mlflow.diviner

The mlflow.diviner module provides an API for logging, saving and loading diviner models. Diviner wraps several popular open source time series forecasting libraries in a unified API that permits training, back-testing cross validation, and forecasting inference for groups of related series. This module exports groups of univariate diviner models in the following formats:

Diviner format: Serialized instance of a diviner model type using native diviner serializers. (e.g., “GroupedProphet” or “GroupedPmdarima”)
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and for batch auditing of historical forecasts.

mlflow.diviner.get_default_conda_env()[source]

Returns: The default Conda environment for MLflow Models produced with the Diviner flavor that is produced by calls to save_model() and log_model().

mlflow.diviner.get_default_pip_requirements()[source]

Returns: A list of default pip requirements for MLflow Models produced with the Diviner flavor. Calls to save_model() and log_model() produce a pip environment that, at a minimum, contains these requirements.

mlflow.diviner.load_model(model_uri, dst_path=None, **kwargs)[source]

Load a Diviner object from a local file or a run.

Parameters

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
- mlflow-artifacts:/path/to/model
For more information about supported URI schemes, see Referencing Artifacts.
dst_path – The local filesystem path to which to download the model artifact. This directory must already exist if provided. If unspecified, a local output path will be created.
kwargs – Optional configuration options for loading of a Diviner model. For models that have been fit and saved using Spark, if a specific DFS temporary directory is desired for loading of Diviner models, use the keyword argument “dfs_tmpdir” to define the loading temporary path for the model during loading.

Returns

A Diviner model instance.

mlflow.diviner.log_model(diviner_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None, **kwargs)[source]

Log a Diviner object as an MLflow artifact for the current run.

Parameters

diviner_model – Diviner model that has been fit on a grouped temporal DataFrame.
artifact_path – Run-relative artifact path to save the model instance to.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "diviner==x.y.z"
            ],
        },
    ],
}
```
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.
registered_model_name – This argument may change or be removed in a future release without warning. 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 –

Model Signature describes model input and output Schema. The model signature can be inferred 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:

Example

from mlflow.models import infer_signature

auto_arima_obj = AutoARIMA(out_of_sample_size=60, maxiter=100)
base_auto_arima = GroupedPmdarima(model_template=auto_arima_obj).fit(
    df=training_data,
    group_key_columns=("region", "state"),
    y_col="y",
    datetime_col="ds",
    silence_warnings=True,
)
predictions = model.predict(n_periods=30, alpha=0.05, return_conf_int=True)
signature = infer_signature(data, predictions)

input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
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.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["diviner", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
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 – Additional arguments for mlflow.models.model.Model Additionally, for models that have been fit in Spark, the following supported configuration options are available to set. Current supported options: - partition_by for setting a (or several) partition columns as a list of column names. Must be a list of strings of grouping key column(s). - partition_count for setting the number of part files to write from a repartition per partition_by group. The default part file count is 200. - dfs_tmpdir for specifying the DFS temporary location where the model will be stored while copying from a local file system to a Spark-supported “dbfs:/” scheme.

Returns

A ModelInfo instance that contains the metadata of the logged model.

mlflow.diviner.save_model(diviner_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None, **kwargs)[source]

Save a Diviner model object to a path on the local file system.

Parameters

diviner_model – Diviner model that has been fit on a grouped temporal DataFrame.
path – Local path destination for the serialized model is to be saved.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "diviner==x.y.z"
            ],
        },
    ],
}
```
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.
mlflow_model – mlflow.models.Model the flavor that this model is being added to.
signature –
Model Signature describes model input and output Schema. The model signature can be inferred 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:
```
from mlflow.models import infer_signature

model = diviner.GroupedProphet().fit(data, ("region", "state"))
predictions = model.predict(prediction_config)
signature = infer_signature(data, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then 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. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["diviner", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
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 – Optional configurations for Spark DataFrame storage iff the model has been fit in Spark. Current supported options: - partition_by for setting a (or several) partition columns as a list of column names. Must be a list of strings of grouping key column(s). - partition_count for setting the number of part files to write from a repartition per partition_by group. The default part file count is 200. - dfs_tmpdir for specifying the DFS temporary location where the model will be stored while copying from a local file system to a Spark-supported “dbfs:/” scheme.