sagemaker xgboost estimator fail to fit

I used the training script from https://sagemaker.readthedocs.io/en/stable/frameworks/xgboost/using_xgboost.html,and trying to train the model. And the here is the code I used for configuring the model. ` sess = sagemaker.Session() hyperparams = { "max_depth": "5", "eta": "0.2", "gamma": "4", "min_child_weight": "6", "subsample": "0.7", "objective": "reg:squarederror", "num_round": "50", "verbosity": "2"}

instance_type = "ml.m5.2xlarge"
output_path = f's3://{bucket}/Model-Resource-Production/dr_training_folder'
content_type = "libsvm"

# Create XGBoost estimator
xgboost_estimator = XGBoost(
    entry_point="script.py",
    framework_version="1.7-1",  # Note: framework_version is mandatory
    hyperparameters=hyperparams,
    role=role,
    instance_count=1,
    instance_type=instance_type,
    output_path=output_path,)

file_path = 'Model-Resource-Production/dr_training_folder'

# Read train test file from S3
training_input_config = TrainingInput(s3_data=f's3://{bucket}/{file_path}/DR_train', content_type='csv')
validation_input_config = TrainingInput(s3_data=f's3://{bucket}/{file_path}/DR_valid', content_type='csv')

# Fit the model
xgboost_estimator.fit({'train': training_input_config, 'validation': validation_input_config})`

However, it returns the error during the "Training in progress" and here is the error: ` sess = sagemaker.Session() hyperparams = { "max_depth": "5", "eta": "0.2", "gamma": "4", "min_child_weight": "6", "subsample": "0.7", "objective": "reg:squarederror", "num_round": "50", "verbosity": "2"}

instance_type = "ml.m5.2xlarge"
output_path = f's3://{bucket}/Model-Resource-Production/dr_training_folder'
content_type = "libsvm"

# Create XGBoost estimator
xgboost_estimator = XGBoost(
    entry_point="script.py",
    framework_version="1.7-1",  # Note: framework_version is mandatory
    hyperparameters=hyperparams,
    role=role,
    instance_count=1,
    instance_type=instance_type,
    output_path=output_path,)

file_path = 'Model-Resource-Production/dr_training_folder'

# Read train test file from S3
training_input_config = TrainingInput(s3_data=f's3://{bucket}/{file_path}/DR_train', content_type='csv')
validation_input_config = TrainingInput(s3_data=f's3://{bucket}/{file_path}/DR_valid', content_type='csv')

# Fit the model
xgboost_estimator.fit({'train': training_input_config, 'validation': validation_input_config})`

`--------------------------------------------------------------------------- UnexpectedStatusException Traceback (most recent call last) Cell In[4], line 71 68 validation_input_config = TrainingInput(s3_data=f's3://{bucket}/{file_path}/DR_valid', content_type='csv') 70 # Fit the model ---> 71 xgboost_estimator.fit({'train': training_input_config, 'validation': validation_input_config})

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/pipeline_context.py:284, in runnable_by_pipeline.<locals>.wrapper(*args, **kwargs) 280 return context 282 return _StepArguments(retrieve_caller_name(self_instance), run_func, *args, **kwargs) --> 284 return run_func(*args, **kwargs)

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/estimator.py:1198, in EstimatorBase.fit(self, inputs, wait, logs, job_name, experiment_config) 1196 self.jobs.append(self.latest_training_job) 1197 if wait: -> 1198 self.latest_training_job.wait(logs=logs)

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/estimator.py:2344, in _TrainingJob.wait(self, logs) 2342 # If logs are requested, call logs_for_jobs. 2343 if logs != "None": -> 2344 self.sagemaker_session.logs_for_job(self.job_name, wait=True, log_type=logs) 2345 else: 2346 self.sagemaker_session.wait_for_job(self.job_name)

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:4756, in Session.logs_for_job(self, job_name, wait, poll, log_type) 4753 last_profiler_rule_statuses = profiler_rule_statuses 4755 if wait: -> 4756 self._check_job_status(job_name, description, "TrainingJobStatus") 4757 if dot: 4758 print()

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:4263, in Session._check_job_status(self, job, desc, status_key_name) 4257 if "CapacityError" in str(reason): 4258 raise exceptions.CapacityError( 4259 message=message, 4260 allowed_statuses=["Completed", "Stopped"], 4261 actual_status=status, 4262 ) -> 4263 raise exceptions.UnexpectedStatusException( 4264 message=message, 4265 allowed_statuses=["Completed", "Stopped"], 4266 actual_status=status, 4267 )

UnexpectedStatusException: Error for Training job sagemaker-xgboost-2023-04-18-13-58-00-099: Failed. Reason: AlgorithmError: ExecuteUserScriptError: Command "/miniconda3/bin/python3 -m script --eta 0.2 --gamma 4 --max_depth 5 --min_child_weight 6 --num_round 50 --objective reg:squarederror --subsample 0.7 --verbosity 2", exit code: 1`

It seems some problems during passing the hyperparameters but I don't know what actually is going on. Thanks so much in advance.