def load_checkpoint(checkpoint_dir, max_try=5):
"""
:param checkpoint_dir: e.g., /opt/ml/checkpoints
:param max_try: number of times to try loading checkpoint before giving up.
:return xgb_model: file path of stored xgb model. None if no checkpoint.
:return iteration: iterations completed before last checkpoiint.
"""
if not checkpoint_dir or not os.path.exists(checkpoint_dir):
return None, 0
regex = r"^{0}\.[0-9]+$".format(CHECKPOINT_FILENAME)
checkpoints = [f for f in os.listdir(checkpoint_dir) if re.match(regex, f)]
if not checkpoints:
return None, 0
checkpoints.sort()
xgb_model, iteration = None, 0
for _ in range(max_try):
try:
latest_checkpoint = checkpoints.pop()
xgb_model = os.path.join(checkpoint_dir, latest_checkpoint)
booster = Booster()
booster.load_model(xgb_model)
filename, extension = latest_checkpoint.split('.')
iteration = int(extension) + 1
break
except XGBoostError:
logging.debug("Wrong checkpoint model format %s",
latest_checkpoint)
return xgb_model, iteration
class BreastCancerTrainable(Trainable):
def setup(self, config):
self.config = config
self.nthread = config.pop("nthread", 1)
self.model: xgb.Booster = None
# Load dataset
data, labels = sklearn.datasets.load_breast_cancer(return_X_y=True)
# Split into train and test set
train_x, test_x, train_y, test_y = train_test_split(
data, labels, test_size=0.25
)
# Build input matrices for XGBoost
self.train_set = xgb.DMatrix(train_x, label=train_y)
self.test_set = xgb.DMatrix(test_x, label=test_y)
def step(self):
# you can also obtain current trial resources:
current_resources = self.trial_resources
if isinstance(current_resources, PlacementGroupFactory):
self.nthread = current_resources.head_cpus
else:
self.nthread = current_resources.cpu
results = {}
config = self.config.copy()
config["nthread"] = int(self.nthread)
self.model = xgb.train(
config,
self.train_set,
evals=[(self.test_set, "eval")],
verbose_eval=False,
xgb_model=self.model,
evals_result=results,
num_boost_round=1,
)
print(config, results)
return {"eval-logloss": results["eval"]["logloss"][-1], "nthread": self.nthread}
def save_checkpoint(self, checkpoint_dir):
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "wb") as outputFile:
pickle.dump((self.config, self.nthread, self.model.save_raw()), outputFile)
return path
def load_checkpoint(self, checkpoint_path):
with open(checkpoint_path, "rb") as inputFile:
self.config, self.nthread, raw_model = pickle.load(inputFile)
self.model = Booster()
self.model.load_model(bytearray(raw_model))
data, labels = sklearn.datasets.load_breast_cancer(return_X_y=True)
# Split into train and test set
train_x, test_x, train_y, test_y = train_test_split(
data, labels, test_size=0.25
)
# Build input matrices for XGBoost
self.train_set = xgb.DMatrix(train_x, label=train_y)
self.test_set = xgb.DMatrix(test_x, label=test_y)
def deserialize_booster(ser_model_string):
"""
Deserialize an xgboost.core.Booster from the input ser_model_string.
"""
booster = Booster()
# TODO: change to use string io
tmp_file_name = os.path.join(_get_or_create_tmp_dir(),
f"{uuid.uuid4()}.json")
with open(tmp_file_name, "w", encoding="utf-8") as f:
f.write(ser_model_string)
booster.load_model(tmp_file_name)
return booster
