import json
import logging
import os
import shutil
import uuid
import warnings
from datetime import datetime
from pathlib import Path
from pprint import pformat
from typing import Any, Callable, Dict, List, Literal, NamedTuple, Optional, Union
import yaml
import mlflow
from mlflow.artifacts import download_artifacts
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository
from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.tracking._tracking_service.utils import _resolve_tracking_uri
from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _upload_artifact_to_uri
from mlflow.utils.annotations import experimental
from mlflow.utils.databricks_utils import get_databricks_runtime
from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
from mlflow.utils.environment import (
_CONDA_ENV_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_add_or_overwrite_requirements,
_get_requirements_from_file,
_remove_requirements,
_write_requirements_to_file,
)
from mlflow.utils.file_utils import TempDir
from mlflow.utils.uri import (
append_to_uri_path,
get_uri_scheme,
)
_logger = logging.getLogger(__name__)
# NOTE: The MLMODEL_FILE_NAME constant is considered @developer_stable
MLMODEL_FILE_NAME = "MLmodel"
_DATABRICKS_FS_LOADER_MODULE = "databricks.feature_store.mlflow_model"
_LOG_MODEL_METADATA_WARNING_TEMPLATE = (
"Logging model metadata to the tracking server has failed. The model artifacts "
"have been logged successfully under %s. Set logging level to DEBUG via "
'`logging.getLogger("mlflow").setLevel(logging.DEBUG)` to see the full traceback.'
)
_LOG_MODEL_MISSING_SIGNATURE_WARNING = (
"Model logged without a signature. Signatures will be required for upcoming model registry "
"features as they validate model inputs and denote the expected schema of model outputs. "
f"Please visit https://www.mlflow.org/docs/{mlflow.__version__.replace('.dev0', '')}/"
"models.html#set-signature-on-logged-model for instructions on setting a model signature on "
"your logged model."
)
# NOTE: The _MLFLOW_VERSION_KEY constant is considered @developer_stable
_MLFLOW_VERSION_KEY = "mlflow_version"
METADATA_FILES = [
MLMODEL_FILE_NAME,
_CONDA_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
]
[docs]class ModelInfo:
"""
The metadata of a logged MLflow Model.
"""
def __init__(
self,
artifact_path: str,
flavors: Dict[str, Any],
model_uri: str,
model_uuid: str,
run_id: str,
saved_input_example_info: Optional[Dict[str, Any]],
signature, # Optional[ModelSignature]
utc_time_created: str,
mlflow_version: str,
signature_dict: Optional[Dict[str, Any]] = None,
metadata: Optional[Dict[str, Any]] = None,
):
self._artifact_path = artifact_path
self._flavors = flavors
self._model_uri = model_uri
self._model_uuid = model_uuid
self._run_id = run_id
self._saved_input_example_info = saved_input_example_info
self._signature_dict = signature_dict
self._signature = signature
self._utc_time_created = utc_time_created
self._mlflow_version = mlflow_version
self._metadata = metadata
@property
def artifact_path(self):
"""
Run relative path identifying the logged model.
:getter: Retrieves the relative path of the logged model.
:type: str
"""
return self._artifact_path
@property
def flavors(self):
"""
A dictionary mapping the flavor name to how to serve
the model as that flavor.
:getter: Gets the mapping for the logged model's flavor that defines parameters used in
serving of the model
:type: Dict[str, str]
.. code-block:: python
:caption: Example flavor mapping for a scikit-learn logged model
{
"python_function": {
"model_path": "model.pkl",
"loader_module": "mlflow.sklearn",
"python_version": "3.8.10",
"env": "conda.yaml",
},
"sklearn": {
"pickled_model": "model.pkl",
"sklearn_version": "0.24.1",
"serialization_format": "cloudpickle",
},
}
"""
return self._flavors
@property
def model_uri(self):
"""
The ``model_uri`` of the logged model in the format ``'runs:/<run_id>/<artifact_path>'``.
:getter: Gets the uri path of the logged model from the uri `runs:/<run_id>` path
encapsulation
:type: str
"""
return self._model_uri
@property
def model_uuid(self):
"""
The ``model_uuid`` of the logged model,
e.g., ``'39ca11813cfc46b09ab83972740b80ca'``.
:getter: [Legacy] Gets the model_uuid (run_id) of a logged model
:type: str
"""
return self._model_uuid
@property
def run_id(self):
"""
The ``run_id`` associated with the logged model,
e.g., ``'8ede7df408dd42ed9fc39019ef7df309'``
:getter: Gets the run_id identifier for the logged model
:type: str
"""
return self._run_id
@property
def saved_input_example_info(self):
"""
A dictionary that contains the metadata of the saved input example, e.g.,
``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``.
:getter: Gets the input example if specified during model logging
:type: Optional[Dict[str, str]]
"""
return self._saved_input_example_info
@property
def signature_dict(self):
"""
A dictionary that describes the model input and output generated by
:py:meth:`ModelSignature.to_dict() <mlflow.models.ModelSignature.to_dict>`.
:getter: Gets the model signature as a dictionary
:type: Optional[Dict[str, Any]]
"""
warnings.warn(
"Field signature_dict is deprecated since v1.28.1. Use signature instead.",
category=FutureWarning,
stacklevel=2,
)
return self._signature_dict
@property
def signature(self): # -> Optional[ModelSignature]
"""
A :py:class:`ModelSignature <mlflow.models.ModelSignature>` that describes the
model input and output.
:getter: Gets the model signature if it is defined
:type: Optional[ModelSignature]
"""
return self._signature
@property
def utc_time_created(self):
"""
The UTC time that the logged model is created, e.g., ``'2022-01-12 05:17:31.634689'``.
:getter: Gets the UTC formatted timestamp for when the model was logged
:type: str
"""
return self._utc_time_created
@property
def mlflow_version(self):
"""
Version of MLflow used to log the model
:getter: Gets the version of MLflow that was installed when a model was logged
:type: str
"""
return self._mlflow_version
@experimental
@property
def metadata(self) -> Optional[Dict[str, Any]]:
"""
User defined metadata added to the model.
:getter: Gets the user-defined metadata about a model
:type: Optional[Dict[str, Any]]
.. code-block:: python
:caption: Example usage of Model Metadata
# Create and log a model with metadata to the Model Registry
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import mlflow
from mlflow.models import infer_signature
with mlflow.start_run():
iris = datasets.load_iris()
clf = RandomForestClassifier()
clf.fit(iris.data, iris.target)
signature = infer_signature(iris.data, iris.target)
mlflow.sklearn.log_model(
clf,
"iris_rf",
signature=signature,
registered_model_name="model-with-metadata",
metadata={"metadata_key": "metadata_value"},
)
# model uri for the above model
model_uri = "models:/model-with-metadata/1"
# Load the model and access the custom metadata from its ModelInfo object
model = mlflow.pyfunc.load_model(model_uri=model_uri)
assert model.metadata.get_model_info().metadata["metadata_key"] == "metadata_value"
# Load the ModelInfo and access the custom metadata
model_info = mlflow.models.get_model_info(model_uri=model_uri)
assert model_info.metadata["metadata_key"] == "metadata_value"
"""
return self._metadata
[docs]class Model:
"""
An MLflow Model that can support multiple model flavors. Provides APIs for implementing
new Model flavors.
"""
def __init__(
self,
artifact_path=None,
run_id=None,
utc_time_created=None,
flavors=None,
signature=None, # ModelSignature
saved_input_example_info: Optional[Dict[str, Any]] = None,
model_uuid: Union[str, Callable, None] = lambda: uuid.uuid4().hex,
mlflow_version: Union[str, None] = mlflow.version.VERSION,
metadata: Optional[Dict[str, Any]] = None,
model_size_bytes: Optional[int] = None,
**kwargs,
):
# store model id instead of run_id and path to avoid confusion when model gets exported
self.run_id = run_id
self.artifact_path = artifact_path
self.utc_time_created = str(utc_time_created or datetime.utcnow())
self.flavors = flavors if flavors is not None else {}
self.signature = signature
self.saved_input_example_info = saved_input_example_info
self.model_uuid = model_uuid() if callable(model_uuid) else model_uuid
self.mlflow_version = mlflow_version
self.metadata = metadata
self.model_size_bytes = model_size_bytes
self.__dict__.update(kwargs)
def __eq__(self, other):
if not isinstance(other, Model):
return False
return self.__dict__ == other.__dict__
[docs] def get_output_schema(self):
"""
Retrieves the output schema of the Model iff the model was saved with a schema definition.
"""
return self.signature.outputs if self.signature is not None else None
[docs] def get_params_schema(self):
"""
Retrieves the parameters schema of the Model iff the model was saved with a schema
definition.
"""
return getattr(self.signature, "params", None)
[docs] def add_flavor(self, name, **params):
"""Add an entry for how to serve the model in a given format."""
self.flavors[name] = params
return self
@experimental
@property
def metadata(self) -> Optional[Dict[str, Any]]:
"""
Custom metadata dictionary passed to the model and stored in the MLmodel file.
:getter: Retrieves custom metadata that have been applied to a model instance.
:setter: Sets a dictionary of custom keys and values to be included with the model instance
:type: Optional[Dict[str, Any]]
Returns:
A Dictionary of user-defined metadata iff defined.
.. code-block:: python
:caption: Example
# Create and log a model with metadata to the Model Registry
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import mlflow
from mlflow.models import infer_signature
with mlflow.start_run():
iris = datasets.load_iris()
clf = RandomForestClassifier()
clf.fit(iris.data, iris.target)
signature = infer_signature(iris.data, iris.target)
mlflow.sklearn.log_model(
clf,
"iris_rf",
signature=signature,
registered_model_name="model-with-metadata",
metadata={"metadata_key": "metadata_value"},
)
# model uri for the above model
model_uri = "models:/model-with-metadata/1"
# Load the model and access the custom metadata
model = mlflow.pyfunc.load_model(model_uri=model_uri)
assert model.metadata.metadata["metadata_key"] == "metadata_value"
"""
return self._metadata
@experimental
@metadata.setter
def metadata(self, value: Optional[Dict[str, Any]]):
self._metadata = value
@property
def signature(self): # -> Optional[ModelSignature]
"""
An optional definition of the expected inputs to and outputs from a model object, defined
with both field names and data types. Signatures support both column-based and tensor-based
inputs and outputs.
:getter: Retrieves the signature of a model instance iff the model was saved with a
signature definition.
:setter: Sets a signature to a model instance.
:type: Optional[ModelSignature]
"""
return self._signature
@signature.setter
def signature(self, value):
# signature cannot be set to `False`, which is used in `log_model` and `save_model` calls
# to disable automatic signature inference
if value is not False:
self._signature = value
@property
def saved_input_example_info(self) -> Optional[Dict[str, Any]]:
"""
A dictionary that contains the metadata of the saved input example, e.g.,
``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``.
"""
return self._saved_input_example_info
@saved_input_example_info.setter
def saved_input_example_info(self, value: Dict[str, Any]):
self._saved_input_example_info = value
@property
def model_size_bytes(self) -> Optional[int]:
"""
An optional integer that represents the model size in bytes
:getter: Retrieves the model size if it's calculated when the model is saved
:setter: Sets the model size to a model instance
:type: Optional[int]
"""
return self._model_size_bytes
@model_size_bytes.setter
def model_size_bytes(self, value: Optional[int]):
self._model_size_bytes = value
[docs] def get_model_info(self):
"""
Create a :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
model metadata.
"""
return ModelInfo(
artifact_path=self.artifact_path,
flavors=self.flavors,
model_uri=f"runs:/{self.run_id}/{self.artifact_path}",
model_uuid=self.model_uuid,
run_id=self.run_id,
saved_input_example_info=self.saved_input_example_info,
signature_dict=self.signature.to_dict() if self.signature else None,
signature=self.signature,
utc_time_created=self.utc_time_created,
mlflow_version=self.mlflow_version,
metadata=self.metadata,
)
[docs] def to_dict(self):
"""Serialize the model to a dictionary."""
res = {k: v for k, v in self.__dict__.items() if not k.startswith("_")}
databricks_runtime = get_databricks_runtime()
if databricks_runtime:
res["databricks_runtime"] = databricks_runtime
if self.signature is not None:
res["signature"] = self.signature.to_dict()
if self.saved_input_example_info is not None:
res["saved_input_example_info"] = self.saved_input_example_info
if self.mlflow_version is None and _MLFLOW_VERSION_KEY in res:
res.pop(_MLFLOW_VERSION_KEY)
if self.metadata is not None:
res["metadata"] = self.metadata
if self.model_size_bytes is not None:
res["model_size_bytes"] = self.model_size_bytes
# Exclude null fields in case MLmodel file consumers such as Model Serving may not
# handle them correctly.
if self.artifact_path is None:
res.pop("artifact_path", None)
if self.run_id is None:
res.pop("run_id", None)
return res
[docs] def to_yaml(self, stream=None):
"""Write the model as yaml string."""
return yaml.safe_dump(self.to_dict(), stream=stream, default_flow_style=False)
def __str__(self):
return self.to_yaml()
[docs] def to_json(self):
"""Write the model as json."""
return json.dumps(self.to_dict())
[docs] def save(self, path):
"""Write the model as a local YAML file."""
with open(path, "w") as out:
self.to_yaml(out)
[docs] @classmethod
def load(cls, path):
"""
Load a model from its YAML representation.
Args:
path: A local filesystem path or URI referring to the MLmodel YAML file
representation of the Model object or to the directory containing
the MLmodel YAML file representation.
Returns:
An instance of Model.
.. code-block:: python
:caption: example
from mlflow.models import Model
# Load the Model object from a local MLmodel file
model1 = Model.load("~/path/to/my/MLmodel")
# Load the Model object from a remote model directory
model2 = Model.load("s3://mybucket/path/to/my/model")
"""
path = download_artifacts(artifact_uri=path)
if os.path.isdir(path):
path = os.path.join(path, MLMODEL_FILE_NAME)
with open(path) as f:
return cls.from_dict(yaml.safe_load(f.read()))
[docs] @classmethod
def from_dict(cls, model_dict):
"""Load a model from its YAML representation."""
from mlflow.models.signature import ModelSignature
model_dict = model_dict.copy()
if "signature" in model_dict and isinstance(model_dict["signature"], dict):
model_dict["signature"] = ModelSignature.from_dict(model_dict["signature"])
if "model_uuid" not in model_dict:
model_dict["model_uuid"] = None
if _MLFLOW_VERSION_KEY not in model_dict:
model_dict[_MLFLOW_VERSION_KEY] = None
return cls(**model_dict)
[docs] @format_docstring(LOG_MODEL_PARAM_DOCS)
@classmethod
def log(
cls,
artifact_path,
flavor,
registered_model_name=None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
metadata=None,
run_id=None,
**kwargs,
):
"""
Log model using supplied flavor module. If no run is active, this method will create a new
active run.
Args:
artifact_path: Run relative path identifying the model.
flavor: Flavor module to save the model with. The module must have
the ``save_model`` function that will persist the model as a valid
MLflow model.
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: {{ signature }}
input_example: {{ input_example }}
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.
metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.
.. Note:: Experimental: This parameter may change or be removed in a
future release without warning.
kwargs: Extra args passed to the model flavor.
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
"""
from mlflow.models.wheeled_model import _ORIGINAL_REQ_FILE_NAME, WheeledModel
with TempDir() as tmp:
local_path = tmp.path("model")
if run_id is None:
run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
mlflow_model = cls(artifact_path=artifact_path, run_id=run_id, metadata=metadata)
flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs)
# Copy model metadata files to a sub-directory 'metadata',
# For UC sharing use-cases.
metadata_path = os.path.join(local_path, "metadata")
if isinstance(flavor, WheeledModel):
# wheeled model updates several metadata files in original model directory
# copy these updated metadata files to the 'metadata' subdirectory
os.makedirs(metadata_path, exist_ok=True)
for file_name in METADATA_FILES + [
_ORIGINAL_REQ_FILE_NAME,
]:
src_file_path = os.path.join(local_path, file_name)
if os.path.exists(src_file_path):
dest_file_path = os.path.join(metadata_path, file_name)
shutil.copyfile(src_file_path, dest_file_path)
else:
os.makedirs(metadata_path, exist_ok=True)
for file_name in METADATA_FILES:
src_file_path = os.path.join(local_path, file_name)
if os.path.exists(src_file_path):
dest_file_path = os.path.join(metadata_path, file_name)
shutil.copyfile(src_file_path, dest_file_path)
tracking_uri = _resolve_tracking_uri()
# We check signature presence here as some flavors have a default signature as a
# fallback when not provided by user, which is set during flavor's save_model() call.
if mlflow_model.signature is None and (
tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks"
):
_logger.warning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)
mlflow.tracking.fluent.log_artifacts(local_path, mlflow_model.artifact_path, run_id)
try:
mlflow.tracking.fluent._record_logged_model(mlflow_model, run_id)
except MlflowException:
# We need to swallow all mlflow exceptions to maintain backwards compatibility with
# older tracking servers. Only print out a warning for now.
_logger.warning(_LOG_MODEL_METADATA_WARNING_TEMPLATE, mlflow.get_artifact_uri())
_logger.debug("", exc_info=True)
if registered_model_name is not None:
mlflow.tracking._model_registry.fluent._register_model(
f"runs:/{run_id}/{mlflow_model.artifact_path}",
registered_model_name,
await_registration_for=await_registration_for,
local_model_path=local_path,
)
return mlflow_model.get_model_info()
[docs]def get_model_info(model_uri: str) -> ModelInfo:
"""
Get metadata for the specified model, such as its input/output signature.
Args:
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>``
- ``mlflow-artifacts:/path/to/model``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
.. code-block:: python
:caption: Example usage of get_model_info
import mlflow.models
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
with mlflow.start_run() as run:
params = {"n_estimators": 3, "random_state": 42}
X, y = [[0, 1]], [1]
signature = mlflow.models.infer_signature(X, y)
rfr = RandomForestRegressor(**params).fit(X, y)
mlflow.log_params(params)
mlflow.sklearn.log_model(rfr, artifact_path="sklearn-model", signature=signature)
model_uri = f"runs:/{run.info.run_id}/sklearn-model"
# Get model info with model_uri
model_info = mlflow.models.get_model_info(model_uri)
# Get model signature directly
model_signature = model_info.signature
assert model_signature == signature
"""
from mlflow.pyfunc import _download_artifact_from_uri
local_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=None)
model_meta = Model.load(os.path.join(local_path, MLMODEL_FILE_NAME))
return ModelInfo(
artifact_path=model_meta.artifact_path,
flavors=model_meta.flavors,
model_uri=model_uri,
model_uuid=model_meta.model_uuid,
run_id=model_meta.run_id,
saved_input_example_info=model_meta.saved_input_example_info,
signature_dict=model_meta.signature.to_dict() if model_meta.signature else None,
signature=model_meta.signature,
utc_time_created=model_meta.utc_time_created,
mlflow_version=model_meta.mlflow_version,
metadata=model_meta.metadata,
)
class Files(NamedTuple):
requirements: Path
conda: Path
def get_model_requirements_files(resolved_uri: str) -> Files:
requirements_txt_file = _download_artifact_from_uri(
artifact_uri=append_to_uri_path(resolved_uri, _REQUIREMENTS_FILE_NAME)
)
conda_yaml_file = _download_artifact_from_uri(
artifact_uri=append_to_uri_path(resolved_uri, _CONDA_ENV_FILE_NAME)
)
return Files(
Path(requirements_txt_file),
Path(conda_yaml_file),
)
def update_model_requirements(
model_uri: str,
operation: Literal["add", "remove"],
requirement_list: List[str],
) -> None:
"""
Add or remove requirements from a model's conda.yaml and requirements.txt files.
The process involves downloading these two files from the model artifacts
(if they're non-local), updating them with the specified requirements,
and then overwriting the existing files. Should the artifact repository
associated with the model artifacts disallow overwriting, this function will
fail.
Note that model registry URIs (i.e. URIs in the form ``models:/``) are not
supported, as artifacts in the model registry are intended to be read-only.
If adding requirements, the function will overwrite any existing requirements
that overlap, or else append the new requirements to the existing list.
If removing requirements, the function will ignore any version specifiers,
and remove all the specified package names. Any requirements that are not
found in the existing files will be ignored.
Args:
model_uri (str): 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``
- ``mlflow-artifacts:/path/to/model``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
operation (Literal["add", "remove]): The operation to perform.
Must be one of "add" or "remove".
requirement_list (List[str]): A list of requirements to add or remove from the model.
For example: ["numpy==1.20.3", "pandas>=1.3.3"]
"""
if ModelsArtifactRepository.is_models_uri(model_uri):
raise MlflowException(
f'Failed to set requirements on "{model_uri}". '
+ "Model URIs with the `models:/` scheme are not supported.",
INVALID_PARAMETER_VALUE,
)
resolved_uri = model_uri
if RunsArtifactRepository.is_runs_uri(model_uri):
resolved_uri = RunsArtifactRepository.get_underlying_uri(model_uri)
_logger.info(f"Retrieving model requirements files from {resolved_uri}...")
local_paths = get_model_requirements_files(resolved_uri)
conda_yaml_path = local_paths.conda
requirements_txt_path = local_paths.requirements
old_conda_reqs = _get_requirements_from_file(conda_yaml_path)
old_requirements_reqs = _get_requirements_from_file(requirements_txt_path)
if operation == "add":
updated_conda_reqs = _add_or_overwrite_requirements(requirement_list, old_conda_reqs)
updated_requirements_reqs = _add_or_overwrite_requirements(
requirement_list, old_requirements_reqs
)
else:
updated_conda_reqs = _remove_requirements(requirement_list, old_conda_reqs)
updated_requirements_reqs = _remove_requirements(requirement_list, old_requirements_reqs)
_write_requirements_to_file(conda_yaml_path, updated_conda_reqs)
_write_requirements_to_file(requirements_txt_path, updated_requirements_reqs)
# just print conda reqs here to avoid log spam
# it should be the same as requirements.txt anyway
_logger.info(
"Done updating requirements!\n\n"
f"Old requirements:\n{pformat([str(req) for req in old_conda_reqs])}\n\n"
f"Updated requirements:\n{pformat(updated_conda_reqs)}\n"
)
_logger.info(f"Uploading updated requirements files to {resolved_uri}...")
_upload_artifact_to_uri(conda_yaml_path, resolved_uri)
_upload_artifact_to_uri(requirements_txt_path, resolved_uri)