I used the training script from [https://sagemaker.readthedocs.io/en/stable/frameworks/xgboost/using_xgboost.html](here),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.

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sagemaker xgboost estimator fail to fit