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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Repack model script for training jobs to inject entry points"""
from __future__ import absolute_import
import argparse
import os
import shutil
import tarfile
import tempfile
# Repack Model
# The following script is run via a training job which takes an existing model and a custom
# entry point script as arguments. The script creates a new model archive with the custom
# entry point in the "code" directory along with the existing model. Subsequently, when the model
# 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
Args:
inference_script (str): The path to the custom entry point.
model_archive (str): The name or path (e.g. s3 uri) of the model TAR archive.
dependencies (str): A space-delimited string of paths to custom dependencies.
source_dir (str): The path to a custom source directory.
"""
# the data directory contains a model archive generated by a previous training job
data_directory = "/opt/ml/input/data/training"
model_path = os.path.join(data_directory, model_archive.split("/")[-1])
# create a temporary directory
with tempfile.TemporaryDirectory() as tmp:
local_path = os.path.join(tmp, "local.tar.gz")
# copy the previous training job's model archive to the temporary directory
shutil.copy2(model_path, local_path)
src_dir = os.path.join(tmp, "src")
# create the "code" directory which will contain the inference script
code_dir = os.path.join(src_dir, "code")
os.makedirs(code_dir)
# extract the contents of the previous training job's model archive to the "src"
# directory of this training job
with tarfile.open(name=local_path, mode="r:gz") as tf:
tf.extractall(path=src_dir)
if source_dir:
# copy /opt/ml/code to code/
if os.path.exists(code_dir):
shutil.rmtree(code_dir)
shutil.copytree("/opt/ml/code", code_dir)
else:
# copy the custom inference script to code/
entry_point = os.path.join("/opt/ml/code", inference_script)
shutil.copy2(entry_point, os.path.join(code_dir, inference_script))
# copy any dependencies to code/lib/
if dependencies:
for dependency in dependencies.split(" "):
actual_dependency_path = os.path.join("/opt/ml/code", dependency)
lib_dir = os.path.join(code_dir, "lib")
if not os.path.exists(lib_dir):
os.mkdir(lib_dir)
if os.path.isfile(actual_dependency_path):
shutil.copy2(actual_dependency_path, lib_dir)
else:
if os.path.exists(lib_dir):
shutil.rmtree(lib_dir)
# a directory is in the dependencies. we have to copy
# all of /opt/ml/code into the lib dir because the original directory
# was flattened by the SDK training job upload..
shutil.copytree("/opt/ml/code", lib_dir)
break
# 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")
if __name__ == "__main__": # pragma: no cover
parser = argparse.ArgumentParser()
parser.add_argument("--inference_script", type=str, default="inference.py")
parser.add_argument("--dependencies", type=str, default=None)
parser.add_argument("--source_dir", type=str, default=None)
parser.add_argument("--model_archive", type=str, default="model.tar.gz")
args, extra = parser.parse_known_args()
repack(
inference_script=args.inference_script,
dependencies=args.dependencies,
source_dir=args.source_dir,
model_archive=args.model_archive,
)
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