conduct replacements for all of distutils.version

docs/conf.py

@@ -62,7 +62,7 @@
     'members': None,
     'member-order': 'bysource',
     'imported-members': None,
-    'exclude-members': 'contextmanager, LooseVersion, tf, keras, torch, mx, pyspark',
+    'exclude-members': 'contextmanager, version.parse, tf, keras, torch, mx, pyspark',
 }
 
 

examples/elastic/tensorflow2/tensorflow2_keras_mnist_elastic.py

@@ -16,7 +16,7 @@
 import argparse
 import tensorflow as tf
 import horovod.tensorflow.keras as hvd
-from distutils.version import LooseVersion
+from packaging import version
 
 parser = argparse.ArgumentParser(description='Tensorflow 2.0 Keras MNIST Example')
 
@@ -26,7 +26,7 @@
 args = parser.parse_args()
 
 if args.use_mixed_precision:
-    if LooseVersion(tf.__version__) >= LooseVersion('2.4.0'):
+    if version.parse(tf.__version__) >= version.parse('2.4.0'):
         from tensorflow.keras import mixed_precision
         mixed_precision.set_global_policy('mixed_float16')
     else:

examples/pytorch/pytorch_mnist.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from distutils.version import LooseVersion
+from packaging import version
 
 import torch.multiprocessing as mp
 import torch.nn as nn
@@ -161,8 +161,8 @@ def test():
         if args.use_mixed_precision:
             raise ValueError("Mixed precision is only supported with cuda enabled.")
 
-    if (args.use_mixed_precision and LooseVersion(torch.__version__)
-            < LooseVersion('1.6.0')):
+    if (args.use_mixed_precision and version.parse(torch.__version__)
+            < version.parse('1.6.0')):
         raise ValueError("""Mixed precision is using torch.cuda.amp.autocast(),
                             which requires torch >= 1.6.0""")
 

examples/spark/keras/keras_spark_mnist.py

@@ -2,15 +2,15 @@
 import os
 import subprocess
 import sys
-from distutils.version import LooseVersion
+from packaging import version
 
 import numpy as np
 
 import pyspark
 import pyspark.sql.types as T
 from pyspark import SparkConf
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-if LooseVersion(pyspark.__version__) < LooseVersion('3.0.0'):
+if version.parse(pyspark.__version__) < version.parse('3.0.0'):
     from pyspark.ml.feature import OneHotEncoderEstimator as OneHotEncoder
 else:
     from pyspark.ml.feature import OneHotEncoder
@@ -78,7 +78,7 @@
     train_df, test_df = train_df.randomSplit([0.9, 0.1])
 
     # Disable GPUs when building the model to prevent memory leaks
-    if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
+    if version.parse(tf.__version__) >= version.parse('2.0.0'):
         # See https://github.com/tensorflow/tensorflow/issues/33168
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
     else:

examples/spark/keras/keras_spark_rossmann_estimator.py

@@ -18,7 +18,7 @@
 import datetime
 import os
 import sys
-from distutils.version import LooseVersion
+from packaging import version
 
 import pyspark.sql.types as T
 import pyspark.sql.functions as F
@@ -338,7 +338,7 @@ def act_sigmoid_scaled(x):
                       'act_sigmoid_scaled': act_sigmoid_scaled}
 
     # Disable GPUs when building the model to prevent memory leaks
-    if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
+    if version.parse(tf.__version__) >= version.parse('2.0.0'):
         # See https://github.com/tensorflow/tensorflow/issues/33168
         os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
     else:

examples/spark/pytorch/pytorch_lightning_spark_mnist.py

@@ -2,15 +2,15 @@
 import os
 import subprocess
 import sys
-from distutils.version import LooseVersion
+from packaging import version
 
 import numpy as np
 
 import pyspark
 import pyspark.sql.types as T
 from pyspark import SparkConf
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-if LooseVersion(pyspark.__version__) < LooseVersion('3.0.0'):
+if version.parse(pyspark.__version__) < version.parse('3.0.0'):
     from pyspark.ml.feature import OneHotEncoderEstimator as OneHotEncoder
 else:
     from pyspark.ml.feature import OneHotEncoder
@@ -21,8 +21,8 @@
 try:
     # tensorflow has to be imported BEFORE pytorch_lightning, otherwise we see the segfault right away
     import tensorflow as tf
-    from distutils.version import LooseVersion
-    if LooseVersion('2.5.0') <= LooseVersion(tf.__version__) < LooseVersion('2.7.0'):
+    from packaging import version
+    if version.parse('2.5.0') <= version.parse(tf.__version__) < version.parse('2.7.0'):
         print('Skipping test as Pytorch Lightning conflicts with present Tensorflow 2.6.x', file=sys.stderr)
         sys.exit(0)
 except ImportError:
@@ -61,7 +61,7 @@
 def train_model(args):
     # do not run this test for pytorch lightning below min supported verson
     import pytorch_lightning as pl
-    if LooseVersion(pl.__version__) < LooseVersion(MIN_PL_VERSION):
+    if version.parse(pl.__version__) < version.parse(MIN_PL_VERSION):
         print("Skip test for pytorch_ligthning=={}, min support version is {}".format(pl.__version__, MIN_PL_VERSION))
         return
 

examples/spark/pytorch/pytorch_lightning_spark_mnist_legacy.py

@@ -2,15 +2,15 @@
 import os
 import subprocess
 import sys
-from distutils.version import LooseVersion
+from packaging import version
 
 import numpy as np
 
 import pyspark
 import pyspark.sql.types as T
 from pyspark import SparkConf
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-if LooseVersion(pyspark.__version__) < LooseVersion('3.0.0'):
+if version.parse(pyspark.__version__) < version.parse('3.0.0'):
     from pyspark.ml.feature import OneHotEncoderEstimator as OneHotEncoder
 else:
     from pyspark.ml.feature import OneHotEncoder
@@ -45,7 +45,7 @@
 def train_model(args):
     # do not run this test for pytorch lightning below min supported verson
     import pytorch_lightning as pl
-    if LooseVersion(pl.__version__) < LooseVersion(MIN_PL_VERSION):
+    if version.parse(pl.__version__) < version.parse(MIN_PL_VERSION):
         print("Skip test for pytorch_ligthning=={}, min support version is {}".format(pl.__version__, MIN_PL_VERSION))
         return
 

examples/spark/pytorch/pytorch_spark_mnist.py

@@ -2,15 +2,15 @@
 import os
 import subprocess
 import sys
-from distutils.version import LooseVersion
+from packaging import version
 
 import numpy as np
 
 import pyspark
 import pyspark.sql.types as T
 from pyspark import SparkConf
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-if LooseVersion(pyspark.__version__) < LooseVersion('3.0.0'):
+if version.parse(pyspark.__version__) < version.parse('3.0.0'):
     from pyspark.ml.feature import OneHotEncoderEstimator as OneHotEncoder
 else:
     from pyspark.ml.feature import OneHotEncoder

horovod/_keras/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-from distutils.version import LooseVersion
+from packaging import version
 
 import horovod.tensorflow as hvd
 import tensorflow as tf
@@ -22,7 +22,7 @@
 from horovod.tensorflow.mpi_ops import rank
 
 
-_PRE_TF_2_4_0 = LooseVersion(tf.__version__) < LooseVersion('2.4.0')
+_PRE_TF_2_4_0 = version.parse(tf.__version__) < version.parse('2.4.0')
 
 
 def create_distributed_optimizer(keras, optimizer, name, device_dense, device_sparse,

horovod/_keras/callbacks.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-from distutils.version import LooseVersion
+from packaging import version
 import warnings
 
 import horovod.tensorflow as hvd
@@ -54,7 +54,7 @@ def __init__(self, backend, device='', *args):
         self.allreduce_ops = {}
         self.device = device
 
-        if LooseVersion("2.3") <= LooseVersion(tf.__version__) < LooseVersion("2.5"):
+        if version.parse("2.3") <= version.parse(tf.__version__) < version.parse("2.5"):
             warnings.warn(
                 "Some callbacks may not have access to the averaged metrics, "
                 "see https://github.com/horovod/horovod/issues/2440")

horovod/common/util.py

@@ -269,7 +269,7 @@ def is_iterable(x):
 
 @_cache
 def is_version_greater_equal_than(ver, target):
-    from distutils.version import LooseVersion
+    from packaging import version
     if any([not isinstance(_str, str) for _str in (ver, target)]):
         raise ValueError("This function only accepts string arguments. \n"
                          "Received:\n"
@@ -285,4 +285,4 @@ def is_version_greater_equal_than(ver, target):
         raise ValueError("We only accepts target version values in the form "
                          "of: major.minor.patch. Received: {}".format(target))
 
-    return LooseVersion(ver) >= LooseVersion(target)
+    return version.parse(ver) >= version.parse(target)

horovod/spark/common/store.py

@@ -22,7 +22,7 @@
 import tempfile
 import warnings
 
-from distutils.version import LooseVersion
+from packaging import version
 
 import pyarrow as pa
 import pyarrow.parquet as pq
@@ -202,7 +202,7 @@ def read_serialized_keras_model(self, ckpt_path, model, custom_objects):
         from tensorflow import keras
         from horovod.spark.keras.util import TFKerasUtil
 
-        if LooseVersion(tensorflow.__version__) < LooseVersion("2.0.0"):
+        if version.parse(tensorflow.__version__) < version.parse("2.0.0"):
             model_bytes = self.read(ckpt_path)
             return codec.dumps_base64(model_bytes)
         else:
@@ -429,7 +429,7 @@ def __init__(self, prefix_path,
                                  user=user,
                                  kerb_ticket=kerb_ticket,
                                  extra_conf=extra_conf)
-        if LooseVersion(pa.__version__) < LooseVersion('0.17.0'):
+        if version.parse(pa.__version__) < version.parse('0.17.0'):
             self._hdfs_kwargs['driver'] = driver
         self._hdfs = self._get_filesystem_fn()()
 
@@ -609,7 +609,7 @@ def read_serialized_keras_model(self, ckpt_path, model, custom_objects):
         from tensorflow import keras
         from horovod.spark.keras.util import TFKerasUtil
 
-        if LooseVersion(tensorflow.__version__) < LooseVersion("2.0.0"):
+        if version.parse(tensorflow.__version__) < version.parse("2.0.0"):
             model.load_weights(ckpt_path)
         else:
             with keras.utils.custom_object_scope(custom_objects):

horovod/spark/keras/remote.py

@@ -22,7 +22,7 @@
 import h5py
 import tensorflow as tf
 
-from distutils.version import LooseVersion
+from packaging import version
 from horovod.spark.common import constants
 from horovod.spark.common.store import DBFSLocalStore
 from horovod.spark.common.util import _get_assigned_gpu_or_default, _set_mp_start_method
@@ -126,7 +126,7 @@ def train(serialized_model, train_rows, val_rows, avg_row_size):
                 print("Skip pinning current process to the GPU.")
 
         if random_seed is not None:
-            if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
+            if version.parse(tf.__version__) < version.parse('2.0.0'):
                 tf.random.set_random_seed(random_seed)
             else:
                 tf.random.set_seed(random_seed)
@@ -176,7 +176,7 @@ def train(serialized_model, train_rows, val_rows, avg_row_size):
                 if _checkpoint_callback:
                     _checkpoint_callback.filepath = ckpt_file
                 else:
-                    if is_dbfs and LooseVersion(tf.__version__) < LooseVersion("2.0.0"):
+                    if is_dbfs and version.parse(tf.__version__) < version.parse("2.0.0"):
                         # Because DBFS local file APIs does not support random write which is
                         # required by h5 format, save_weights_only=True is needed for switching
                         # to the TensorFlow SavedModel format.
@@ -270,15 +270,15 @@ def train(serialized_model, train_rows, val_rows, avg_row_size):
 
             if hvd.rank() == 0:
                 if is_dbfs:
-                    if LooseVersion(tf.__version__) < LooseVersion("2.0.0"):
+                    if version.parse(tf.__version__) < version.parse("2.0.0"):
                         model.load_weights(ckpt_file)
                     else:
                         # needs to be deserialized in the with scope
                         with k.utils.custom_object_scope(custom_objects):
                             model = k.models.load_model(ckpt_file)
                     serialized_model = keras_utils.serialize_model(model)
                 else:
-                    if LooseVersion(tf.__version__) >= LooseVersion("2.0.0"):
+                    if version.parse(tf.__version__) >= version.parse("2.0.0"):
                         with k.utils.custom_object_scope(custom_objects):
                             model = k.models.load_model(ckpt_file)
                         serialized_model = keras_utils.serialize_model(model)
@@ -302,7 +302,7 @@ def deserialize_keras_model(model_bytes, load_model_fn):
 
 def _pin_gpu_fn():
     # Horovod: pin GPU to be used to process local rank (one GPU per process)
-    return _pin_gpu_tensorflow2_fn() if LooseVersion(tf.__version__) >= LooseVersion('2.0.0') \
+    return _pin_gpu_tensorflow2_fn() if version.parse(tf.__version__) >= version.parse('2.0.0') \
         else _pin_gpu_tensorflow1_fn()
 
 
@@ -328,7 +328,7 @@ def fn(hvd, tf, keras):
 
 
 def _pin_cpu_fn():
-    return _pin_cpu_tensorflow2_fn() if LooseVersion(tf.__version__) >= LooseVersion('2.0.0') \
+    return _pin_cpu_tensorflow2_fn() if version.parse(tf.__version__) >= version.parse('2.0.0') \
         else _pin_cpu_tensorflow1_fn()
 
 

horovod/spark/keras/util.py

@@ -16,7 +16,7 @@
 
 import io
 
-from distutils.version import LooseVersion
+from packaging import version
 
 import h5py
 import numpy as np

horovod/spark/lightning/estimator.py

@@ -21,7 +21,7 @@
 import os
 import time
 import warnings
-from distutils.version import LooseVersion
+from packaging import version
 
 from pyspark import keyword_only
 from pyspark.ml.param.shared import Param, Params, TypeConverters
@@ -298,7 +298,7 @@ def __init__(self,
         kwargs = self._input_kwargs
 
         # pl version check
-        if LooseVersion(pl.__version__) < LooseVersion(MIN_PL_VERSION):
+        if version.parse(pl.__version__) < version.parse(MIN_PL_VERSION):
             raise RuntimeError("Only support pytorch_lightning version > {}, found version {}".format(MIN_PL_VERSION, pl.__version__))
 
         if EstimatorParams.loss.name in kwargs and TorchEstimator.loss_constructors.name in kwargs:

horovod/spark/lightning/remote.py

@@ -19,7 +19,7 @@
 import tempfile
 import math
 import warnings
-from distutils.version import LooseVersion
+from packaging import version
 
 import torch
 import pytorch_lightning as pl

horovod/spark/task/task_service.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-from distutils.version import LooseVersion
+from packaging import version
 
 import os
 import pyspark
@@ -96,7 +96,7 @@ def _handle(self, req, client_address):
         return super(SparkTaskService, self)._handle(req, client_address)
 
     def _get_resources(self):
-        if LooseVersion(pyspark.__version__) >= LooseVersion('3.0.0'):
+        if version.parse(pyspark.__version__) >= version.parse('3.0.0'):
             task_context = pyspark.TaskContext.get()
             if task_context:
                 return task_context.resources()

horovod/tensorflow/__init__.py

@@ -16,7 +16,7 @@
 # ==============================================================================
 # pylint: disable=g-short-docstring-punctuation
 
-from distutils.version import LooseVersion
+from packaging import version
 import os
 import warnings
 
@@ -45,7 +45,7 @@
 from horovod.tensorflow.gradient_aggregation import LocalGradientAggregationHelper
 
 import tensorflow as tf
-_IS_TF2 = LooseVersion(tf.__version__) >= LooseVersion('2.0.0')
+_IS_TF2 = version.parse(tf.__version__) >= version.parse('2.0.0')
 
 # @DEKHTIARJonathan: Do not remove, this fixes issues:
 # - https://github.com/tensorflow/tensorflow/issues/38516