the num of estimators in random forest test is hard coded .

[johnnyzhon]

test environment:
there are 4 V100 gpu

detail:
tests/test_random_forest.py:499:

---

/root/miniconda3/lib/python3.9/site-packages/pyspark/ml/base.py:205: in fit
return self._fit(dataset)
src/spark_rapids_ml/core.py:812: in _fit
return self._fit_internal(dataset, None)[0]
src/spark_rapids_ml/core.py:781: in _fit_internal
pipelined_rdd = self._call_cuml_fit_func(
src/spark_rapids_ml/core.py:487: in _call_cuml_fit_func
cuml_fit_func = self._get_cuml_fit_func(
src/spark_rapids_ml/tree.py:287: in _get_cuml_fit_func
n_estimators_of_all_params.append(self._estimators_per_worker(num_trees))

---

self = RandomForestRegressor_5def7d11920a, n_estimators = 3

def _estimators_per_worker(self, n_estimators: int) -> List[int]:
    """Calculate the number of trees each task should train according to n_estimators"""
    n_workers = self.num_workers
    if n_estimators < n_workers:

      raise ValueError("n_estimators cannot be lower than number of spark tasks.")

E ValueError: n_estimators cannot be lower than number of spark tasks.