def get_default_conda_env():
"""
:return: The default Conda environment for MLflow Models produced by calls to
:func:`save_model()` and :func:`log_model()`. This Conda environment
contains the current version of PySpark that is installed on the caller's
system. ``dev`` versions of PySpark are replaced with stable versions in
the resulting Conda environment (e.g., if you are running PySpark version
``2.4.5.dev0``, invoking this method produces a Conda environment with a
dependency on PySpark version ``2.4.5``).
"""
return _mlflow_conda_env(
additional_pip_deps=get_default_pip_requirements())
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="pyspark"))
def log_model(
spark_model,
artifact_path,
conda_env=None,
dfs_tmpdir=None,
sample_input=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requirements=None,
extra_pip_requirements=None,
):
"""
Log a Spark MLlib model as an MLflow artifact for the current run. This uses the
Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment
that, at minimum, contains these requirements.
"""
return [_get_pinned_requirement("catboost")]
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())
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
def save_model(cb_model,
path,
conda_env=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
**kwargs):
"""
Save a CatBoost model to a path on the local file system.
:param cb_model: CatBoost model (an instance of `CatBoost`_, `CatBoostClassifier`_,
or `CatBoostRegressor`_) to be saved.
:param path: Local path where the model is to be saved.
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',
'mlflow',
'cloudpickle==1.6.0']}]}
"""
return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="torch"))
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,
pip_requirements=None,
extra_pip_requirements=None,
**kwargs,