Distributed XGBoost with Dask

Dask 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 with some extended functionalities and a different interface. 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.

Contents

Requirements

Dask can be installed using either pip or conda (see the dask installation documentation for more information). For accelerating XGBoost with GPUs, 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:

import xgboost as xgb
import dask.array as da
import dask.distributed

if __name__ == "__main__":
    cluster = dask.distributed.LocalCluster()
    client = dask.distributed.Client(cluster)

    # X and y must be Dask dataframes or arrays
    num_obs = 1e5
    num_features = 20
    X = da.random.random(size=(num_obs, num_features), chunks=(1000, num_features))
    y = da.random.random(size=(num_obs, 1), chunks=(1000, 1))

    dtrain = xgb.dask.DaskDMatrix(client, X, y)

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

Here we first create a cluster in single-node mode with distributed.LocalCluster, then connect a distributed.Client to this cluster, setting up an environment for later computation. Notice that the cluster construction is guared by __name__ == "__main__", which is necessary otherwise there might be obscure errors.

We then create a xgboost.dask.DaskDMatrix object and pass it to xgboost.dask.train(), along with some other parameters, much like XGBoost’s normal, non-dask interface. Unlike that interface, data and label must be either Dask DataFrame or Dask Array instances.

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:

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

For prediction, pass the output returned by train into xgboost.dask.predict():

prediction = xgb.dask.predict(client, output, dtrain)
# Or equivalently, pass ``output['booster']``:
prediction = xgb.dask.predict(client, output['booster'], dtrain)

Eliminating the construction of DaskDMatrix is also possible, this can make the computation a bit faster when meta information like base_margin is not needed:

prediction = xgb.dask.predict(client, output, X)
# Use inplace version.
prediction = xgb.dask.inplace_predict(client, output, X)

Here prediction is a dask Array object containing predictions from model if input is a DaskDMatrix or da.Array. When putting dask collection directly into the predict function or using xgboost.dask.inplace_predict(), the output type depends on input data. See next section for details.

Alternatively, XGBoost also implements the Scikit-Learn interface with DaskXGBClassifier, DaskXGBRegressor, DaskXGBRanker and 2 random forest variances. This wrapper is similar to the single node Scikit-Learn interface in xgboost, with dask collection as inputs and has an additional client attribute. See following sections and XGBoost Dask Feature Walkthrough for more examples.

Running prediction

In previous example we used DaskDMatrix as input to predict function. In practice, it’s also possible to call predict function directly on dask collections like Array and DataFrame and might have better prediction performance. When DataFrame is used as prediction input, the result is a dask Series instead of array. Also, there’s in-place predict support on dask interface, which can help reducing both memory usage and prediction time.

# dtrain is the DaskDMatrix defined above.
prediction = xgb.dask.predict(client, booster, dtrain)

or equivalently:

# where X is a dask DataFrame or dask Array.
prediction = xgb.dask.predict(client, booster, X)

Also for inplace prediction:

booster.set_param({'predictor': 'gpu_predictor'})
# where X is a dask DataFrame or dask Array containing cupy or cuDF backed data.
prediction = xgb.dask.inplace_predict(client, booster, X)

When input is da.Array object, output is always da.Array. However, if the input type is dd.DataFrame, output can be dd.Series, dd.DataFrame or da.Array, depending on output shape. For example, when shap based prediction is used, the return value can have 3 or 4 dimensions , in such cases an Array is always returned.

The performance of running prediction, either using predict or inplace_predict, is sensitive to number of blocks. Internally, it’s implemented using da.map_blocks and dd.map_partitions. When number of partitions is large and each of them have only small amount of data, the overhead of calling predict becomes visible. On the other hand, if not using GPU, the number of threads used for prediction on each block matters. Right now, xgboost uses single thread for each partition. If the number of blocks on each workers is smaller than number of cores, then the CPU workers might not be fully utilized.

One simple optimization for running consecutive predictions is using distributed.Future:

dataset = [X_0, X_1, X_2]
booster_f = client.scatter(booster, broadcast=True)
futures = []
for X in dataset:
    # Here we pass in a future instead of concrete booster
    shap_f = xgb.dask.predict(client, booster_f, X, pred_contribs=True)
    futures.append(shap_f)

results = client.gather(futures)

This is only available on functional interface, as the Scikit-Learn wrapper doesn’t know how to maintain a valid future for booster. To obtain the booster object from Scikit-Learn wrapper object:

cls = xgb.dask.DaskXGBClassifier()
cls.fit(X, y)

booster = cls.get_booster()

Scikit-Learn interface

As mentioned previously, there’s another interface that mimics the scikit-learn estimators with higher level of of abstraction. The interface is easier to use compared to the functional interface but with more constraints. It’s worth mentioning that, although the interface mimics scikit-learn estimators, it doesn’t work with normal scikit-learn utilities like GridSearchCV as scikit-learn doesn’t understand distributed dask data collection.

from distributed import LocalCluster, Client
import xgboost as xgb


def main(client: Client) -> None:
    X, y = load_data()
    clf = xgb.dask.DaskXGBClassifier(n_estimators=100, tree_method="hist")
    clf.client = client  # assign the client
    clf.fit(X, y, eval_set=[(X, y)])
    proba = clf.predict_proba(X)


if __name__ == "__main__":
    with LocalCluster() as cluster:
        with Client(cluster) as client:
            main(client)

Working with other clusters

LocalCluster is mostly used for testing. In real world applications some other clusters might be preferred. Examples are like LocalCUDACluster for single node multi-GPU instance, manually launched cluster by using command line utilities like dask-worker from distributed for not yet automated environments. Some special clusters like KubeCluster from dask-kubernetes package are also possible. The dask API in xgboost is orthogonal to the cluster type and can be used with any of them. A typical testing workflow with KubeCluster looks like this:

from dask_kubernetes import KubeCluster  # Need to install the ``dask-kubernetes`` package
from dask.distributed import Client
import xgboost as xgb
import dask
import dask.array as da

dask.config.set({"kubernetes.scheduler-service-type": "LoadBalancer",
                 "kubernetes.scheduler-service-wait-timeout": 360,
                 "distributed.comm.timeouts.connect": 360})


def main():
    '''Connect to a remote kube cluster with GPU nodes and run training on it.'''
    m = 1000
    n = 10
    kWorkers = 2                # assuming you have 2 GPU nodes on that cluster.
    # You need to work out the worker-spec youself.  See document in dask_kubernetes for
    # its usage.  Here we just want to show that XGBoost works on various clusters.
    cluster = KubeCluster.from_yaml('worker-spec.yaml', deploy_mode='remote')
    cluster.scale(kWorkers)     # scale to use all GPUs

    with Client(cluster) as client:
        X = da.random.random(size=(m, n), chunks=100)
        y = da.random.random(size=(m, ), chunks=100)

        regressor = xgb.dask.DaskXGBRegressor(n_estimators=10, missing=0.0)
        regressor.client = client
        regressor.set_params(tree_method='gpu_hist')
        regressor.fit(X, y, eval_set=[(X, y)])


if __name__ == '__main__':
    # Launch the kube cluster on somewhere like GKE, then run this as client process.
    # main function will connect to that cluster and start training xgboost model.
    main()

However, these clusters might have their subtle differences like network configuration, or specific cluster implementation might contains bugs that we are not aware of. Open an issue if such case is found and there’s no documentation on how to resolve it in that cluster implementation.

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:

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 training. But if nthread parameter is set:

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

New in version 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 and document in distributed. 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:

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:

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))

Evaluation and Early Stopping

New in version 1.3.0.

The Dask interface allows the use of validation sets that are stored in distributed collections (Dask DataFrame or Dask Array). These can be used for evaluation and early stopping.

To enable early stopping, pass one or more validation sets containing DaskDMatrix objects.

import dask.array as da
import xgboost as xgb

num_rows = 1e6
num_features = 100
num_partitions = 10
rows_per_chunk = num_rows / num_partitions

data = da.random.random(
    size=(num_rows, num_features),
    chunks=(rows_per_chunk, num_features)
)

labels = da.random.random(
    size=(num_rows, 1),
    chunks=(rows_per_chunk, 1)
)

X_eval = da.random.random(
    size=(num_rows, num_features),
    chunks=(rows_per_chunk, num_features)
)

y_eval = da.random.random(
    size=(num_rows, 1),
    chunks=(rows_per_chunk, 1)
)

dtrain = xgb.dask.DaskDMatrix(
    client=client,
    data=data,
    label=labels
)

dvalid = xgb.dask.DaskDMatrix(
    client=client,
    data=X_eval,
    label=y_eval
)

result = xgb.dask.train(
    client=client,
    params={
        "objective": "reg:squarederror",
    },
    dtrain=dtrain,
    num_boost_round=10,
    evals=[(dvalid, "valid1")],
    early_stopping_rounds=3
)

When validation sets are provided to xgb.dask.train() in this way, the model object returned by xgb.dask.train() contains a history of evaluation metrics for each validation set, across all boosting rounds.

print(result["history"])
# {'valid1': OrderedDict([('rmse', [0.28857, 0.28858, 0.288592, 0.288598])])}

If early stopping is enabled by also passing early_stopping_rounds, you can check the best iteration in the returned booster.

booster = result["booster"]
print(booster.best_iteration)
best_model = booster[: booster.best_iteration]

Other customization

XGBoost dask interface accepts other advanced features found in single node Python interface, including callback functions, custom evaluation metric and objective:

def eval_error_metric(predt, dtrain: xgb.DMatrix):
    label = dtrain.get_label()
    r = np.zeros(predt.shape)
    gt = predt > 0.5
    r[gt] = 1 - label[gt]
    le = predt <= 0.5
    r[le] = label[le]
    return 'CustomErr', np.sum(r)

# custom callback
early_stop = xgb.callback.EarlyStopping(
    rounds=early_stopping_rounds,
    metric_name="CustomErr",
    data_name="Train",
    save_best=True,
)

booster = xgb.dask.train(
    client,
    params={
        "objective": "binary:logistic",
        "eval_metric": ["error", "rmse"],
        "tree_method": "hist",
    },
    dtrain=D_train,
    evals=[(D_train, "Train"), (D_valid, "Valid")],
    feval=eval_error_metric,  # custom evaluation metric
    num_boost_round=100,
    callbacks=[early_stop],
)

Troubleshooting

New in version 1.6.0.

In some environments XGBoost might fail to resolve the IP address of the scheduler, a symptom is user receiving OSError: [Errno 99] Cannot assign requested address error during training. A quick workaround is to specify the address explicitly. To do that dask config is used:

import dask
from distributed import Client
from xgboost import dask as dxgb
# let xgboost know the scheduler address
dask.config.set({"xgboost.scheduler_address": "192.0.0.100"})

with Client(scheduler_file="sched.json") as client:
    reg = dxgb.DaskXGBRegressor()

# or we can specify the port too
with dask.config.set({"xgboost.scheduler_address": "192.0.0.100:12345"}):
    reg = dxgb.DaskXGBRegressor()

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, 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 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 can be used to monitor what operations are currently being performed.

Memory Usage

Here are some pratices on reducing memory usage with dask and xgboost.

  • In a distributed work flow, data is best loaded by dask collections directly instead of loaded by client process. When loading with client process is unavoidable, use client.scatter to distribute data from client process to workers. See [2] for a nice summary.

  • When using GPU input, like dataframe loaded by dask_cudf, you can try xgboost.dask.DaskDeviceQuantileDMatrix as a drop in replacement for DaskDMatrix to reduce overall memory usage. See Example of training with Dask on GPU for an example.

  • Use in-place prediction when possible.

References:

  1. https://github.com/dask/dask/issues/6833

  2. https://stackoverflow.com/questions/45941528/how-to-efficiently-send-a-large-numpy-array-to-the-cluster-with-dask-array