.. _sphx_glr_tutorials_layer.py: .. _layer_tutorial: .. currentmodule:: mlens.parallel Layer Mechanics =============== ML-Ensemble is designed to provide an easy user interface. But it is also designed to be extremely flexible, all the wile providing maximum concurrency at minimal memory consumption. The lower-level API that builds the ensemble and manages the computations is constructed in as modular a fashion as possible. The low-level API introduces a computational graph-like environment that you can directly exploit to gain further control over your ensemble. In fact, building your ensemble through the low-level API is almost as straight forward as using the high-level API. In this tutorial, we will walk through how to use the :class:`Group` and :class:`Layer` classes to fit several learners. Suppose we want to fit several learners. The :ref:` learner tutorial ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: layer.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_