.. _train-lightgbm:

Get Started with Distributed Training using LightGBM
====================================================

This tutorial walks through the process of converting an existing LightGBM script to use Ray Train.

Learn how to:

1. Configure a :ref:`training function <train-overview-training-function>` to report metrics and save checkpoints.
2. Configure :ref:`scaling <train-overview-scaling-config>` and CPU or GPU resource requirements for a training job.
3. Launch a distributed training job with a :class:`~ray.train.lightgbm.LightGBMTrainer`.

Quickstart
----------

For reference, the final code will look something like this:

.. testcode::
    :skipif: True

    import ray.train
    from ray.train.lightgbm import LightGBMTrainer

    def train_func():
        # Your LightGBM training code here.
        ...

    scaling_config = ray.train.ScalingConfig(num_workers=2, resources_per_worker={"CPU": 4})
    trainer = LightGBMTrainer(train_func, scaling_config=scaling_config)
    result = trainer.fit()

1. `train_func` is the Python code that executes on each distributed training worker.
2. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
3. :class:`~ray.train.lightgbm.LightGBMTrainer` launches the distributed training job.

Compare a LightGBM training script with and without Ray Train.

.. tab-set::

    .. tab-item:: LightGBM + Ray Train

        .. literalinclude:: ./doc_code/lightgbm_quickstart.py
            :language: python
            :start-after: __lightgbm_ray_start__
            :end-before: __lightgbm_ray_end__

    .. tab-item:: LightGBM

        .. literalinclude:: ./doc_code/lightgbm_quickstart.py
            :language: python
            :start-after: __lightgbm_start__
            :end-before: __lightgbm_end__

Set up a training function
--------------------------

First, update your training code to support distributed training.
Begin by wrapping your `native <https://lightgbm.readthedocs.io/en/latest/Python-Intro.html>`_ 
or `scikit-learn estimator <https://lightgbm.readthedocs.io/en/latest/Python-API.html#scikit-learn-api>`_ 
LightGBM training code in a :ref:`training function <train-overview-training-function>`:

.. testcode::
    :skipif: True

    def train_func():
        # Your native LightGBM training code here.
        train_set = ...
        lightgbm.train(...)

Each distributed training worker executes this function.

You can also specify the input argument for `train_func` as a dictionary via the Trainer's `train_loop_config`. For example:

.. testcode:: python
    :skipif: True

    def train_func(config):
        label_column = config["label_column"]
        num_boost_round = config["num_boost_round"]
        ...

    config = {"label_column": "target", "num_boost_round": 100}
    trainer = ray.train.lightgbm.LightGBMTrainer(train_func, train_loop_config=config, ...)

.. warning::

    Avoid passing large data objects through `train_loop_config` to reduce the
    serialization and deserialization overhead. Instead,
    initialize large objects (e.g. datasets, models) directly in `train_func`.

    .. code-block:: diff

         def load_dataset():
             # Return a large in-memory dataset
             ...

         def load_model():
             # Return a large in-memory model instance
             ...

        -config = {"data": load_dataset(), "model": load_model()}

         def train_func(config):
        -    data = config["data"]
        -    model = config["model"]

        +    data = load_dataset()
        +    model = load_model()
             ...

         trainer = ray.train.lightgbm.LightGBMTrainer(train_func, train_loop_config=config, ...)


Configure distributed training parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

To enable distributed LightGBM training, add network communication parameters to your training configuration using :func:`ray.train.lightgbm.get_network_params`. 
This function automatically configures the necessary network settings for worker communication:

.. code-block:: diff

     def train_func():
         ...
         params = {
             # Your LightGBM training parameters here
             ...
    +        "tree_learner": "data_parallel",
    +        "pre_partition": True,
    +        **ray.train.lightgbm.get_network_params(),
         }
         
         model = lightgbm.train(
             params,
             ...
         )
         ...

.. note::
    Make sure to set ``tree_learner`` to enable distributed training. See the `LightGBM documentation <https://lightgbm.readthedocs.io/en/latest/Parallel-Learning-Guide.html#tree-learner>`_ for more details.
    You should also set ``pre_partition=True`` if using Ray Data to load and shard your dataset, as shown in the quickstart example.

Report metrics and save checkpoints
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

To persist your checkpoints and monitor training progress, add a
:class:`ray.train.lightgbm.RayTrainReportCallback` utility callback to your Trainer:


.. testcode:: python
    :skipif: True

    import lightgbm
    from ray.train.lightgbm import RayTrainReportCallback

    def train_func():
        ...
        bst = lightgbm.train(
            ...,
            callbacks=[
                RayTrainReportCallback(
                    metrics=["eval-multi_logloss"], frequency=1
                )
            ],
        )
        ...


Reporting metrics and checkpoints to Ray Train enables :ref:`fault-tolerant training <train-fault-tolerance>` and the integration with Ray Tune.

Loading data
------------

When running distributed LightGBM training, each worker should use a different shard of the dataset.


.. testcode:: python
    :skipif: True

    def get_train_dataset(world_rank: int) -> lightgbm.Dataset:
        # Define logic to get the Dataset shard for this worker rank
        ...

    def get_eval_dataset(world_rank: int) -> lightgbm.Dataset:
        # Define logic to get the Dataset for each worker
        ...

    def train_func():
        rank = ray.train.get_world_rank()
        train_set = get_train_dataset(rank)
        eval_set = get_eval_dataset(rank)
        ...

A common way to do this is to pre-shard the dataset and then assign each worker a different set of files to read.

Pre-sharding the dataset is not very flexible to changes in the number of workers, since some workers may be assigned more data than others. For more flexibility, Ray Data provides a solution for sharding the dataset at runtime.

Use Ray Data to shard the dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:ref:`Ray Data <data>` is a distributed data processing library that allows you to easily shard and distribute your data across multiple workers. 

First, load your **entire** dataset as a Ray Data Dataset. 
Reference the :ref:`Ray Data Quickstart <data_quickstart>` for more details on how to load and preprocess data from different sources.

.. testcode:: python
    :skipif: True

    train_dataset = ray.data.read_parquet("s3://path/to/entire/train/dataset/dir")
    eval_dataset = ray.data.read_parquet("s3://path/to/entire/eval/dataset/dir")

In the training function, you can access the dataset shards for this worker using :meth:`ray.train.get_dataset_shard`. 
Convert this into a native `lightgbm.Dataset <https://lightgbm.readthedocs.io/en/latest/Python-Intro.html#dataset>`_.


.. testcode:: python
    :skipif: True

    def get_dataset(dataset_name: str) -> lightgbm.Dataset:
        shard = ray.train.get_dataset_shard(dataset_name)
        df = shard.materialize().to_pandas()
        X, y = df.drop("target", axis=1), df["target"]
        return lightgbm.Dataset(X, label=y)

    def train_func():
        train_set = get_dataset("train")
        eval_set = get_dataset("eval")
        ...


Finally, pass the dataset to the Trainer. This will automatically shard the dataset across the workers. These keys must match the keys used when calling ``get_dataset_shard`` in the training function.


.. testcode:: python
    :skipif: True

    trainer = LightGBMTrainer(..., datasets={"train": train_dataset, "eval": eval_dataset})
    trainer.fit()


For more details, see :ref:`data-ingest-torch`.

Configure scale and GPUs
------------------------

Outside of your training function, create a :class:`~ray.train.ScalingConfig` object to configure:

1. :class:`num_workers <ray.train.ScalingConfig>` - The number of distributed training worker processes.
2. :class:`use_gpu <ray.train.ScalingConfig>` - Whether each worker should use a GPU (or CPU).
3. :class:`resources_per_worker <ray.train.ScalingConfig>` - The number of CPUs or GPUs per worker.

.. testcode::

    from ray.train import ScalingConfig
    
    # 4 nodes with 8 CPUs each.
    scaling_config = ScalingConfig(num_workers=4, resources_per_worker={"CPU": 8})

.. note::
    When using Ray Data with Ray Train, be careful not to request all available CPUs in your cluster with the `resources_per_worker` parameter. 
    Ray Data needs CPU resources to execute data preprocessing operations in parallel. 
    If all CPUs are allocated to training workers, Ray Data operations may be bottlenecked, leading to reduced performance. 
    A good practice is to leave some portion of CPU resources available for Ray Data operations.

    For example, if your cluster has 8 CPUs per node, you might allocate 6 CPUs to training workers and leave 2 CPUs for Ray Data:

    .. testcode::

        # Allocate 6 CPUs per worker, leaving resources for Ray Data operations
        scaling_config = ScalingConfig(num_workers=4, resources_per_worker={"CPU": 6})


In order to use GPUs, you will need to set the `use_gpu` parameter to `True` in your :class:`~ray.train.ScalingConfig` object.
This will request and assign a single GPU per worker.

.. testcode::
    
    # 1 node with 8 CPUs and 4 GPUs each.
    scaling_config = ScalingConfig(num_workers=4, use_gpu=True)

    # 4 nodes with 8 CPUs and 4 GPUs each.
    scaling_config = ScalingConfig(num_workers=16, use_gpu=True)

When using GPUs, you will also need to update your training function to use the assigned GPU. 
This can be done by setting the `"device"` parameter as `"gpu"`. 
For more details on LightGBM's GPU support, see the `LightGBM GPU documentation <https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html>`__.

.. code-block:: diff

    def train_func():
        ...

        params = {
            ...,
  +         "device": "gpu",
        }

        bst = lightgbm.train(
            params,
            ...
        )


Configure persistent storage
----------------------------

Create a :class:`~ray.train.RunConfig` object to specify the path where results
(including checkpoints and artifacts) will be saved.

.. testcode::

    from ray.train import RunConfig

    # Local path (/some/local/path/unique_run_name)
    run_config = RunConfig(storage_path="/some/local/path", name="unique_run_name")

    # Shared cloud storage URI (s3://bucket/unique_run_name)
    run_config = RunConfig(storage_path="s3://bucket", name="unique_run_name")

    # Shared NFS path (/mnt/nfs/unique_run_name)
    run_config = RunConfig(storage_path="/mnt/nfs", name="unique_run_name")


.. warning::

    Specifying a *shared storage location* (such as cloud storage or NFS) is
    *optional* for single-node clusters, but it is **required for multi-node clusters.**
    Using a local path will :ref:`raise an error <multinode-local-storage-warning>`
    during checkpointing for multi-node clusters.


For more details, see :ref:`persistent-storage-guide`.

Launch a training job
---------------------

Tying it all together, you can now launch a distributed training job with a :class:`~ray.train.lightgbm.LightGBMTrainer`.

.. testcode::
    :hide:

    from ray.train import ScalingConfig

    train_func = lambda: None
    scaling_config = ScalingConfig(num_workers=1)
    run_config = None

.. testcode::

    from ray.train.lightgbm import LightGBMTrainer

    trainer = LightGBMTrainer(
        train_func, scaling_config=scaling_config, run_config=run_config
    )
    result = trainer.fit()

Access training results
-----------------------

After training completes, a :class:`~ray.train.Result` object is returned which contains
information about the training run, including the metrics and checkpoints reported during training.

.. testcode::

    result.metrics     # The metrics reported during training.
    result.checkpoint  # The latest checkpoint reported during training.
    result.path        # The path where logs are stored.
    result.error       # The exception that was raised, if training failed.

For more usage examples, see :ref:`train-inspect-results`.

Next steps
----------

After you have converted your LightGBM training script to use Ray Train:

* See :ref:`User Guides <train-user-guides>` to learn more about how to perform specific tasks.
* Browse the :doc:`Examples <examples>` for end-to-end examples of how to use Ray Train.
* Consult the :ref:`API Reference <train-api>` for more details on the classes and methods from this tutorial.