[Bug Report] - SageMaker Pipelines to Run Jobs Locally

**Link to the notebook**

Following example from: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb

My notebook with error (i made some modifications to it, minor ones): https://github.com/fjpa121197/aws-sagemaker-training/blob/main/sagemaker-pipelines-local-mode.ipynb

**Describe the bug**
I'm trying to follow this tutorial to run Sagemaker Pipelines locally and test them before using managed resources. I have created a pipeline definition that includes preprocessing, training and evaluation. I'm able to create the pipeline without any problem, but when executing the pipeline, I encountered error in the evaluation step. It is related to not being able to download the model.tar.gz file to the container and to the correct directory to use the model for evaluation.

Error:

```python
Starting pipeline step: 'AbaloneEval'
Container jhais7c823-algo-1-7ko39 Creating
Container jhais7c823-algo-1-7ko39 Created
Attaching to jhais7c823-algo-1-7ko39
jhais7c823-algo-1-7ko39 | Traceback (most recent call last):
jhais7c823-algo-1-7ko39 | File "/opt/ml/processing/input/code/evaluation.py", line 16, in
jhais7c823-algo-1-7ko39 | with tarfile.open(model_path) as tar:
jhais7c823-algo-1-7ko39 | File "/miniconda3/lib/python3.8/tarfile.py", line 1603, in open
jhais7c823-algo-1-7ko39 | return func(name, "r", fileobj, **kwargs)
jhais7c823-algo-1-7ko39 | File "/miniconda3/lib/python3.8/tarfile.py", line 1667, in gzopen
jhais7c823-algo-1-7ko39 | fileobj = GzipFile(name, mode + "b", compresslevel, fileobj)
jhais7c823-algo-1-7ko39 | File "/miniconda3/lib/python3.8/gzip.py", line 173, in __init__
jhais7c823-algo-1-7ko39 | fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
jhais7c823-algo-1-7ko39 | FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/processing/model/model.tar.gz'

jhais7c823-algo-1-7ko39 exited with code 1
Aborting on container exit...
Container jhais7c823-algo-1-7ko39 Stopping
Container jhais7c823-algo-1-7ko39 Stopped
Pipeline step 'AbaloneEval' FAILED. Failure message is: RuntimeError: Failed to run: ['docker-compose', '-f', 'C:\\Users\\FRANCI~1.PAR\\AppData\\Local\\Temp\\tmp188wz79r\\docker-compose.yaml', 'up', '--build', '--abort-on-container-exit']
Pipeline execution 1012b92d-36c6-4499-b898-d78d7a2bea8a FAILED because step 'AbaloneEval' failed.
```

I understand that the evaluation step definition is as follows:
```python
Job Name: script-abalone-eval-2022-10-25-09-04-44-205
Inputs: [{'InputName': 'input-1', 'AppManaged': False, 'S3Input': {'S3Uri': , 'LocalPath': '/opt/ml/processing/model', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'input-2', 'AppManaged': False, 'S3Input': {'S3Uri': , 'LocalPath': '/opt/ml/processing/test', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-local-pipeline-tutorials/script-abalone-eval-2022-10-25-09-04-44-205/input/code/evaluation.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: [{'OutputName': 'evaluation', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-local-pipeline-tutorials/script-abalone-eval-2022-10-25-09-04-44-205/output/evaluation', 'LocalPath': '/opt/ml/processing/evaluation', 'S3UploadMode': 'EndOfJob'}}]
```

And my `eval_args` definition is as follows:

```python
eval_args = script_eval.run(
inputs=[
ProcessingInput(
source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
destination="/opt/ml/processing/model",
),
ProcessingInput(
source=step_process.properties.ProcessingOutputConfig.Outputs["test"].S3Output.S3Uri,
destination="/opt/ml/processing/test",
),
],
outputs=[
ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
],
code="code/evaluation.py",
)
```
where `source` for the first input refers to the step_train defined before and it should download the model artifacts, but it is not doing it. For the other defined input, it does download the test data to use, but not the model artificats.

Not sure if there is a replacement for: `source=step_train.properties.ModelArtifacts.S3ModelArtifacts` argument.

Am I doing something wrong? I don't think it is permission/policies related since it doesn't give any AccessDenied errors.

Im using sagemaker 2.113.0

Thanks in advance

Start by reproducing the failure from the linked SageMaker local-mode notebook and inspect the evaluation.py entry point and the eval_args ProcessingInput definitions. Compare the model-artifact input with the test-data input and verify that the evaluation container receives model.tar.gz at /opt/ml/processing/model; the pipeline should complete the AbaloneEval step successfully.

Written by the indexing model from the issue text.