[BUG] cudf_udf nightly cudf import rmm failed

[pxLi]

Describe the bug
conda install seems inconsistent sometimes it could be
cudf-22.02.00a211129 cuda_11_py38_g0f7c5328db_101
and sometimes its
cudf-22.02.00a211202 cuda_11_py38_gb848dd5c9c_142

if cuda_11_py38_g0f7c5328db_101, it would fail to import rmm in test

21/12/03 00:59:34 ERROR Executor: Exception in task 5.0 in stage 1.0 (TID 11)
java.io.EOFException
	at java.io.DataInputStream.readInt(DataInputStream.java:392)
	at org.apache.spark.api.python.PythonWorkerFactory.createSocket$1(PythonWorkerFactory.scala:120)
	at org.apache.spark.api.python.PythonWorkerFactory.liftedTree1$1(PythonWorkerFactory.scala:136)
	at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:135)
	at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:105)
	at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)
	at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:131)
	at org.apache.spark.sql.rapids.execution.python.GpuArrowEvalPythonExec.$anonfun$doExecuteColumnar$2(GpuArrowEvalPythonExec.scala:618)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:837)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:837)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
21/12/03 00:59:34 INFO CoarseGrainedExecutorBackend: Got assigned task 12
21/12/03 00:59:34 INFO Executor: Running task 5.1 in stage 1.0 (TID 12)
Traceback (most recent call last):
  File "/home/jenkins/agent/workspace/jenkins-rapids_cudf_udf-dev-github-21-cuda11.0/jars/rapids-4-spark_2.12-22.02.0-SNAPSHOT.jar/rapids/daemon.py", line 131, in manager
  File "/home/jenkins/agent/workspace/jenkins-rapids_cudf_udf-dev-github-21-cuda11.0/jars/rapids-4-spark_2.12-22.02.0-SNAPSHOT.jar/rapids/worker.py", line 37, in initialize_gpu_mem
    from cudf import rmm
  File "/opt/conda/lib/python3.8/site-packages/cudf/__init__.py", line 12, in <module>
    from cudf import api, core, datasets, testing
  File "/opt/conda/lib/python3.8/site-packages/cudf/datasets.py", line 5, in <module>
    from cudf._lib.transform import bools_to_mask
ImportError: /opt/conda/lib/python3.8/site-packages/cudf/_lib/transform.cpython-38-x86_64-linux-gnu.so: undefined symbol: _ZN4cudf21generalized_masked_opERKNS_10table_viewERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS_9data_typeEPN3rmm2mr22device_memory_resourceE
21/12/03 00:59:35 ERROR Executor: Exception in task 4.0 in stage 1.0 (TID 10)

[pxLi]

I am going to try use mamba to see if this could resolve the inconsistent versions issue