BigMLFlow¶

All the resources generated by the BigML API-first platform, including models, are totally white-box, and they can be downloaded as JSON and used to predict anywhere.

On the other hand, MLFlow offers tracking and deploying capacities for a variety of ML models as long a flavor is created to define how to log, save and load those models to be actionable.

The bigmlflow library implements this flavor. It uses BigML’s Python bindings to integrate the BigML models with MLFlow’s tracking and deploying capacities.

Installation¶

This library is available as a PyPI package. To install it, just run:

pip install bigmlflow

The flavor is implemented in a single bigmlflow module

Flavor methods¶

The bigmlflow.bigml module provides an API for logging and loading BigML models. This module exports BigML models with the following flavors:

BigML (native) format: This is the main flavor that can be loaded back into BigML.
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and batch inference.

bigmlflow.bigml.get_default_conda_env()¶

Returns: The default Conda environment for MLflow Models produced by calls to save_model() and log_model().

bigmlflow.bigml.get_default_pip_requirements()¶

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

bigmlflow.bigml.load_model(model_uri, dst_path=None)¶

Load a BigML model from a local file (if run_id is None) 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
- models:/<model_name>/<model_version>
- models:/<model_name>/<stage>
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 unspecified, a local output path will be created.

Returns

A SupervisedModel model object.

bigmlflow.bigml.log_model(bigml_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] = None, pip_requirements=None, extra_pip_requirements=None, **kwargs)¶

Log a BigML model as an MLflow artifact for the current run.

Parameters

bigml_model – BigML model to be saved.
artifact_path – Run-relative artifact path.
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 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=3.7.0",
        {
            "pip": [
                "bigml==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 – 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 –
ModelSignature 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.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
```
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 will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["bigml", "-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.
kwargs – kwargs to pass to bigml_model save model method, if any.

Returns

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

bigmlflow.bigml.save_model(bigml_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] = None, pip_requirements=None, extra_pip_requirements=None)¶

Save an BigML model to a path on the local file system.

Parameters

bigml_model – BigML model to be saved.
path – Local path where the 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 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=3.7.0",
        {
            "pip": [
                "bigml==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.
signature –
ModelSignature 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.signature import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
```
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 will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
mlflow_model – mlflow.models.Model this flavor is being added to.

BigMLFlow usage¶

Some examples are available in the repository to illustrate how you can use MLFlow to generate BigML models, log evaluation metrics and deploy the different Supervised Models available in the BigML platform.

Tests¶

The tests directory contains some tests for the logging, saving and loading of models. We use Pytest to run the tests, so you can install it separately

pip install pytest

or as an extra for development and testing purposes

pip install -e .[tests]

How to Contribute¶

Please follow the next steps:

Fork the project on github.com.

Create a new branch.

Commit changes to the new branch.

Send a pull request.