95aec091e4
### What changes were proposed in this pull request? As part of the Stage level scheduling features, add the Python api's to set resource profiles. This also adds the functionality to properly apply the pyspark memory configuration when specified in the ResourceProfile. The pyspark memory configuration is being passed in the task local properties. This was an easy way to get it to the PythonRunner that needs it. I modeled this off how the barrier task scheduling is passing the addresses. As part of this I added in the JavaRDD api's because those are needed by python. ### Why are the changes needed? python api for this feature ### Does this PR introduce any user-facing change? Yes adds the java and python apis for user to specify a ResourceProfile to use stage level scheduling. ### How was this patch tested? unit tests and manually tested on yarn. Tests also run to verify it errors properly on standalone and local mode where its not yet supported. Closes #28085 from tgravescs/SPARK-29641-pr-base. Lead-authored-by: Thomas Graves <tgraves@nvidia.com> Co-authored-by: Thomas Graves <tgraves@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
218 lines
7.1 KiB
Python
218 lines
7.1 KiB
Python
# -*- encoding: utf-8 -*-
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import sys
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import tempfile
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import threading
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import time
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import unittest
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has_resource_module = True
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try:
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import resource
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except ImportError:
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has_resource_module = False
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from py4j.protocol import Py4JJavaError
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from pyspark import SparkConf, SparkContext
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from pyspark.testing.utils import ReusedPySparkTestCase, PySparkTestCase, QuietTest
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if sys.version_info[0] >= 3:
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xrange = range
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class WorkerTests(ReusedPySparkTestCase):
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def test_cancel_task(self):
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temp = tempfile.NamedTemporaryFile(delete=True)
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temp.close()
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path = temp.name
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def sleep(x):
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import os
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import time
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with open(path, 'w') as f:
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f.write("%d %d" % (os.getppid(), os.getpid()))
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time.sleep(100)
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# start job in background thread
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def run():
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try:
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self.sc.parallelize(range(1), 1).foreach(sleep)
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except Exception:
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pass
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import threading
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t = threading.Thread(target=run)
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t.daemon = True
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t.start()
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daemon_pid, worker_pid = 0, 0
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while True:
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if os.path.exists(path):
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with open(path) as f:
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data = f.read().split(' ')
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daemon_pid, worker_pid = map(int, data)
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break
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time.sleep(0.1)
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# cancel jobs
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self.sc.cancelAllJobs()
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t.join()
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for i in range(50):
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try:
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os.kill(worker_pid, 0)
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time.sleep(0.1)
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except OSError:
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break # worker was killed
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else:
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self.fail("worker has not been killed after 5 seconds")
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try:
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os.kill(daemon_pid, 0)
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except OSError:
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self.fail("daemon had been killed")
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# run a normal job
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rdd = self.sc.parallelize(xrange(100), 1)
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self.assertEqual(100, rdd.map(str).count())
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def test_after_exception(self):
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def raise_exception(_):
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raise Exception()
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rdd = self.sc.parallelize(xrange(100), 1)
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with QuietTest(self.sc):
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self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
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self.assertEqual(100, rdd.map(str).count())
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def test_after_jvm_exception(self):
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tempFile = tempfile.NamedTemporaryFile(delete=False)
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tempFile.write(b"Hello World!")
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tempFile.close()
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data = self.sc.textFile(tempFile.name, 1)
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filtered_data = data.filter(lambda x: True)
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self.assertEqual(1, filtered_data.count())
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os.unlink(tempFile.name)
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with QuietTest(self.sc):
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self.assertRaises(Exception, lambda: filtered_data.count())
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rdd = self.sc.parallelize(xrange(100), 1)
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self.assertEqual(100, rdd.map(str).count())
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def test_accumulator_when_reuse_worker(self):
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from pyspark.accumulators import INT_ACCUMULATOR_PARAM
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acc1 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
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self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc1.add(x))
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self.assertEqual(sum(range(100)), acc1.value)
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acc2 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
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self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc2.add(x))
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self.assertEqual(sum(range(100)), acc2.value)
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self.assertEqual(sum(range(100)), acc1.value)
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def test_reuse_worker_after_take(self):
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rdd = self.sc.parallelize(xrange(100000), 1)
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self.assertEqual(0, rdd.first())
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def count():
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try:
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rdd.count()
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except Exception:
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pass
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t = threading.Thread(target=count)
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t.daemon = True
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t.start()
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t.join(5)
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self.assertTrue(not t.isAlive())
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self.assertEqual(100000, rdd.count())
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def test_with_different_versions_of_python(self):
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rdd = self.sc.parallelize(range(10))
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rdd.count()
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version = self.sc.pythonVer
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self.sc.pythonVer = "2.0"
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try:
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with QuietTest(self.sc):
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self.assertRaises(Py4JJavaError, lambda: rdd.count())
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finally:
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self.sc.pythonVer = version
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def test_python_exception_non_hanging(self):
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# SPARK-21045: exceptions with no ascii encoding shall not hanging PySpark.
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try:
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def f():
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raise Exception("exception with 中 and \xd6\xd0")
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self.sc.parallelize([1]).map(lambda x: f()).count()
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except Py4JJavaError as e:
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if sys.version_info.major < 3:
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# we have to use unicode here to avoid UnicodeDecodeError
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self.assertRegexpMatches(unicode(e).encode("utf-8"), "exception with 中")
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else:
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self.assertRegexpMatches(str(e), "exception with 中")
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class WorkerReuseTest(PySparkTestCase):
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def test_reuse_worker_of_parallelize_xrange(self):
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rdd = self.sc.parallelize(xrange(20), 8)
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previous_pids = rdd.map(lambda x: os.getpid()).collect()
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current_pids = rdd.map(lambda x: os.getpid()).collect()
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for pid in current_pids:
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self.assertTrue(pid in previous_pids)
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@unittest.skipIf(
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not has_resource_module,
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"Memory limit feature in Python worker is dependent on "
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"Python's 'resource' module; however, not found.")
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class WorkerMemoryTest(unittest.TestCase):
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def setUp(self):
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class_name = self.__class__.__name__
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conf = SparkConf().set("spark.executor.pyspark.memory", "2g")
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self.sc = SparkContext('local[4]', class_name, conf=conf)
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def test_memory_limit(self):
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rdd = self.sc.parallelize(xrange(1), 1)
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def getrlimit():
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import resource
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return resource.getrlimit(resource.RLIMIT_AS)
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actual = rdd.map(lambda _: getrlimit()).collect()
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self.assertTrue(len(actual) == 1)
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self.assertTrue(len(actual[0]) == 2)
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[(soft_limit, hard_limit)] = actual
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self.assertEqual(soft_limit, 2 * 1024 * 1024 * 1024)
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self.assertEqual(hard_limit, 2 * 1024 * 1024 * 1024)
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def tearDown(self):
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self.sc.stop()
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if __name__ == "__main__":
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import unittest
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from pyspark.tests.test_worker import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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