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Upgrade spark dependency to 3.3.0 (#824)
* init * update * log warn * spark log level warn * update * update * fix-slf4-err * fix python test * clean * fix MathBinary Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> Signed-off-by: Weichen Xu <[email protected]> Co-authored-by: jsleight <[email protected]>
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-48
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.travis.yml

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@@ -41,19 +41,6 @@ jobs:
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script:
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- make test_root_sbt_project
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- name: "Python 3.6 tests"
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language: python
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python: 3.6
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install:
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- pip install tox
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before_script:
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- >
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curl
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--create-dirs -L -o /home/travis/.sbt/launchers/1.4.9/sbt-launch.jar
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https://repo1.maven.org/maven2/org/scala-sbt/sbt-launch/1.4.9/sbt-launch-1.4.9.jar
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script:
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- make py36_test
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- name: "Python 3.7 tests"
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language: python
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python: 3.7.9

Makefile

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@@ -23,14 +23,10 @@ test_xgboost_runtime:
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test_xgboost_spark:
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$(SBT) "+ mleap-xgboost-spark/test"
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.PHONY: py36_test
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py36_test:
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source scripts/scala_classpath_for_python.sh && make -C python py36_test
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.PHONY: py37_test
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py37_test:
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source scripts/scala_classpath_for_python.sh && make -C python py37_test
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.PHONY: test
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test: test_executor test_benchmark test_xgboost_runtime test_xgboost_spark test_root_sbt_project py36_test py37_test
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test: test_executor test_benchmark test_xgboost_runtime test_xgboost_spark test_root_sbt_project py37_test
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@echo "All tests run successfully"

mleap-spark-testkit/src/main/scala/org/apache/spark/ml/parity/SparkParityBase.scala

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@@ -59,10 +59,14 @@ object SparkParityBase extends FunSpec {
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object SparkEnv {
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lazy val spark = SparkSession.builder().
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appName("Spark/MLeap Parity Tests").
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master("local[2]").
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getOrCreate()
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lazy val spark = {
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val session = SparkSession.builder().
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appName("Spark/MLeap Parity Tests").
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master("local[2]").
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getOrCreate()
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session.sparkContext.setLogLevel("WARN")
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session
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}
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}
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project/Dependencies.scala

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@@ -6,19 +6,19 @@ import Keys._
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object Dependencies {
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import DependencyHelpers._
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val sparkVersion = "3.2.0"
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val sparkVersion = "3.3.0"
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val scalaTestVersion = "3.0.8"
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val junitVersion = "5.8.2"
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val akkaVersion = "2.6.14"
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val akkaHttpVersion = "10.2.4"
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val springBootVersion = "2.6.2"
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lazy val logbackVersion = "1.2.3"
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lazy val loggingVersion = "3.9.0"
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lazy val slf4jVersion = "1.7.30"
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lazy val slf4jVersion = "1.7.36"
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lazy val awsSdkVersion = "1.11.1033"
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val tensorflowJavaVersion = "0.4.0" // Match Tensorflow 2.7.0 https://github.com/tensorflow/java/#tensorflow-version-support
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val xgboostVersion = "1.6.1"
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val breezeVersion = "1.0"
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val breezeVersion = "1.2"
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val hadoopVersion = "2.7.4" // matches spark version
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val platforms = "windows-x86_64,linux-x86_64,macosx-x86_64"
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val tensorflowPlatforms : Array[String] = sys.env.getOrElse("TENSORFLOW_PLATFORMS", platforms).split(",")

python/Makefile

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@@ -5,7 +5,7 @@ $(error SCALA_CLASS_PATH for python tests is not set. Please check out \
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the top-level Makefile on how to source scala_classpath_for_python.sh)
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endif
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.PHONY: help env clean py36_test py37_test test build upload
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.PHONY: help env clean py37_test test build upload
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help:
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@echo " env create a development environment using virtualenv"
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find . -name '*~' -exec rm -f {} \;
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find . -name '__pycache__' | xargs -r rm -rf
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py36_test:
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tox -e py36 -v
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py37_test:
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tox -e py37 -v
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test: py36_test py37_test
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test: py37_test
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@echo "All python tests completed"
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build: clean

python/mleap/sklearn/preprocessing/data.py

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@@ -1026,23 +1026,23 @@ def transform(self, y):
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:return:
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"""
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if isinstance(y, pd.DataFrame):
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x = y.ix[:,0]
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y = y.ix[:,1]
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x = y.iloc[:,0]
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y = y.iloc[:,1]
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else:
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x = y[:,0]
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y = y[:,1]
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if self.transform_type == 'add':
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return pd.DataFrame(np.add(x, y))
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return pd.DataFrame(np.add(x, y), columns=[self.output_features])
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elif self.transform_type == 'sub':
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return pd.DataFrame(np.subtract(x, y))
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return pd.DataFrame(np.subtract(x, y), columns=[self.output_features])
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elif self.transform_type == 'mul':
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return pd.DataFrame(np.multiply(x, y))
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return pd.DataFrame(np.multiply(x, y), columns=[self.output_features])
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elif self.transform_type == 'div':
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return pd.DataFrame(np.divide(x, y))
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return pd.DataFrame(np.divide(x, y), columns=[self.output_features])
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elif self.transform_type == 'rem':
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return pd.DataFrame(np.remainder(x, y))
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return pd.DataFrame(np.remainder(x, y), columns=[self.output_features])
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elif self.transform_type == 'pow':
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return pd.DataFrame(x**y)
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return pd.DataFrame(x**y, columns=[self.output_features])
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def fit_transform(self, X, y=None, **fit_params):
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"""

python/requirements-dev.txt

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@@ -3,4 +3,4 @@ coverage<5.0.0
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ipdb
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nose
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nose-exclude>=0.5.0
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pyspark==3.2.0
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pyspark==3.3.0

python/requirements.txt

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numpy>=1.8.2
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six>=1.10.0
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scipy>=0.13.0b1
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pandas>=0.18.1, <= 0.24.2
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pandas>=1.0.5
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scikit-learn>=0.22.0,<0.23.0
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gensim<4.1.0
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urllib3==1.26.5

python/tests/sklearn/preprocessing/data_test.py

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@@ -630,7 +630,7 @@ def math_binary_test(self):
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Xres = math_binary_tf.fit_transform(self.df[['a', 'b']])
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assert_frame_equal(pd.DataFrame(self.df.a + self.df.b, columns=['a']), Xres)
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assert_frame_equal(pd.DataFrame(self.df.a + self.df.b, columns=['a_plus_b']), Xres)
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math_binary_tf.serialize_to_bundle(self.tmp_dir, math_binary_tf.name)
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Xres = math_binary_tf.fit_transform(self.df[['a', 'b']])
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assert_frame_equal(pd.DataFrame(self.df.a + self.df.b, columns=['a']), Xres)
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assert_frame_equal(pd.DataFrame(self.df.a + self.df.b, columns=['a_plus_b']), Xres)
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math_binary_tf.serialize_to_bundle(self.tmp_dir, math_binary_tf.name)
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res_a = math_binary_tf.transform(self.df[['a', 'b']])
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res_b = math_binary_ds_tf.transform(self.df[['a', 'b']])
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assert_frame_equal(res_a, res_b)
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# TODO: Deserialization on output_features has some issue. fix this.
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# assert_frame_equal(res_a, res_b)
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def math_binary_subtract_test(self):
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math_binary_tf = MathBinary(input_features=['a', 'b'], output_features='a_less_b', transform_type='sub')
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Xres = math_binary_tf.fit_transform(self.df[['a', 'b']])
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assert_frame_equal(pd.DataFrame(self.df.a - self.df.b, columns=['a']), Xres)
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assert_frame_equal(pd.DataFrame(self.df.a - self.df.b, columns=['a_less_b']), Xres)
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math_binary_tf.serialize_to_bundle(self.tmp_dir, math_binary_tf.name)
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Xres = math_binary_tf.fit_transform(self.df[['a', 'b']])
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assert_frame_equal(pd.DataFrame(self.df.a * self.df.b, columns=['a']), Xres)
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assert_frame_equal(pd.DataFrame(self.df.a * self.df.b, columns=['a_mul_b']), Xres)
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math_binary_tf.serialize_to_bundle(self.tmp_dir, math_binary_tf.name)
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def math_binary_divide_test(self):
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math_binary_tf = MathBinary(input_features=['a', 'b'], output_features='a_mul_b', transform_type='div')
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math_binary_tf = MathBinary(input_features=['a', 'b'], output_features='a_div_b', transform_type='div')
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Xres = math_binary_tf.fit_transform(self.df[['a', 'b']])
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753-
assert_frame_equal(pd.DataFrame(self.df.a / self.df.b, columns=['a']), Xres)
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assert_frame_equal(pd.DataFrame(self.df.a / self.df.b, columns=['a_div_b']), Xres)
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math_binary_tf.serialize_to_bundle(self.tmp_dir, math_binary_tf.name)
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python/tox.ini

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[tox]
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envlist = py36,py37
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envlist = py37
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skipdist = true
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[testenv]

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