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,