Merge pull request aws#35 from verdimrc/fp-integ-test-non-tf

Integration tests for MXNetProcessor and PyTorchProcessor

Author: verdimrc

tests/integ/test_mxnet.py

@@ -22,6 +22,7 @@
 from sagemaker import ModelPackage
 from sagemaker.mxnet.estimator import MXNet
 from sagemaker.mxnet.model import MXNetModel
+from sagemaker.mxnet.processing import MXNetProcessor
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.kms_utils import get_or_create_kms_key
@@ -65,6 +66,35 @@ def mxnet_training_job(
         return mx.latest_training_job.name
 
 
+@pytest.mark.release
+def test_framework_processing_job_with_deps(
+    sagemaker_session,
+    mxnet_training_latest_version,
+    mxnet_training_latest_py_version,
+    cpu_instance_type,
+):
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
+        entry_point = "main_script.py"
+
+        processor = MXNetProcessor(
+            framework_version=mxnet_training_latest_version,
+            py_version=mxnet_training_latest_py_version,
+            role="SageMakerRole",
+            instance_count=1,
+            instance_type=cpu_instance_type,
+            sagemaker_session=sagemaker_session,
+            base_job_name="test-mxnet",
+        )
+
+        processor.run(
+            code=entry_point,
+            source_dir=code_path,
+            inputs=[],
+            wait=True,
+        )
+
+
 @pytest.mark.release
 def test_attach_deploy(mxnet_training_job, sagemaker_session, cpu_instance_type):
     endpoint_name = "test-mxnet-attach-deploy-{}".format(sagemaker_timestamp())

tests/integ/test_pytorch.py

@@ -18,6 +18,7 @@
 
 from sagemaker.pytorch.estimator import PyTorch
 from sagemaker.pytorch.model import PyTorchModel
+from sagemaker.pytorch.processing import PyTorchProcessor
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import (
     test_region,
@@ -95,6 +96,35 @@ def fixture_training_job_with_latest_inference_version(
         return pytorch.latest_training_job.name
 
 
+@pytest.mark.release
+def test_framework_processing_job_with_deps(
+    sagemaker_session,
+    pytorch_training_latest_version,
+    pytorch_training_latest_py_version,
+    cpu_instance_type,
+):
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
+        entry_point = "main_script.py"
+
+        processor = PyTorchProcessor(
+            framework_version=pytorch_training_latest_version,
+            py_version=pytorch_training_latest_py_version,
+            role="SageMakerRole",
+            instance_count=1,
+            instance_type=cpu_instance_type,
+            sagemaker_session=sagemaker_session,
+            base_job_name="test-pytorch",
+        )
+
+        processor.run(
+            code=entry_point,
+            source_dir=code_path,
+            inputs=[],
+            wait=True,
+        )
+
+
 @pytest.mark.release
 def test_fit_deploy(
     pytorch_training_job_with_latest_infernce_version, sagemaker_session, cpu_instance_type