infra: properly fail PR build if has-matching-changes fails

buildspec-localmodetests.yml

@@ -11,16 +11,5 @@ phases:
 
       # local mode tests
       - start_time=`date +%s`
-      - HAS_MATCHING_CHANGES_OUTPUT=$(has-matching-changes "tests/" "src/*.py" "setup.py" "setup.cfg" "buildspec.yml")
-      - |
-        if [ "$HAS_MATCHING_CHANGES" = "Changes Found" ] ; then
-          tox -e py36 -- tests/integ -m local_mode --durations 50
-        fi
-      - ./ci-scripts/displaytime.sh 'py36 local mode' $start_time
-
-      - start_time=`date +%s`
-      - |
-        if [ "$HAS_MATCHING_CHANGES" = "Changes Found" ] ; then
-          tox -e py27 -- tests/integ -m local_mode --durations 50
-        fi
-      - ./ci-scripts/displaytime.sh 'py27 local mode' $start_time
+      - execute-command-if-has-matching-changes "tox -e py27,py36 -- tests/integ -m local_mode --durations 50" "tests/" "src/*.py" "setup.py" "setup.cfg" "buildspec.yml"
+      - ./ci-scripts/displaytime.sh 'py27,py36 local mode' $start_time

buildspec-notebooktests.yml

@@ -6,9 +6,5 @@ phases:
       # run notebook test
       - echo "running notebook test"
       - start_time=`date +%s`
-      - HAS_MATCHING_CHANGES_OUTPUT=$(has-matching-changes "src/*.py" "setup.py" "setup.cfg" "tests/scripts/run-notebook-test.sh")
-      - |
-        if [ "$HAS_MATCHING_CHANGES" = "Changes Found" ] ; then
-          ./tests/scripts/run-notebook-test.sh
-        fi
+      - execute-command-if-has-matching-changes "./tests/scripts/run-notebook-test.sh" "src/*.py" "setup.py" "setup.cfg" "tests/scripts/run-notebook-test.sh"
       - ./ci-scripts/displaytime.sh 'notebook test' $start_time

buildspec.yml

@@ -11,18 +11,12 @@ phases:
 
       # run integration tests
       - start_time=`date +%s`
-      - HAS_MATCHING_CHANGES_OUTPUT=$(has-matching-changes "tests/" "src/*.py" "setup.py" "setup.cfg" "buildspec.yml")
-      - |
-        if [ "$HAS_MATCHING_CHANGES" = "Changes Found" ] ; then
-          python3 -u ci-scripts/queue_build.py
-        fi
+      - execute-command-if-has-matching-changes "python3 -u ci-scripts/queue_build.py" "tests/" "src/*.py" "setup.py" "setup.cfg" "buildspec.yml"
       - ./ci-scripts/displaytime.sh 'build queue' $start_time
 
       - start_time=`date +%s`
       - |
-        if [ "$HAS_MATCHING_CHANGES" = "Changes Found" ] ; then
-          tox -e py36 -- tests/integ -m "not local_mode" -n 512 --reruns 3 --reruns-delay 5 --durations 50 --boto-config '{"region_name": "us-east-2"}'
-        fi
+        execute-command-if-has-matching-changes "tox -e py36 -- tests/integ -m \"not local_mode\" -n 512 --reruns 3 --reruns-delay 5 --durations 50 --boto-config '{\"region_name\": \"us-east-2\"}'" "tests/" "src/*.py" "setup.py" "setup.cfg" "buildspec.yml"
       - ./ci-scripts/displaytime.sh 'py36 tests/integ' $start_time
 
   post_build:

doc/using_chainer.rst

@@ -509,7 +509,7 @@ The ChainerModel constructor takes the following arguments:
    which should be executed as the entry point to model hosting.
 -  ``source_dir (str):`` Optional. Path (absolute or relative) to a
    directory with any other training source code dependencies including
-   tne entry point file. Structure within this directory will be
+   the entry point file. Structure within this directory will be
    preserved when training on SageMaker.
 -  ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
    and hosting containers will generate Cloudwatch metrics under the

doc/using_mxnet.rst

@@ -720,7 +720,7 @@ The MXNetModel constructor takes the following arguments:
    which should be executed as the entry point to model hosting.
 -  ``source_dir (str):`` Optional. Path (absolute or relative) to a
    directory with any other training source code dependencies including
-   tne entry point file. Structure within this directory will be
+   the entry point file. Structure within this directory will be
    preserved when training on SageMaker.
 -  ``container_log_level (int):`` Log level to use within the container.
    Valid values are defined in the Python logging module.

doc/using_sklearn.rst

@@ -486,7 +486,7 @@ The sklearn_model constructor takes the following arguments:
    which should be executed as the entry point to model hosting.
 -  ``source_dir (str):`` Optional. Path (absolute or relative) to a
    directory with any other training source code dependencies including
-   tne entry point file. Structure within this directory will be
+   the entry point file. Structure within this directory will be
    preserved when training on SageMaker.
 -  ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
    and hosting containers will generate Cloudwatch metrics under the

src/sagemaker/chainer/estimator.py

@@ -92,7 +92,7 @@ def __init__(
                 NCCL_DEBUG=WARN' will pass that option string to the mpirun
                 command.
             source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from tne entry
+                any other training source code dependencies aside from the entry
                 point file (default: None). Structure within this directory are
                 preserved when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for

src/sagemaker/mxnet/estimator.py

@@ -71,7 +71,7 @@ def __init__(
                 file which should be executed as the entry point to training.
                 This should be compatible with either Python 2.7 or Python 3.5.
             source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from tne entry
+                any other training source code dependencies aside from the entry
                 point file (default: None). Structure within this directory are
                 preserved when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for

src/sagemaker/pytorch/estimator.py

@@ -69,7 +69,7 @@ def __init__(
                 file which should be executed as the entry point to training.
                 This should be compatible with either Python 2.7 or Python 3.5.
             source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from tne entry
+                any other training source code dependencies aside from the entry
                 point file (default: None). Structure within this directory are
                 preserved when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for

src/sagemaker/rl/estimator.py

@@ -189,7 +189,7 @@ def create_model(
                 hosting. This should be compatible with Python 3.5 (default:
                 self.entry_point)
             source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from tne entry
+                any other training source code dependencies aside from the entry
                 point file (default: self.source_dir). Structure within this
                 directory are preserved when hosting on Amazon SageMaker.
             dependencies (list[str]): A list of paths to directories (absolute

src/sagemaker/sklearn/estimator.py

@@ -70,7 +70,7 @@ def __init__(
                 executing your model training code. List of supported versions
                 https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
             source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from tne entry
+                any other training source code dependencies aside from the entry
                 point file (default: None). Structure within this directory are
                 preserved when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for

src/sagemaker/xgboost/estimator.py

@@ -82,7 +82,7 @@ def __init__(
                 training code.  List of supported versions
                 https://github.com/aws/sagemaker-python-sdk#xgboost-sagemaker-estimators
             source_dir (str): Path (absolute or relative) to a directory with any other training
-                source code dependencies aside from tne entry point file (default: None).
+                source code dependencies aside from the entry point file (default: None).
                 Structure within this directory are preserved when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for training (default: None).
                 The hyperparameters are made accessible as a dict[str, str] to the training code