Parallel Learning Guide
=======================

This guide describes distributed learning in LightGBM. Distributed learning allows the use of multiple machines to produce a single model.

Follow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first.

**List of external libraries in which LightGBM can be used in a distributed fashion**

-  `Dask API of LightGBM <./Python-API.rst#dask-api>`__ (formerly it was a separate package) allows to create ML workflow on Dask distributed data structures.

-  `MMLSpark`_ integrates LightGBM into Apache Spark ecosystem.
   `The following example`_ demonstrates how easy it's possible to utilize the great power of Spark.

-  `Kubeflow Fairing`_ suggests using LightGBM in a Kubernetes cluster.
   `These examples`_ help to get started with LightGBM in a hybrid cloud environment.
   Also you can use `Kubeflow XGBoost Operator`_ to train LightGBM model.
   Please check `this example`_ for how to do this.

Choose Appropriate Parallel Algorithm
-------------------------------------

LightGBM provides 3 distributed learning algorithms now.

+--------------------+---------------------------+
| Parallel Algorithm | How to Use                |
+====================+===========================+
| Data parallel      | ``tree_learner=data``     |
+--------------------+---------------------------+
| Feature parallel   | ``tree_learner=feature``  |
+--------------------+---------------------------+
| Voting parallel    | ``tree_learner=voting``   |
+--------------------+---------------------------+

These algorithms are suited for different scenarios, which is listed in the following table:

+-------------------------+-------------------+-----------------+
|                         | #data is small    | #data is large  |
+=========================+===================+=================+
| **#feature is small**   | Feature Parallel  | Data Parallel   |
+-------------------------+-------------------+-----------------+
| **#feature is large**   | Feature Parallel  | Voting Parallel |
+-------------------------+-------------------+-----------------+

More details about these parallel algorithms can be found in `optimization in parallel learning <./Features.rst#optimization-in-parallel-learning>`__.

Build Parallel Version
----------------------

Default build version support parallel learning based on the socket.

If you need to build parallel version with MPI support, please refer to `Installation Guide <./Installation-Guide.rst#build-mpi-version>`__.

Preparation
-----------

Socket Version
^^^^^^^^^^^^^^

It needs to collect IP of all machines that want to run distributed learning in and allocate one TCP port (assume 12345 here) for all machines,
and change firewall rules to allow income of this port (12345). Then write these IP and ports in one file (assume ``mlist.txt``), like following:

.. code::

    machine1_ip 12345
    machine2_ip 12345

MPI Version
^^^^^^^^^^^

It needs to collect IP (or hostname) of all machines that want to run distributed learning in.
Then write these IP in one file (assume ``mlist.txt``) like following:

.. code::

    machine1_ip
    machine2_ip

**Note**: For Windows users, need to start "smpd" to start MPI service. More details can be found `here`_.

Run Parallel Learning
---------------------

Socket Version
^^^^^^^^^^^^^^

1. Edit following parameters in config file:

   ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here.

   ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here.

   ``machine_list_file=mlist.txt``, ``mlist.txt`` is created in `Preparation section <#preparation>`__.

   ``local_listen_port=12345``, ``12345`` is allocated in `Preparation section <#preparation>`__.

2. Copy data file, executable file, config file and ``mlist.txt`` to all machines.

3. Run following command on all machines, you need to change ``your_config_file`` to real config file.

   For Windows: ``lightgbm.exe config=your_config_file``

   For Linux: ``./lightgbm config=your_config_file``

MPI Version
^^^^^^^^^^^

1. Edit following parameters in config file:

   ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here.

   ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here.

2. Copy data file, executable file, config file and ``mlist.txt`` to all machines.

   **Note**: MPI needs to be run in the **same path on all machines**.

3. Run following command on one machine (not need to run on all machines), need to change ``your_config_file`` to real config file.

   For Windows:
   
   .. code::

       mpiexec.exe /machinefile mlist.txt lightgbm.exe config=your_config_file

   For Linux:

   .. code::

       mpiexec --machinefile mlist.txt ./lightgbm config=your_config_file

Example
^^^^^^^

-  `A simple distributed learning example`_

.. _MMLSpark: https://aka.ms/spark

.. _The following example: https://github.com/Azure/mmlspark/blob/master/notebooks/samples/LightGBM%20-%20Quantile%20Regression%20for%20Drug%20Discovery.ipynb

.. _Kubeflow Fairing: https://www.kubeflow.org/docs/components/fairing/fairing-overview

.. _These examples: https://github.com/kubeflow/fairing/tree/master/examples/lightgbm

.. _Kubeflow XGBoost Operator: https://github.com/kubeflow/xgboost-operator

.. _this example: https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/lightgbm-dist

.. _here: https://www.youtube.com/watch?v=iqzXhp5TxUY

.. _A simple distributed learning example: https://github.com/microsoft/lightgbm/tree/master/examples/parallel_learning
