BigMLer - A command-line tool for BigML’s API

BigMLer makes BigML even easier.

BigMLer wraps BigML’s API Python bindings to offer a high-level command-line script to easily create and publish datasets and models, create ensembles, make local predictions from multiple models, clusters and simplify many other machine learning tasks.

BigMLer is open sourced under the Apache License, Version 2.0.


BigMLer needs Python 3.8 or higher versions to work. Compatibility with Python 2.X was discontinued in version 3.27.2.

BigMLer requires bigml 9.7.1 or higher, that contains the bindings providing support to use the BigML platform to create, update, get and delete resources, but also to produce local predictions using the models created in BigML. Most of them will be actionable with the basic installation, but some additional dependencies are needed to use local Topic Models to produce Topic Distributions. These can be installed using:

pip install bigmler[topics]

The bindings also support local predictions for models generated from images. To use these models, an additional set of libraries needs to be installed using:

pip install bigmler[images]

The external libraries used in this case exist for the majority of recent Operating System versions. Still, some of them might need especific compiler versions or dlls, so their installation may require an additional setup effort.

The full set of libraries can be installed using

pip install bigmler[full]

BigMLer Installation

To install the latest stable release with pip

$ pip install bigmler

You can also install the development version of bigmler directly from the Git repository

$ pip install -e git://

For a detailed description of install instructions on Windows see the :ref:bigmler-windows section.

Support for local Topic Distributions (Topic Models’ predictions) and local predictions for datasets that include Images will only be available as extras, because the libraries used for that are not usually available in all Operating Systems. If you need to support those, please check the Installation Extras section.

Installation Extras

Local Topic Distributions support can be installed using:

pip install bigmler[topics]

Images local predictions support can be installed using:

pip install bigmler[images]

The full set of features can be installed using:

pip install bigmler[full]

WARNING: Mind that installing these extras can require some extra work, as explained in the Requirements section.

BigML Authentication on Unix or Mac OS

All the requests to must be authenticated using your username and API key and are always transmitted over HTTPS.

BigML module will look for your username and API key in the environment variables BIGML_USERNAME and BIGML_API_KEY respectively. You can add the following lines to your .bashrc or .bash_profile to set those variables automatically when you log in

export BIGML_USERNAME=myusername
export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

Otherwise, you can initialize directly when running the BigMLer script as follows

bigmler --train data/iris.csv --username myusername \
        --api-key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

For a detailed description of authentication instructions on Windows see the :ref:bigmler-windows section.

BigMLer Install and Authentication on Windows

To install BigMLer on Windows environments, you’ll need Python installed. The code has been tested with Python 3.10 and you can create a conda environment with that Python version or download it from Python for Windows and install it. In the latter case, you’ll also need too install the pip tool to install BigMLer.

To install pip, first you need to open your command terminal window (write cmd in the input field that appears when you click on Start and hit enter). Then you can follow the steps described, for example, in this guide to install its latest version.

And finally, to install BigMLer in its basic capacities, just type

python -m pip install bigmler

and BigMLer should be installed in your computer or conda environment. Then issuing

bigmler --version

should show BigMLer version information.

Extensions of BigMLer to use images are usually not available in Windows. The libraries needed for those models are not available usually for that operating system. If your Machine Learning project involves images, we recommend that you choose a Linux based operating system.

Finally, to start using BigMLer to handle your BigML resources, you need to set your credentials in BigML for authentication. If you want them to be permanently stored in your system, use

setx BIGML_USERNAME myusername
setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

Note that setx will not change the environment variables of your actual console, so you will need to open a new one to start using them.

Prior Versions Compatibility Issues

BigMLer will accept flags written with underscore as word separator like --clear_logs for compatibility with prior versions. Also --field-names is accepted, although the more complete --field-attributes flag is preferred. --stat_pruning and --no_stat_pruning are discontinued and their effects can be achived by setting the actual --pruning flag to statistical or no-pruning values respectively.

Running the Tests

The tests will be run using pytest. You’ll need to set up your authentication via environment variables, as explained in the authentication section. Also some of the tests need other environment variables like BIGML_ORGANIZATION to test calls when used by Organization members and BIGML_EXTERNAL_CONN_HOST, BIGML_EXTERNAL_CONN_PORT, BIGML_EXTERNAL_CONN_DB, BIGML_EXTERNAL_CONN_USER, BIGML_EXTERNAL_CONN_PWD and BIGML_EXTERNAL_CONN_SOURCE in order to test external data connectors.

With that in place, you can run the test suite simply by issuing

$ pytest

BigMLer subcommands

In addition to the BigMLer simple command, that covers the main functionality, there are some additional subcommands:

Usual workflows’ subcommands

bigmler connector:

Used to generate external connectors to databases. See Connector subcommand.

bigmler source:

Used to generate sources from data files. See Source subcommand.

bigmler dataset:

Used to generate datasets from data files, sources and transformations on other datasets See Dataset subcommand.

bigmler cluster:

Used to generate clusters and centroids’ predictions See Cluster subcommand.

bigmler anomaly:

Used to generate anomaly detectors and anomaly scores. See Anomaly subcommand.

bigmler sample:

Used to generate samples of data from your existing datasets. See Sample subcommand.

bigmler association:

Used to generate association rules from your datasets. See Association subcommand.

bigmler logistic-regression:

Used to generate logistic regression models and predictions. See Logistic-regression subcommand.

bigmler linear-regression:

Used to generate linear regression models and predictions. See Linear-regression subcommand.

bigmler topic-model:

Used to generate topic models and topic distributions. See Topic Model subcommand.

bigmler time-series:

Used to generate time series and forecasts. See Time Series subcommand.

bigmler deepnet:

Used to generate deepnets and their predictions. See Deepnet subcommand.

bigmler fusion:

Used to generate fusions and their predictions. See Fusion subcommand.

bigmler pca:

Used to generate PCAs and their projections. See PCA subcommand.

bigmler project:

Used to generate and manage projects for organization purposes. See Project subcommand.

Management subcommands

bigmler delete:

Used to delete the remotely created resources. See Delete subcommand.


Used to generate the code you need to predict locally with no connection to BigML. See Export subcommand.

Reporting subcommands

bigmler report:

Used to generate reports for the analyze subcommand showing the ROC curve and evaluation metrics of cross-validations. See Report subcommand.

Model tuning subcommands

bigmler analyze:

Used for feature analysis, node threshold analysis and k-fold cross-validation. See Analyze subcommand.

Scripting subcommands

bigmler reify:

Used to generate scripts to reproduce the existing resources in BigML. See Reify subcommand.

bigmler execute:

Used to create WhizzML libraries or scripts and execute them. See Execute subcommand.

bigmler whizzml:

Used to create WhizzML packages of libraries or scripts based on the information of the metadata.json file in the package directory. See Whizzml subcommand

bigmler retrain:

Used to retrain models by adding new data to the existing datasets and building a new model from it. See Retrain subcommand

BigML Development Mode

The Sandbox environment that could be reached by using the flag --dev has been deprecated and. Right now, there’s only one mode to work with BigML: the previous Production Model, so the flag is no longer available.

Using BigMLer

To run BigMLer you can use the console script directly. The --help option will describe all the available options

bigmler --help

Alternatively you can just call bigmler as follows

python --help

This will display the full list of optional arguments. You can read a brief explanation for each option below.

Building the Documentation

Install the tools required to build the documentation

$ pip install sphinx
$ pip install sphinx-rtd-theme

To build the HTML version of the documentation

$ cd docs/
$ make html

Then launch docs/_build/html/index.html in your browser.

Additional Information

For additional information, see the full documentation for the Python bindings on Read the Docs. For more information about BigML’s API, see the BigML developer’s documentation.


Please report problems and bugs to our issue tracker.

Discussions about the different bindings take place in the general BigML mailing list.

How to Contribute

Please follow the next steps:

  1. Fork the project on github.

  2. Create a new branch.

  3. Commit changes to the new branch.

  4. Send a pull request.

For details on the underlying API, see the BigML API documentation.