Distributed XGBoost on Ray
XGBoost-Ray is a distributed backend for
XGBoost, built
on top of
distributed computing framework Ray.
XGBoost-Ray
All releases are tested on large clusters and workloads.
Installation
You can install the latest XGBoost-Ray release from PIP:
pip install "xgboost_ray"
If you’d like to install the latest master, use this command instead:
pip install "git+https://github.com/ray-project/xgboost_ray.git#egg=xgboost_ray"
Usage
XGBoost-Ray provides a drop-in replacement for XGBoost’s train
function. To pass data, instead of using xgb.DMatrix you will
have to use xgboost_ray.RayDMatrix. You can also use a scikit-learn
interface - see next section.
Just as in original xgb.train() function, the
training parameters
are passed as the params dictionary.
Ray-specific distributed training parameters are configured with a
xgboost_ray.RayParams object. For instance, you can set
the num_actors property to specify how many distributed actors
you would like to use.
Here is a simplified example (which requires sklearn):
Training:
from xgboost_ray import RayDMatrix, RayParams, train
from sklearn.datasets import load_breast_cancer
train_x, train_y = load_breast_cancer(return_X_y=True)
train_set = RayDMatrix(train_x, train_y)
evals_result = {}
bst = train(
{
"objective": "binary:logistic",
"eval_metric": ["logloss", "error"],
},
train_set,
evals_result=evals_result,
evals=[(train_set, "train")],
verbose_eval=False,
ray_params=RayParams(
num_actors=2, # Number of remote actors
cpus_per_actor=1))
bst.save_model("model.xgb")
print("Final training error: {:.4f}".format(
evals_result["train"]["error"][-1]))
Prediction:
from xgboost_ray import RayDMatrix, RayParams, predict
from sklearn.datasets import load_breast_cancer
import xgboost as xgb
data, labels = load_breast_cancer(return_X_y=True)
dpred = RayDMatrix(data, labels)
bst = xgb.Booster(model_file="model.xgb")
pred_ray = predict(bst, dpred, ray_params=RayParams(num_actors=2))
print(pred_ray)
scikit-learn API
XGBoost-Ray also features a scikit-learn API fully mirroring pure
XGBoost scikit-learn API, providing a completely drop-in
replacement. The following estimators are available:
RayXGBClassifierRayXGRegressorRayXGBRFClassifierRayXGBRFRegressorRayXGBRanker
Example usage of RayXGBClassifier:
from xgboost_ray import RayXGBClassifier, RayParams
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
seed = 42
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.25, random_state=42
)
clf = RayXGBClassifier(
n_jobs=4, # In XGBoost-Ray, n_jobs sets the number of actors
random_state=seed
)
# scikit-learn API will automatically convert the data
# to RayDMatrix format as needed.
# You can also pass X as a RayDMatrix, in which case
# y will be ignored.
clf.fit(X_train, y_train)
pred_ray = clf.predict(X_test)
print(pred_ray)
pred_proba_ray = clf.predict_proba(X_test)
print(pred_proba_ray)
# It is also possible to pass a RayParams object
# to fit/predict/predict_proba methods - will override
# n_jobs set during initialization
clf.fit(X_train, y_train, ray_params=RayParams(num_actors=2))
pred_ray = clf.predict(X_test, ray_params=RayParams(num_actors=2))
print(pred_ray)
Things to keep in mind:
n_jobs parameter controls the number of actors spawned.
You can pass a RayParams object to the
fit/predict/predict_proba methods as the ray_params argument
for greater control over resource allocation. Doing
so will override the value of n_jobs with the value of
ray_params.num_actors attribute. For more information, refer
to the Resources section below.- By default
n_jobs is set to 1, which means the training
will not be distributed. Make sure to either set n_jobs
to a higher value or pass a RayParams object as outlined above
in order to take advantage of XGBoost-Ray’s functionality.
- After calling
fit, additional evaluation results (e.g. training time,
number of rows, callback results) will be available under
additional_results_ attribute.
- XGBoost-Ray’s scikit-learn API is based on XGBoost 1.4.
While we try to support older XGBoost versions, please note that
this library is only fully tested and supported for XGBoost >= 1.4.
For more information on the scikit-learn API, refer to the XGBoost documentation.
Data loading
Data is passed to XGBoost-Ray via a RayDMatrix object.
The RayDMatrix lazy loads data and stores it sharded in the
Ray object store. The Ray XGBoost actors then access these
shards to run their training on.
A RayDMatrix support various data and file types, like
Pandas DataFrames, Numpy Arrays, CSV files and Parquet files.
Example loading multiple parquet files:
import glob
from xgboost_ray import RayDMatrix, RayFileType
# We can also pass a list of files
path = list(sorted(glob.glob("/data/nyc-taxi/*/*/*.parquet")))
# This argument will be passed to `pd.read_parquet()`
columns = [
"passenger_count",
"trip_distance", "pickup_longitude", "pickup_latitude",
"dropoff_longitude", "dropoff_latitude",
"fare_amount", "extra", "mta_tax", "tip_amount",
"tolls_amount", "total_amount"
]
dtrain = RayDMatrix(
path,
label="passenger_count", # Will select this column as the label
columns=columns,
# ignore=["total_amount"], # Optional list of columns to ignore
filetype=RayFileType.PARQUET)
Hyperparameter Tuning
XGBoost-Ray integrates with Ray Tune to provide distributed hyperparameter tuning for your
distributed XGBoost models. You can run multiple XGBoost-Ray training runs in parallel, each with a different
hyperparameter configuration, and each training run parallelized by itself. All you have to do is move your training
code to a function, and pass the function to tune.run. Internally, train will detect if tune is being used and will
automatically report results to tune.
Example using XGBoost-Ray with Ray Tune:
from xgboost_ray import RayDMatrix, RayParams, train
from sklearn.datasets import load_breast_cancer
num_actors = 4
num_cpus_per_actor = 1
ray_params = RayParams(
num_actors=num_actors,
cpus_per_actor=num_cpus_per_actor)
def train_model(config):
train_x, train_y = load_breast_cancer(return_X_y=True)
train_set = RayDMatrix(train_x, train_y)
evals_result = {}
bst = train(
params=config,
dtrain=train_set,
evals_result=evals_result,
evals=[(train_set, "train")],
verbose_eval=False,
ray_params=ray_params)
bst.save_model("model.xgb")
from ray import tune
# Specify the hyperparameter search space.
config = {
"tree_method": "approx",
"objective": "binary:logistic",
"eval_metric": ["logloss", "error"],
"eta": tune.loguniform(1e-4, 1e-1),
"subsample": tune.uniform(0.5, 1.0),
"max_depth": tune.randint(1, 9)
}
# Make sure to use the `get_tune_resources` method to set the `resources_per_trial`
analysis = tune.run(
train_model,
config=config,
metric="train-error",
mode="min",
num_samples=4,
resources_per_trial=ray_params.get_tune_resources())
print("Best hyperparameters", analysis.best_config)
Also see examples/simple_tune.py for another example.
Fault tolerance
XGBoost-Ray leverages the stateful Ray actor model to
enable fault tolerant training. There are currently
two modes implemented.
Non-elastic training (warm restart)
When an actor or node dies, XGBoost-Ray will retain the
state of the remaining actors. In non-elastic training,
the failed actors will be replaced as soon as resources
are available again. Only these actors will reload their
parts of the data. Training will resume once all actors
are ready for training again.
You can set this mode in the RayParams:
from xgboost_ray import RayParams
ray_params = RayParams(
elastic_training=False, # Use non-elastic training
max_actor_restarts=2, # How often are actors allowed to fail
)
Elastic training
In elastic training, XGBoost-Ray will continue training
with fewer actors (and on fewer data) when a node or actor
dies. The missing actors are staged in the background,
and are reintegrated into training once they are back and
loaded their data.
This mode will train on fewer data for a period of time,
which can impact accuracy. In practice, we found these
effects to be minor, especially for large shuffled datasets.
The immediate benefit is that training time is reduced
significantly to almost the same level as if no actors died.
Thus, especially when data loading takes a large part of
the total training time, this setting can dramatically speed
up training times for large distributed jobs.
You can configure this mode in the RayParams:
from xgboost_ray import RayParams
ray_params = RayParams(
elastic_training=True, # Use elastic training
max_failed_actors=3, # Only allow at most 3 actors to die at the same time
max_actor_restarts=2, # How often are actors allowed to fail
)
Resources
By default, XGBoost-Ray tries to determine the number of CPUs
available and distributes them evenly across actors.
In the case of very large clusters or clusters with many different
machine sizes, it makes sense to limit the number of CPUs per actor
by setting the cpus_per_actor argument. Consider always
setting this explicitly.
The number of XGBoost actors always has to be set manually with
the num_actors argument.
Multi GPU training
XGBoost-Ray enables multi GPU training. The XGBoost core backend
will automatically leverage NCCL2 for cross-device communication.
All you have to do is to start one actor per GPU and set XGBoost’s
tree_method to a GPU-compatible option, eg. gpu_hist (see XGBoost
documentation for more details.)
For instance, if you have 2 machines with 4 GPUs each, you will want
to start 8 remote actors, and set gpus_per_actor=1. There is usually
no benefit in allocating less (e.g. 0.5) or more than one GPU per actor.
You should divide the CPUs evenly across actors per machine, so if your
machines have 16 CPUs in addition to the 4 GPUs, each actor should have
4 CPUs to use.
from xgboost_ray import RayParams
ray_params = RayParams(
num_actors=8,
gpus_per_actor=1,
cpus_per_actor=4, # Divide evenly across actors per machine
)
How many remote actors should I use?
This depends on your workload and your cluster setup.
Generally there is no inherent benefit of running more than
one remote actor per node for CPU-only training. This is because
XGBoost core can already leverage multiple CPUs via threading.
However, there are some cases when you should consider starting
more than one actor per node:
- For multi GPU training, each GPU should have a separate
remote actor. Thus, if your machine has 24 CPUs and 4 GPUs,
you will want to start 4 remote actors with 6 CPUs and 1 GPU
each
- In a heterogeneous cluster, you might want to find the
greatest common divisor
for the number of CPUs.
E.g. for a cluster with three nodes of 4, 8, and 12 CPUs, respectively,
you should set the number of actors to 6 and the CPUs per
actor to 4.
Distributed data loading
XGBoost-Ray can leverage both centralized and distributed data loading.
In centralized data loading, the data is partitioned by the head node
and stored in the object store. Each remote actor then retrieves their
partitions by querying the Ray object store. Centralized loading is used
when you pass centralized in-memory dataframes, such as Pandas dataframes
or Numpy arrays, or when you pass a single source file, such as a single CSV
or Parquet file.
from xgboost_ray import RayDMatrix
# This will use centralized data loading, as only one source file is specified
# `label_col` is a column in the CSV, used as the target label
ray_params = RayDMatrix("./source_file.csv", label="label_col")
In distributed data loading, each remote actor loads their data directly from
the source (e.g. local hard disk, NFS, HDFS, S3),
without a central bottleneck. The data is still stored in the
object store, but locally to each actor. This mode is used automatically
when loading data from multiple CSV or Parquet files. Please note that
we do not check or enforce partition sizes in this case - it is your job
to make sure the data is evenly distributed across the source files.
from xgboost_ray import RayDMatrix
# This will use distributed data loading, as four source files are specified
# Please note that you cannot schedule more than four actors in this case.
# `label_col` is a column in the Parquet files, used as the target label
ray_params = RayDMatrix([
"hdfs:///tmp/part1.parquet",
"hdfs:///tmp/part2.parquet",
"hdfs:///tmp/part3.parquet",
"hdfs:///tmp/part4.parquet",
], label="label_col")
Lastly, XGBoost-Ray supports distributed dataframe representations, such
as Ray Datasets,
Modin and
Dask dataframes
(used with Dask on Ray).
Here, XGBoost-Ray will check on which nodes the distributed partitions
are currently located, and will assign partitions to actors in order to
minimize cross-node data transfer. Please note that we also assume here
that partition sizes are uniform.
from xgboost_ray import RayDMatrix
# This will try to allocate the existing Modin partitions
# to co-located Ray actors. If this is not possible, data will
# be transferred across nodes
ray_params = RayDMatrix(existing_modin_df)
Data sources
The following data sources can be used with a RayDMatrix object.
| Type |
Centralized loading |
Distributed loading |
| Numpy array |
Yes |
No |
| Pandas dataframe |
Yes |
No |
| Single CSV |
Yes |
No |
| Multi CSV |
Yes |
Yes |
| Single Parquet |
Yes |
No |
| Multi Parquet |
Yes |
Yes |
| Ray Dataset |
Yes |
Yes |
| Petastorm |
Yes |
Yes |
| Dask dataframe |
Yes |
Yes |
| Modin dataframe |
Yes |
Yes |
Memory usage
XGBoost uses a compute-optimized datastructure, the DMatrix,
to hold training data. When converting a dataset to a DMatrix,
XGBoost creates intermediate copies and ends up
holding a complete copy of the full data. The data will be converted
into the local dataformat (on a 64 bit system these are 64 bit floats.)
Depending on the system and original dataset dtype, this matrix can
thus occupy more memory than the original dataset.
The peak memory usage for CPU-based training is at least
3x the dataset size (assuming dtype float32 on a 64bit system)
plus about 400,000 KiB for other resources,
like operating system requirements and storing of intermediate
results.
Example
- Machine type: AWS m5.xlarge (4 vCPUs, 16 GiB RAM)
- Usable RAM: ~15,350,000 KiB
- Dataset: 1,250,000 rows with 1024 features, dtype float32.
Total size: 5,000,000 KiB
- XGBoost DMatrix size: ~10,000,000 KiB
This dataset will fit exactly on this node for training.
Note that the DMatrix size might be lower on a 32 bit system.
GPUs
Generally, the same memory requirements exist for GPU-based
training. Additionally, the GPU must have enough memory
to hold the dataset.
In the example above, the GPU must have at least
10,000,000 KiB (about 9.6 GiB) memory. However,
empirically we found that using a DeviceQuantileDMatrix
seems to show more peak GPU memory usage, possibly
for intermediate storage when loading data (about 10%).
Best practices
In order to reduce peak memory usage, consider the following
suggestions:
- Store data as
float32 or less. More precision is often
not needed, and keeping data in a smaller format will
help reduce peak memory usage for initial data loading.
- Pass the
dtype when loading data from CSV. Otherwise,
floating point values will be loaded as np.float64
per default, increasing peak memory usage by 33%.
Placement Strategies
XGBoost-Ray leverages Ray’s Placement Group API (https://docs.ray.io/en/latest/ray-core/scheduling/placement-group.html)
to implement placement strategies for better fault tolerance.
By default, a SPREAD strategy is used for training, which attempts to spread all of the training workers
across the nodes in a cluster on a best-effort basis. This improves fault tolerance since it minimizes the
number of worker failures when a node goes down, but comes at a cost of increased inter-node communication
To disable this strategy, set the RXGB_USE_SPREAD_STRATEGY environment variable to 0. If disabled, no
particular placement strategy will be used.
Note that this strategy is used only when elastic_training is not used. If elastic_training is set to True,
no placement strategy is used.
When XGBoost-Ray is used with Ray Tune for hyperparameter tuning, a PACK strategy is used. This strategy
attempts to place all workers for each trial on the same node on a best-effort basis. This means that if a node
goes down, it will be less likely to impact multiple trials.
When placement strategies are used, XGBoost-Ray will wait for 100 seconds for the required resources
to become available, and will fail if the required resources cannot be reserved and the cluster cannot autoscale
to increase the number of resources. You can change the RXGB_PLACEMENT_GROUP_TIMEOUT_S environment variable to modify
how long this timeout should be.
More examples
For complete end to end examples, please have a look at
the examples folder:
Resources
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