fix: remove deprecated distutils (#4837)

* fix: remove deprecated distutils imports by local mode

* fix: remove deprecated distutils api calls in workflows

Also remove from tests.

Closes #4534

* fix pytorch

* use latest py_version

* fix format

* use latest default sklearn

* unit test

* unit test

---------

Co-authored-by: Justin Mahlik <[email protected]>

Author: benieric

.pylintrc

@@ -310,7 +310,7 @@ ignore-mixin-members=yes
 # (useful for modules/projects where namespaces are manipulated during runtime
 # and thus existing member attributes cannot be deduced by static analysis. It
 # supports qualified module names, as well as Unix pattern matching.
-ignored-modules=distutils
+ignored-modules=
 
 # List of class names for which member attributes should not be checked (useful
 # for classes with dynamically set attributes). This supports the use of

src/sagemaker/local/image.py

@@ -30,7 +30,6 @@
 import tarfile
 import tempfile
 
-from distutils.spawn import find_executable
 from threading import Thread
 from typing import Dict, List
 from six.moves.urllib.parse import urlparse
@@ -170,7 +169,7 @@ def _get_compose_cmd_prefix():
             compose_cmd_prefix.extend(["docker", "compose"])
             return compose_cmd_prefix
 
-        if find_executable("docker-compose") is not None:
+        if shutil.which("docker-compose") is not None:
             logger.info("'Docker Compose' found using Docker Compose CLI.")
             compose_cmd_prefix.extend(["docker-compose"])
             return compose_cmd_prefix

src/sagemaker/local/utils.py

@@ -21,7 +21,6 @@
 import re
 import errno
 
-from distutils.dir_util import copy_tree
 from six.moves.urllib.parse import urlparse
 
 from sagemaker import s3
@@ -102,7 +101,7 @@ def move_to_destination(source, destination, job_name, sagemaker_session, prefix
 
 
 def recursive_copy(source, destination):
-    """A wrapper around distutils.dir_util.copy_tree.
+    """A wrapper around shutil.copy_tree.
 
     This won't throw any exception when the source directory does not exist.
 
@@ -111,7 +110,7 @@ def recursive_copy(source, destination):
         destination (str): destination path
     """
     if os.path.isdir(source):
-        copy_tree(source, destination)
+        shutil.copytree(source, destination, dirs_exist_ok=True)
 
 
 def kill_child_processes(pid):

src/sagemaker/workflow/_repack_model.py

@@ -27,13 +27,6 @@
 # is unpacked for inference, the custom entry point will be used.
 # Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/amazon-sagemaker-toolkits.html
 
-# distutils.dir_util.copy_tree works way better than the half-baked
-# shutil.copytree which bombs on previously existing target dirs...
-# alas ... https://bugs.python.org/issue10948
-# we'll go ahead and use the copy_tree function anyways because this
-# repacking is some short-lived hackery, right??
-from distutils.dir_util import copy_tree
-
 from os.path import abspath, realpath, dirname, normpath, join as joinpath
 
 logger = logging.getLogger(__name__)
@@ -188,7 +181,7 @@ def repack(inference_script, model_archive, dependencies=None, source_dir=None):
 
         # copy the "src" dir, which includes the previous training job's model and the
         # custom inference script, to the output of this training job
-        copy_tree(src_dir, "/opt/ml/model")
+        shutil.copytree(src_dir, "/opt/ml/model", dirs_exist_ok=True)
 
 
 if __name__ == "__main__":  # pragma: no cover

src/sagemaker/workflow/_utils.py

@@ -46,7 +46,7 @@
 
 logger = logging.getLogger(__name__)
 
-FRAMEWORK_VERSION = "0.23-1"
+FRAMEWORK_VERSION = "1.2-1"
 INSTANCE_TYPE = "ml.m5.large"
 REPACK_SCRIPT = "_repack_model.py"
 REPACK_SCRIPT_LAUNCHER = "_repack_script_launcher.sh"

tests/data/_repack_model.py

@@ -26,13 +26,6 @@
 # is unpacked for inference, the custom entry point will be used.
 # Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/amazon-sagemaker-toolkits.html
 
-# distutils.dir_util.copy_tree works way better than the half-baked
-# shutil.copytree which bombs on previously existing target dirs...
-# alas ... https://bugs.python.org/issue10948
-# we'll go ahead and use the copy_tree function anyways because this
-# repacking is some short-lived hackery, right??
-from distutils.dir_util import copy_tree
-
 
 def repack(inference_script, model_archive, dependencies=None, source_dir=None):  # pragma: no cover
     """Repack custom dependencies and code into an existing model TAR archive
@@ -92,7 +85,7 @@ def repack(inference_script, model_archive, dependencies=None, source_dir=None):
 
         # copy the "src" dir, which includes the previous training job's model and the
         # custom inference script, to the output of this training job
-        copy_tree(src_dir, "/opt/ml/model")
+        shutil.copytree(src_dir, "/opt/ml/model", dirs_exist_ok=True)
 
 
 if __name__ == "__main__":  # pragma: no cover

tests/integ/sagemaker/workflow/test_retry.py

@@ -148,6 +148,8 @@ def test_model_registration_with_model_repack(
     role,
     pipeline_name,
     region_name,
+    pytorch_training_latest_version,
+    pytorch_training_latest_py_version,
 ):
     base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
     entry_point = os.path.join(base_dir, "mnist.py")
@@ -166,8 +168,8 @@ def test_model_registration_with_model_repack(
     pytorch_estimator = PyTorch(
         entry_point=entry_point,
         role=role,
-        framework_version="1.5.0",
-        py_version="py3",
+        framework_version=pytorch_training_latest_version,
+        py_version=pytorch_training_latest_py_version,
         instance_count=instance_count,
         instance_type=instance_type,
         sagemaker_session=pipeline_session,

tests/unit/sagemaker/local/test_local_image.py

@@ -160,7 +160,7 @@ def test_get_compose_cmd_prefix_with_docker_cli():
     "subprocess.check_output",
     side_effect=subprocess.CalledProcessError(returncode=1, cmd="docker compose version"),
 )
-@patch("sagemaker.local.image.find_executable", Mock(return_value="/usr/bin/docker-compose"))
+@patch("sagemaker.local.image.shutil.which", Mock(return_value="/usr/bin/docker-compose"))
 def test_get_compose_cmd_prefix_with_docker_compose_cli(check_output):
     compose_cmd_prefix = _SageMakerContainer._get_compose_cmd_prefix()
     assert compose_cmd_prefix == ["docker-compose"]
@@ -170,7 +170,7 @@ def test_get_compose_cmd_prefix_with_docker_compose_cli(check_output):
     "subprocess.check_output",
     side_effect=subprocess.CalledProcessError(returncode=1, cmd="docker compose version"),
 )
-@patch("sagemaker.local.image.find_executable", Mock(return_value=None))
+@patch("sagemaker.local.image.shutil.which", Mock(return_value=None))
 def test_get_compose_cmd_prefix_raises_import_error(check_output):
     with pytest.raises(ImportError) as e:
         _SageMakerContainer._get_compose_cmd_prefix()

tests/unit/sagemaker/local/test_local_utils.py

@@ -85,11 +85,11 @@ def test_move_to_destination_illegal_destination():
 
 
 @patch("sagemaker.local.utils.os.path")
-@patch("sagemaker.local.utils.copy_tree")
+@patch("sagemaker.local.utils.shutil.copytree")
 def test_recursive_copy(copy_tree, m_os_path):
     m_os_path.isdir.return_value = True
     sagemaker.local.utils.recursive_copy("source", "destination")
-    copy_tree.assert_called_with("source", "destination")
+    copy_tree.assert_called_with("source", "destination", dirs_exist_ok=True)
 
 
 @patch("sagemaker.local.utils.os")

tests/unit/sagemaker/workflow/test_step_collections.py

@@ -55,7 +55,7 @@
 
 MODEL_NAME = "gisele"
 MODEL_REPACKING_IMAGE_URI = (
-    "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3"
+    "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.2-1-cpu-py3"
 )
 
 
@@ -1219,8 +1219,7 @@ def test_estimator_transformer_with_model_repack_with_estimator(estimator, sourc
             assert arguments == {
                 "AlgorithmSpecification": {
                     "TrainingInputMode": "File",
-                    "TrainingImage": "246618743249.dkr.ecr.us-west-2.amazonaws.com/"
-                    + "sagemaker-scikit-learn:0.23-1-cpu-py3",
+                    "TrainingImage": MODEL_REPACKING_IMAGE_URI,
                 },
                 "ProfilerConfig": {"DisableProfiler": True},
                 "OutputDataConfig": {"S3OutputPath": "s3://my-bucket/"},