from packaging.version import Version
import os
import numpy as np
import pandas as pd
import yaml
import mlflow
from mlflow import pyfunc
from mlflow.exceptions import MlflowException
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import ModelSignature
from mlflow.models.utils import ModelInputExample, _save_example
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.annotations import experimental
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,
)
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.autologging_utils import (
autologging_integration,
safe_patch,
ExceptionSafeClass,
try_mlflow_log,
batch_metrics_logger,
)
FLAVOR_NAME = "gluon"
_MODEL_SAVE_PATH = "net"
[docs]@experimental
def load_model(model_uri, ctx):
"""
Load a Gluon 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 ctx: Either CPU or GPU.
:return: A Gluon model instance.
.. code-block:: python
:caption: Example
# Load persisted model as a Gluon model, make inferences against an NDArray
model = mlflow.gluon.load_model("runs:/" + gluon_random_data_run.info.run_id + "/model")
model(nd.array(np.random.rand(1000, 1, 32)))
"""
import mxnet as mx
from mxnet import gluon
from mxnet import sym
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri)
model_arch_path = os.path.join(local_model_path, "data", _MODEL_SAVE_PATH) + "-symbol.json"
model_params_path = os.path.join(local_model_path, "data", _MODEL_SAVE_PATH) + "-0000.params"
if Version(mx.__version__) >= Version("2.0.0"):
return gluon.SymbolBlock.imports(
model_arch_path, input_names=["data"], param_file=model_params_path, ctx=ctx
)
else:
symbol = sym.load(model_arch_path)
inputs = sym.var("data", dtype="float32")
net = gluon.SymbolBlock(symbol, inputs)
net.collect_params().load(model_params_path, ctx)
return net
class _GluonModelWrapper:
def __init__(self, gluon_model):
self.gluon_model = gluon_model
def predict(self, data):
"""
:param data: Either a pandas DataFrame or a numpy array containing input array values.
If the input is a DataFrame, it will be converted to an array first by a
`ndarray = df.values`.
:return: Model predictions. If the input is a pandas.DataFrame, the predictions are returned
in a pandas.DataFrame. If the input is a numpy array, the predictions are returned
as either a numpy.ndarray or a plain list for hybrid models.
"""
import mxnet as mx
if isinstance(data, pd.DataFrame):
ndarray = mx.nd.array(data.values)
preds = self.gluon_model(ndarray)
if isinstance(preds, mx.ndarray.ndarray.NDArray):
preds = preds.asnumpy()
return pd.DataFrame(preds)
elif isinstance(data, np.ndarray):
ndarray = mx.nd.array(data)
preds = self.gluon_model(ndarray)
if isinstance(preds, mx.ndarray.ndarray.NDArray):
preds = preds.asnumpy()
return preds
else:
raise TypeError("Input data should be pandas.DataFrame or numpy.ndarray")
def _load_pyfunc(path):
"""
Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``.
:param path: Local filesystem path to the MLflow Model with the ``gluon`` flavor.
"""
import mxnet as mx
m = load_model(path, mx.current_context())
return _GluonModelWrapper(m)
[docs]@experimental
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="mxnet"))
def save_model(
gluon_model,
path,
mlflow_model=None,
conda_env=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Save a Gluon model to a path on the local file system.
:param gluon_model: Gluon model to be saved. Must be already hybridized.
:param path: Local path where the model is to be saved.
:param mlflow_model: MLflow model config this flavor is being added to.
:param conda_env: {{ conda_env }}
: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 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 pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
.. code-block:: python
:caption: Example
from mxnet.gluon import Trainer
from mxnet.gluon.contrib import estimator
from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
from mxnet.gluon.nn import HybridSequential
from mxnet.metric import Accuracy
import mlflow
# Build, compile, and train your model
gluon_model_path = ...
net = HybridSequential()
with net.name_scope():
...
net.hybridize()
net.collect_params().initialize()
softmax_loss = SoftmaxCrossEntropyLoss()
trainer = Trainer(net.collect_params())
est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer)
est.fit(train_data=train_data, epochs=100, val_data=validation_data)
# Save the model as an MLflow Model
mlflow.gluon.save_model(net, gluon_model_path)
"""
import mxnet as mx
_validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
path = os.path.abspath(path)
if os.path.exists(path):
raise MlflowException("Path '{}' already exists".format(path))
data_subpath = "data"
data_path = os.path.join(path, data_subpath)
os.makedirs(data_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)
# The epoch argument of the export method does not play any role in selecting
# a specific epoch's parameters, and is there only for display purposes.
gluon_model.export(os.path.join(data_path, _MODEL_SAVE_PATH))
pyfunc.add_to_model(mlflow_model, loader_module="mlflow.gluon", env=_CONDA_ENV_FILE_NAME)
mlflow_model.add_flavor(FLAVOR_NAME, mxnet_version=mx.__version__)
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()
inferred_reqs = mlflow.models.infer_pip_requirements(
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))
[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("mxnet")]
[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]@experimental
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="mxnet"))
def log_model(
gluon_model,
artifact_path,
conda_env=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Log a Gluon model as an MLflow artifact for the current run.
:param gluon_model: Gluon model to be saved. Must be already hybridized.
:param artifact_path: Run-relative artifact path.
:param conda_env: {{ conda_env }}
: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 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 pip_requirements: {{ pip_requirements }}
:param extra_pip_requirements: {{ extra_pip_requirements }}
.. code-block:: python
:caption: Example
from mxnet.gluon import Trainer
from mxnet.gluon.contrib import estimator
from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
from mxnet.gluon.nn import HybridSequential
from mxnet.metric import Accuracy
import mlflow
# Build, compile, and train your model
net = HybridSequential()
with net.name_scope():
...
net.hybridize()
net.collect_params().initialize()
softmax_loss = SoftmaxCrossEntropyLoss()
trainer = Trainer(net.collect_params())
est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer)
# Log metrics and log the model
with mlflow.start_run():
est.fit(train_data=train_data, epochs=100, val_data=validation_data)
mlflow.gluon.log_model(net, "model")
"""
Model.log(
artifact_path=artifact_path,
flavor=mlflow.gluon,
gluon_model=gluon_model,
conda_env=conda_env,
registered_model_name=registered_model_name,
signature=signature,
input_example=input_example,
pip_requirements=pip_requirements,
extra_pip_requirements=extra_pip_requirements,
)
[docs]@experimental
@autologging_integration(FLAVOR_NAME)
def autolog(
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 Gluon to MLflow.
Logs loss and any other metrics specified in the fit
function, and optimizer data as parameters. Model checkpoints
are logged as artifacts to a 'models' directory.
: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 MXNet Gluon autologging integration. If ``False``,
enables the MXNet Gluon 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
gluon 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 MXNet Gluon
autologging. If ``False``, show all events and warnings during MXNet Gluon
autologging.
"""
from mxnet.gluon.contrib.estimator import Estimator, EpochEnd, TrainBegin, TrainEnd
from mxnet.gluon.nn import HybridSequential
def getGluonCallback(metrics_logger):
class __MLflowGluonCallback(EpochEnd, TrainEnd, TrainBegin, metaclass=ExceptionSafeClass):
def __init__(self):
self.current_epoch = 0
def epoch_end(self, estimator, *args, **kwargs):
logs = {}
for metric in estimator.train_metrics:
metric_name, metric_val = metric.get()
logs[metric_name] = metric_val
for metric in estimator.val_metrics:
metric_name, metric_val = metric.get()
logs[metric_name] = metric_val
metrics_logger.record_metrics(logs, self.current_epoch)
self.current_epoch += 1
def train_begin(self, estimator, *args, **kwargs):
try_mlflow_log(mlflow.log_param, "num_layers", len(estimator.net))
if estimator.max_epoch is not None:
try_mlflow_log(mlflow.log_param, "epochs", estimator.max_epoch)
if estimator.max_batch is not None:
try_mlflow_log(mlflow.log_param, "batches", estimator.max_batch)
try_mlflow_log(
mlflow.log_param, "optimizer_name", type(estimator.trainer.optimizer).__name__
)
if hasattr(estimator.trainer.optimizer, "lr"):
try_mlflow_log(
mlflow.log_param, "learning_rate", estimator.trainer.optimizer.lr
)
if hasattr(estimator.trainer.optimizer, "epsilon"):
try_mlflow_log(mlflow.log_param, "epsilon", estimator.trainer.optimizer.epsilon)
def train_end(self, estimator, *args, **kwargs):
if isinstance(estimator.net, HybridSequential) and log_models:
try_mlflow_log(log_model, estimator.net, artifact_path="model")
return __MLflowGluonCallback()
def fit(original, self, *args, **kwargs):
# Wrap `fit` execution within a batch metrics logger context.
run_id = mlflow.active_run().info.run_id
with batch_metrics_logger(run_id) as metrics_logger:
mlflowGluonCallback = getGluonCallback(metrics_logger)
if len(args) >= 4:
l = list(args)
l[3] += [mlflowGluonCallback]
args = tuple(l)
elif "event_handlers" in kwargs:
kwargs["event_handlers"] += [mlflowGluonCallback]
else:
kwargs["event_handlers"] = [mlflowGluonCallback]
result = original(self, *args, **kwargs)
return result
safe_patch(FLAVOR_NAME, Estimator, "fit", fit, manage_run=True)