Ray train job gets killed with no errors!

I tried to run a single XGBoost training job but the job gets killed without any errors/exceptions. checked the logs but it seems nothing is wrong. How should I find the problem? Is there any way to increase the log level?
My script and screenshots from the terminal and Ray’s dashboard are attached.

Code:

# Load data.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")

# Split data into train and validation.
train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)

scaling_config = ScalingConfig(
    # Number of workers to use for data parallelism.
    num_workers=2,
    # Whether to use GPU acceleration.
    use_gpu=False,
)

sync_config = SyncConfig(
    syncer=CustomCommandSyncer(
        sync_up_template="aws s3 --endpoint-url=$AWS_ENDPOINT_URL  sync {source} {target}",
        sync_down_template="aws s3 --endpoint-url=$AWS_ENDPOINT_URL sync {source} {target}",
        delete_template="aws s3 --endpoint-url=$AWS_ENDPOINT_URL rm {target} --recursive",
    ),
)

trainer = XGBoostTrainer(
    scaling_config=scaling_config,
    run_config=RunConfig(
        sync_config=sync_config,
        verbose=1,
        storage_path="s3://mlops/milad/"
    ),
    label_column="target",
    num_boost_round=10,
    params={
        # XGBoost specific params
        "objective": "binary:logistic",
        # "tree_method": "gpu_hist",  # uncomment this to use GPU for training
        "eval_metric": ["logloss", "error"],
    },
    datasets={"train": train_dataset, "valid": valid_dataset},
)

result = trainer.fit()

print(result.metrics)

Terminal:
https://mega.nz/file/XJkjyTYb#8AFhRnFTxzojkUIHlkE-wW8q-qRSeFnE7CVJaDVOA_A

Dashboard:
https://mega.nz/file/mMNmTKDC#ePShAMUO-WCtcOkyx8zCsys9wQqT8N6DhjFP0J8ejTs

Hey it’s not clear to me from the logs that you’ve shown - can you copy/paste the full log from the Dashboard view?

Update

Thanks for the response.
The problem was insufficient memory of the head node. After I increased the memory of the head node, the problem was solved. I wonder how can we detect such problems. The logs on the dashboard don’t say anything about the memory shortage or insufficient CPU cores.

how did you increase the memory of the head node?