#############################
Distributed XGBoost with Dask
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`Dask <https://dask.org>`_ is a parallel computing library built on Python. Dask allows
easy management of distributed workers and excels at handling large distributed data science
workflows.  The implementation in XGBoost originates from `dask-xgboost
<https://github.com/dask/dask-xgboost>`_ with some extended functionalities and a
different interface.  Right now it is still under construction and may change (with proper
warnings) in the future.  The tutorial here focuses on basic usage of dask with CPU tree
algorithms.  For an overview of GPU based training and internal workings, see `A New,
Official Dask API for XGBoost
<https://medium.com/rapids-ai/a-new-official-dask-api-for-xgboost-e8b10f3d1eb7>`_.

**Contents**

.. contents::
  :backlinks: none
  :local:

************
Requirements
************

Dask can be installed using either pip or conda (see the dask `installation
documentation <https://docs.dask.org/en/latest/install.html>`_ for more information).  For
accelerating XGBoost with GPUs, `dask-cuda <https://github.com/rapidsai/dask-cuda>`_ is
recommended for creating GPU clusters.


********
Overview
********

A dask cluster consists of three different components: a centralized scheduler, one or
more workers, and one or more clients which act as the user-facing entry point for submitting
tasks to the cluster.  When using XGBoost with dask, one needs to call the XGBoost dask interface
from the client side.  Below is a small example which illustrates basic usage of running XGBoost
on a dask cluster:

.. code-block:: python

  import xgboost as xgb
  import dask.distributed

  cluster = dask.distributed.LocalCluster(n_workers=4, threads_per_worker=1)
  client = dask.distributed.Client(cluster)

  dtrain = xgb.dask.DaskDMatrix(client, X, y)  # X and y are dask dataframes or arrays

  output = xgb.dask.train(client,
                          {'verbosity': 2,
                           'tree_method': 'hist'},
                          dtrain,
                          num_boost_round=4, evals=[(dtrain, 'train')])

Here we first create a cluster in single-node mode with ``dask.distributed.LocalCluster``, then
connect a ``dask.distributed.Client`` to this cluster, setting up an environment for later computation.

We then create a ``DMatrix`` object and pass it to ``train``, along with some other parameters,
much like XGBoost's normal, non-dask interface. The primary difference with XGBoost's dask interface is
we pass our dask client as an additional argument for carrying out the computation. Note that if
client is set to ``None``, XGBoost will use the default client returned by dask.

There are two sets of APIs implemented in XGBoost.  The first set is functional API
illustrated in above example.  Given the data and a set of parameters, the ``train`` function
returns a model and the computation history as a Python dictionary:

.. code-block:: python

  {'booster': Booster,
   'history': dict}

For prediction, pass the ``output`` returned by ``train`` into ``xgb.dask.predict``:

.. code-block:: python

  prediction = xgb.dask.predict(client, output, dtrain)

Or equivalently, pass ``output['booster']``:

.. code-block:: python

  prediction = xgb.dask.predict(client, output['booster'], dtrain)

Here ``prediction`` is a dask ``Array`` object containing predictions from model.

Alternatively, XGBoost also implements the Scikit-Learn interface with ``DaskXGBClassifier``
and ``DaskXGBRegressor``. See ``xgboost/demo/dask`` for more examples.

*******
Threads
*******

XGBoost has built in support for parallel computation through threads by the setting
``nthread`` parameter (``n_jobs`` for scikit-learn).  If these parameters are set, they
will override the configuration in Dask.  For example:

.. code-block:: python

  with dask.distributed.LocalCluster(n_workers=7, threads_per_worker=4) as cluster:

There are 4 threads allocated for each dask worker.  Then by default XGBoost will use 4
threads in each process for both training and prediction.  But if ``nthread`` parameter is
set:

.. code-block:: python

  output = xgb.dask.train(client,
                          {'verbosity': 1,
                           'nthread': 8,
                           'tree_method': 'hist'},
                          dtrain,
                          num_boost_round=4, evals=[(dtrain, 'train')])

XGBoost will use 8 threads in each training process.

********************
Working with asyncio
********************

.. versionadded:: 1.2.0

XGBoost's dask interface supports the new ``asyncio`` in Python and can be integrated into
asynchronous workflows.  For using dask with asynchronous operations, please refer to
`this dask example <https://examples.dask.org/applications/async-await.html>`_ and document in
`distributed <https://distributed.dask.org/en/latest/asynchronous.html>`_. To use XGBoost's
dask interface asynchronously, the ``client`` which is passed as an argument for training and
prediction must be operating in asynchronous mode by specifying ``asynchronous=True`` when the
``client`` is created (example below). All functions (including ``DaskDMatrix``) provided
by the functional interface will then return coroutines which can then be awaited to retrieve
their result.

Functional interface:

.. code-block:: python

    async with dask.distributed.Client(scheduler_address, asynchronous=True) as client:
        X, y = generate_array()
        m = await xgb.dask.DaskDMatrix(client, X, y)
        output = await xgb.dask.train(client, {}, dtrain=m)

        with_m = await xgb.dask.predict(client, output, m)
        with_X = await xgb.dask.predict(client, output, X)
        inplace = await xgb.dask.inplace_predict(client, output, X)

        # Use `client.compute` instead of the `compute` method from dask collection
        print(await client.compute(with_m))


While for the Scikit-Learn interface, trivial methods like ``set_params`` and accessing class
attributes like ``evals_result_`` do not require ``await``.  Other methods involving
actual computation will return a coroutine and hence require awaiting:

.. code-block:: python

    async with dask.distributed.Client(scheduler_address, asynchronous=True) as client:
        X, y = generate_array()
        regressor = await xgb.dask.DaskXGBRegressor(verbosity=1, n_estimators=2)
        regressor.set_params(tree_method='hist')  # trivial method, synchronous operation
        regressor.client = client  #  accessing attribute, synchronous operation
        regressor = await regressor.fit(X, y, eval_set=[(X, y)])
        prediction = await regressor.predict(X)

        # Use `client.compute` instead of the `compute` method from dask collection
        print(await client.compute(prediction))

Be careful that XGBoost uses all the workers supplied by the ``client`` object.  If you
are training on GPU cluster and have 2 GPUs, the client object passed to XGBoost should
return 2 workers.

*****************************************************************************
Why is the initialization of ``DaskDMatrix``  so slow and throws weird errors
*****************************************************************************

The dask API in XGBoost requires construction of ``DaskDMatrix``.  With the Scikit-Learn
interface, ``DaskDMatrix`` is implicitly constructed for all input data during the ``fit`` or
``predict`` steps.  You might have observed that ``DaskDMatrix`` construction can take large amounts of time,
and sometimes throws errors that don't seem to be relevant to ``DaskDMatrix``.  Here is a
brief explanation for why.  By default most dask computations are `lazily evaluated
<https://docs.dask.org/en/latest/user-interfaces.html#laziness-and-computing>`_, which
means that computation is not carried out until you explicitly ask for a result by, for example,
calling ``compute()``.  See the previous link for details in dask, and `this wiki
<https://en.wikipedia.org/wiki/Lazy_evaluation>`_ for information on the general concept of lazy evaluation.
The ``DaskDMatrix`` constructor forces lazy computations to be evaluated, which means it's
where all your earlier computation actually being carried out, including operations like
``dd.read_csv()``.  To isolate the computation in ``DaskDMatrix`` from other lazy
computations, one can explicitly wait for results of input data before constructing a ``DaskDMatrix``.
Also dask's `diagnostics dashboard <https://distributed.dask.org/en/latest/web.html>`_ can be used to
monitor what operations are currently being performed.

***********
Limitations
***********

Basic functionality including model training and generating classification and regression predictions
have been implemented.  However, there are still some other limitations we haven't
addressed yet:

- Label encoding for the ``DaskXGBClassifier`` classifier may not be supported.  So users need
  to encode their training labels into discrete values first.
- Ranking is not yet supported.
- Empty worker is not well supported by classifier.  If the training hangs for classifier
  with a warning about empty DMatrix, please consider balancing your data first.  But
  regressor works fine with empty DMatrix.
- Callback functions are not tested.
- Only ``GridSearchCV`` from Scikit-Learn is supported.  Meaning that we can distribute data
  among workers but have to train one model at a time.  If you want to scale up grid searching with
  model parallelism with `Dask-ML <https://ml.dask.org/>`_, please consider using XGBoost's non-dask
  Scikit-Learn interface, for example ``xgboost.XGBRegressor``, for now.
