: (Experimental) :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: (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 can be a Pandas DataFrame where the given example will be 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. :param requirements_file: A string containing the path to requirements file. Remote URIs are resolved to absolute filesystem paths. For example, consider the following ``requirements_file`` string - requirements_file = "s3://my-bucket/path/to/my_file" In this case, the ``"my_file"`` requirements file is downloaded from S3. If ``None``, no requirements file is added to the model. :param extra_files: A list containing the paths to corresponding extra files. Remote URIs are resolved to absolute filesystem paths. For example, consider the following ``extra_files`` list - extra_files = ["s3://my-bucket/path/to/my_file1", "s3://my-bucket/path/to/my_file2"] In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3. If ``None``, no extra files are added to the model. :param kwargs: kwargs to pass to ``torch.save`` method. .. code-block:: python :caption: Example import os import torch import mlflow.pytorch # Class defined here class LinearNNModel(torch.nn.Module): ... # Initialize our model, criterion and optimizer ... # Training loop ... # Save PyTorch models to current working directory with mlflow.start_run() as run: mlflow.pytorch.save_model(model, "model") # Convert to a scripted model and save it scripted_pytorch_model = torch.jit.script(model) mlflow.pytorch.save_model(scripted_pytorch_model, "scripted_model") # Load each saved model for inference for model_path in ["model", "scripted_model"]: model_uri = "{}/{}".format(os.getcwd(), model_path) loaded_model = mlflow.pytorch.load_model(model_uri) print("Loaded {}:".format(model_path)) for x in [6.0, 8.0, 12.0, 30.0]: X = torch.Tensor([[x]]) y_pred = loaded_model(X) print("predict X: {}, y_pred: {:.2f}".format(x, y_pred.data.item())) print("--") .. code-block:: text :caption: Output Loaded model: predict X: 6.0, y_pred: 11.90 predict X: 8.0, y_pred: 15.92 predict X: 12.0, y_pred: 23.96 predict X: 30.0, y_pred: 60.13 -- Loaded scripted_model: predict X: 6.0, y_pred: 11.90 predict X: 8.0, y_pred: 15.92 predict X: 12.0, y_pred: 23.96 predict X: 30.0, y_pred: 60.13 """ import torch pickle_module = pickle_module or mlflow_pytorch_pickle_module if not isinstance(pytorch_model, torch.nn.Module): raise TypeError("Argument 'pytorch_model' should be a torch.nn.Module") if code_paths is not None: if not isinstance(code_paths, list): raise TypeError("Argument code_paths should be a list, not {}".format(type(code_paths))) path = os.path.abspath(path) if os.path.exists(path): raise RuntimeError("Path '{}' already exists".format(path)) if mlflow_model is None: mlflow_model = Model() os.makedirs(path) 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 = "data" model_data_path = os.path.join(path, model_data_subpath) os.makedirs(model_data_path) # Persist the pickle module name as a file in the model's `data` directory. This is necessary # because the `data` directory is the only available parameter to `_load_pyfunc`, and it # does not contain the MLmodel configuration; therefore, it is not sufficient to place # the module name in the MLmodel # # TODO: Stop persisting this information to the filesystem once we have a mechanism for # supplying the MLmodel configuration to `mlflow.pytorch._load_pyfunc` pickle_module_path = os.path.join(model_data_path, _PICKLE_MODULE_INFO_FILE_NAME) with open(pickle_module_path, "w") as f: f.write(pickle_module.__name__) # Save pytorch model model_path = os.path.join(model_data_path, _SERIALIZED_TORCH_MODEL_FILE_NAME) if isinstance(pytorch_model, torch.jit.ScriptModule): torch.jit.ScriptModule.save(pytorch_model, model_path) else: torch.save(pytorch_model, model_path, pickle_module=pickle_module, **kwargs) torchserve_artifacts_config = {} if extra_files: torchserve_artifacts_config[_EXTRA_FILES_KEY] = [] if not isinstance(extra_files, list): raise TypeError("Extra files argument should be a list") with TempDir() as tmp_extra_files_dir: for extra_file in extra_files: _download_artifact_from_uri( artifact_uri=extra_file, output_path=tmp_extra_files_dir.path() ) rel_path = posixpath.join(_EXTRA_FILES_KEY, os.path.basename(extra_file),) torchserve_artifacts_config[_EXTRA_FILES_KEY].append({"path": rel_path}) shutil.move( tmp_extra_files_dir.path(), posixpath.join(path, _EXTRA_FILES_KEY), ) conda_env_subpath = "conda.yaml" if conda_env is None: conda_env = get_default_conda_env() elif not isinstance(conda_env, dict): with open(conda_env, "r") as f: conda_env = yaml.safe_load(f) with open(os.path.join(path, conda_env_subpath), "w") as f: yaml.safe_dump(conda_env, stream=f, default_flow_style=False) if requirements_file: if not isinstance(requirements_file, str): raise TypeError("Path to requirements file should be a string") with TempDir() as tmp_requirements_dir: _download_artifact_from_uri( artifact_uri=requirements_file, output_path=tmp_requirements_dir.path() ) rel_path = os.path.basename(requirements_file) torchserve_artifacts_config[_REQUIREMENTS_FILE_KEY] = {"path": rel_path} shutil.move(tmp_requirements_dir.path(rel_path), path) else: _log_pip_requirements(conda_env, path) if code_paths is not None: code_dir_subpath = "code" for code_path in code_paths: _copy_file_or_tree(src=code_path, dst=path, dst_dir=code_dir_subpath) else: code_dir_subpath = None mlflow_model.add_flavor( FLAVOR_NAME, model_data=model_data_subpath, pytorch_version=torch.__version__, **torchserve_artifacts_config, ) pyfunc.add_to_model( mlflow_model, loader_module="mlflow.pytorch", data=model_data_subpath, pickle_module_name=pickle_module.__name__, code=code_dir_subpath, env=conda_env_subpath, ) mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
def _load_model(path, **kwargs): """ :param path: The path to a serialized PyTorch model. :param kwargs: Additional kwargs to pass to the PyTorch ``torch.load`` function. """ import torch if os.path.isdir(path): # `path` is a directory containing a serialized PyTorch model and a text file containing # information about the pickle module that should be used by PyTorch to load it model_path = os.path.join(path, "model.pth") pickle_module_path = os.path.join(path, _PICKLE_MODULE_INFO_FILE_NAME) with open(pickle_module_path, "r") as f: pickle_module_name = f.read() if "pickle_module" in kwargs and kwargs["pickle_module"].__name__ != pickle_module_name: _logger.warning( "Attempting to load the PyTorch model with a pickle module, '%s', that does not" " match the pickle module that was used to save the model: '%s'.", kwargs["pickle_module"].__name__, pickle_module_name, ) else: try: kwargs["pickle_module"] = importlib.import_module(pickle_module_name) except ImportError as exc: raise MlflowException( message=( "Failed to import the pickle module that was used to save the PyTorch" " model. Pickle module name: `{pickle_module_name}`".format( pickle_module_name=pickle_module_name ) ), error_code=RESOURCE_DOES_NOT_EXIST, ) from exc else: model_path = path if Version(torch.__version__) >= Version("1.5.0"): return torch.load(model_path, **kwargs) else: try: # load the model as an eager model. return torch.load(model_path, **kwargs) except Exception: # If fails, assume the model as a scripted model return torch.jit.load(model_path)
[docs]def load_model(model_uri, **kwargs): """ Load a PyTorch 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>`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html# artifact-locations>`_. :param kwargs: kwargs to pass to ``torch.load`` method. :return: A PyTorch model. .. code-block:: python :caption: Example import torch import mlflow.pytorch # Class defined here class LinearNNModel(torch.nn.Module): ... # Initialize our model, criterion and optimizer ... # Training loop ... # Log the model with mlflow.start_run() as run: mlflow.pytorch.log_model(model, "model") # Inference after loading the logged model model_uri = "runs:/{}/model".format(run.info.run_id) loaded_model = mlflow.pytorch.load_model(model_uri) for x in [4.0, 6.0, 30.0]: X = torch.Tensor([[x]]) y_pred = loaded_model(X) print("predict X: {}, y_pred: {:.2f}".format(x, y_pred.data.item())) .. code-block:: text :caption: Output predict X: 4.0, y_pred: 7.57 predict X: 6.0, y_pred: 11.64 predict X: 30.0, y_pred: 60.48 """ import torch local_model_path = _download_artifact_from_uri(artifact_uri=model_uri) try: pyfunc_conf = _get_flavor_configuration( model_path=local_model_path, flavor_name=pyfunc.FLAVOR_NAME ) except MlflowException: pyfunc_conf = {} code_subpath = pyfunc_conf.get(pyfunc.CODE) if code_subpath is not None: pyfunc_utils._add_code_to_system_path( code_path=os.path.join(local_model_path, code_subpath) ) pytorch_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) if torch.__version__ != pytorch_conf["pytorch_version"]: _logger.warning( "Stored model version '%s' does not match installed PyTorch version '%s'", pytorch_conf["pytorch_version"], torch.__version__, ) torch_model_artifacts_path = os.path.join(local_model_path, pytorch_conf["model_data"]) return _load_model(path=torch_model_artifacts_path, **kwargs)
def _load_pyfunc(path, **kwargs): """ Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``. :param path: Local filesystem path to the MLflow Model with the ``pytorch`` flavor. """ return _PyTorchWrapper(_load_model(path, **kwargs)) class _PyTorchWrapper(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, pytorch_model): self.pytorch_model = pytorch_model def predict(self, data, device="cpu"): import torch 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 PyTorch 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") self.pytorch_model.to(device) self.pytorch_model.eval() with torch.no_grad(): input_tensor = torch.from_numpy(inp_data).to(device) preds = self.pytorch_model(input_tensor) if not isinstance(preds, torch.Tensor): raise TypeError( "Expected PyTorch model to output a single output tensor, " "but got output of type '{}'".format(type(preds)) ) if isinstance(data, pd.DataFrame): predicted = pd.DataFrame(preds.numpy()) predicted.index = data.index else: predicted = preds.numpy() return predicted
[docs]@experimental def log_state_dict(state_dict, artifact_path, **kwargs): """ Log a state_dict as an MLflow artifact for the current run. .. warning:: This function just logs a state_dict as an artifact and doesn't generate an :ref:`MLflow Model <models>`. :param state_dict: state_dict to be saved. :param artifact_path: Run-relative artifact path. :param kwargs: kwargs to pass to ``torch.save``. .. code-block:: python :caption: Example # Log a model as a state_dict with mlflow.start_run(): state_dict = model.state_dict() mlflow.pytorch.log_state_dict(state_dict, artifact_path="model") # Log a checkpoint as a state_dict with mlflow.start_run(): state_dict = { "model": model.state_dict(), "optimizer": optimizer.state_dict(), "epoch": epoch, "loss": loss, } mlflow.pytorch.log_state_dict(state_dict, artifact_path="checkpoint") """ with TempDir() as tmp: local_path = tmp.path() save_state_dict(state_dict=state_dict, path=local_path, **kwargs) mlflow.log_artifacts(local_path, artifact_path)
[docs]@experimental def save_state_dict(state_dict, path, **kwargs): """ Save a state_dict to a path on the local file system :param state_dict: state_dict to be saved. :param path: Local path where the state_dict is to be saved. :param kwargs: kwargs to pass to ``torch.save``. """ import torch # The object type check here aims to prevent a scenario where a user accidentally passees # a model instead of a state_dict and `torch.save` (which accepts both model and state_dict) # successfully completes, leaving the user unaware of the mistake. if not isinstance(state_dict, dict): raise TypeError( "Invalid object type for `state_dict`: {}. Must be an instance of `dict`".format( type(state_dict) ) ) os.makedirs(path, exist_ok=True) state_dict_path = os.path.join(path, _TORCH_STATE_DICT_FILE_NAME) torch.save(state_dict, state_dict_path, **kwargs)
[docs]@experimental def load_state_dict(state_dict_uri, **kwargs): """ Load a state_dict from a local file or a run. :param state_dict_uri: The location, in URI format, of the state_dict, for example: - ``/Users/me/path/to/local/state_dict`` - ``relative/path/to/local/state_dict`` - ``s3://my_bucket/path/to/state_dict`` - ``runs:/<mlflow_run_id>/run-relative/path/to/state_dict`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html# artifact-locations>`_. :param kwargs: kwargs to pass to ``torch.load``. :return: A state_dict .. code-block:: python :caption: Example with mlflow.start_run(): artifact_path = "model" mlflow.pytorch.log_state_dict(model.state_dict(), artifact_path) state_dict_uri = mlflow.get_artifact_uri(artifact_path) state_dict = mlflow.pytorch.load_state_dict(state_dict_uri) """ import torch local_path = _download_artifact_from_uri(artifact_uri=state_dict_uri) state_dict_path = os.path.join(local_path, _TORCH_STATE_DICT_FILE_NAME) return torch.load(state_dict_path, **kwargs)
[docs]@experimental @autologging_integration(FLAVOR_NAME) def autolog( log_every_n_epoch=1, log_models=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, ): # pylint: disable=unused-argument """ Enables (or disables) and configures autologging from `PyTorch Lightning <https://pytorch-lightning.readthedocs.io/en/latest>`_ to MLflow. Autologging is performed when you call the `fit` method of `pytorch_lightning.Trainer() \ <https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#>`_. Explore the complete `PyTorch MNIST \ <https://github.com/mlflow/mlflow/tree/master/examples/pytorch/MNIST>`_ for an expansive example with implementation of additional lightening steps. **Note**: Autologging is only supported for PyTorch Lightning models, i.e., models that subclass `pytorch_lightning.LightningModule \ <https://pytorch-lightning.readthedocs.io/en/latest/lightning_module.html>`_. In particular, autologging support for vanilla PyTorch models that only subclass `torch.nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_ is not yet available. :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 PyTorch Lightning autologging integration. If ``False``, enables the PyTorch Lightning 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 disable_for_unsupported_versions: If ``True``, disable autologging for versions of pytorch and pytorch-lightning that have not been tested against this version of the MLflow client or are incompatible. :param silent: If ``True``, suppress all event logs and warnings from MLflow during PyTorch Lightning autologging. If ``False``, show all events and warnings during PyTorch Lightning autologging. .. code-block:: python :caption: Example import os import pytorch_lightning as pl import torch from torch.nn import functional as F from torch.utils.data import DataLoader from torchvision import transforms from torchvision.datasets import MNIST from pytorch_lightning.metrics.functional import accuracy import mlflow.pytorch from mlflow.tracking import MlflowClient # For brevity, here is the simplest most minimal example with just a training # loop step, (no validation, no testing). It illustrates how you can use MLflow # to auto log parameters, metrics, and models. class MNISTModel(pl.LightningModule): def __init__(self): super(MNISTModel, self).__init__() self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_nb): x, y = batch loss = F.cross_entropy(self(x), y) acc = accuracy(loss, y) # Use the current of PyTorch logger self.log("train_loss", loss, on_epoch=True) self.log("acc", acc, on_epoch=True) return loss def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) def print_auto_logged_info(r): tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")} artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")] print("run_id: {}".format(r.info.run_id)) print("artifacts: {}".format(artifacts)) print("params: {}".format(r.data.params)) print("metrics: {}".format(r.data.metrics)) print("tags: {}".format(tags)) # Initialize our model mnist_model = MNISTModel() # Initialize DataLoader from MNIST Dataset train_ds = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()) train_loader = DataLoader(train_ds, batch_size=32) # Initialize a trainer trainer = pl.Trainer(max_epochs=20, progress_bar_refresh_rate=20) # Auto log all MLflow entities mlflow.pytorch.autolog() # Train the model with mlflow.start_run() as run: trainer.fit(mnist_model, train_loader) # fetch the auto logged parameters and metrics print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id)) .. code-block:: text :caption: Output run_id: 42caa17b60cb489c8083900fb52506a7 artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/data'] params: {'betas': '(0.9, 0.999)', 'weight_decay': '0', 'epochs': '20', 'eps': '1e-08', 'lr': '0.02', 'optimizer_name': 'Adam', ' amsgrad': 'False'} metrics: {'acc_step': 0.0, 'train_loss_epoch': 1.0917967557907104, 'train_loss_step': 1.0794280767440796, 'train_loss': 1.0794280767440796, 'acc_epoch': 0.0033333334140479565, 'acc': 0.0} tags: {'Mode': 'training'} .. figure:: ../_static/images/pytorch_lightening_autolog.png PyTorch autologged MLflow entities """ import pytorch_lightning as pl from mlflow.pytorch._pytorch_autolog import _create_patch_fit fit = _create_patch_fit(log_every_n_epoch=log_every_n_epoch, log_models=log_models) safe_patch(FLAVOR_NAME, pl.Trainer, "fit", fit, manage_run=True)