XGBoost¶
Contents¶
SageMaker Example¶
Use XGBoost as a built-in algorithm¶
The XGBoost algorithm can be used 1) as a built-in algorithm, or 2) as a
framework such as MXNet, PyTorch, or Tensorflow. If SageMaker XGBoost is
used as a built-in algorithm in container version 0.90-2 or later,
Amazon SageMaker Debugger will be available by default (i.e., zero code
change experience). See XGBoost Algorithm AWS
docmentation
for more information on how to use XGBoost as a built-in algorithm. See
Amazon SageMaker Debugger
examples
for sample notebooks that demonstrate debugging and monitoring
capabilities of Amazon SageMaker Debugger. See SageMaker Python
SDK for more
information on how to configure the Amazon SageMaker Debugger from the
Python SDK.
Use XGBoost as a framework¶
When SageMaker XGBoost is used as a framework, it is recommended that the hook is configured from the SageMaker Python SDK. By using SageMaker Python SDK, you can run different jobs (e.g., Processing jobs) on the SageMaker platform. You can retrieve the hook as follows.
import xgboost as xgb
from smdebug.xgboost import Hook
dtrain = xgb.DMatrix("train.libsvm")
dtest = xgb.DMatrix("test.libsmv")
hook = Hook.create_from_json_file()
hook.train_data = dtrain # required
hook.validation_data = dtest # optional
hook.hyperparameters = params # optional
bst = xgb.train(
params,
dtrain,
callbacks=[hook],
evals_result=[(dtrain, "train"), (dvalid, "validation")]
)
Alternatively, you can also create the hook from smdebug’s Python
API as shown in the next section.
Use the Debugger hook¶
If you are in a non-SageMaker environment, or even in SageMaker, if you want to configure the hook in a certain way in script mode, you can use the full Debugger hook API as follows.
import xgboost as xgb
from smdebug.xgboost import Hook
dtrain = xgb.DMatrix("train.libsvm")
dvalid = xgb.DMatrix("validation.libsmv")
hook = Hook(
out_dir=out_dir, # required
train_data=dtrain, # required
validation_data=dvalid, # optional
hyperparameters=hyperparameters, # optional
)
Full API¶
def __init__(
self,
out_dir,
export_tensorboard = False,
tensorboard_dir = None,
dry_run = False,
reduction_config = None,
save_config = None,
include_regex = None,
include_collections = None,
save_all = False,
include_workers = "one",
hyperparameters = None,
train_data = None,
validation_data = None,
)
Initializes the hook. Pass this object as a callback to
xgboost.train().
out_dir(str): A path into which tensors and metadata will be written.
export_tensorboard(bool): Whether to use TensorBoard logs.
tensorboard_dir(str): Where to save TensorBoard logs.
dry_run(bool): If true, evaluations are not actually saved to disk.
reduction_config(ReductionConfig object): Not supported in XGBoost and will be ignored.
save_config(SaveConfig object): See the Common API.
include_regex(list[str]): List of additional regexes to save.
include_collections(list[str]): List of collections to save.
save_all(bool): Saves all tensors and collections. WARNING: May be memory-intensive and slow.
include_workers(str): Used for distributed training, can also be “all”.
hyperparameters(dict): Booster params.
train_data(DMatrix object): Data to be trained.
validation_data(DMatrix object): Validation set for which metrics will evaluated during training.