[FATAL tini (7)] exec train failed: No such file or directory

**BUG Description**
I'm trying to automate and scale a large collection of experiments using AWS SageMamker via Python SDK. However, I am facing an error that does not give any direction to resolve it.

**To reproduce**
```python
role = "arn:..."

estimator = PyTorch(
image_uri="1...ecr...amazonaws.com/...:prototype",
entry_point="main.py",
role=role,
region="us-...",
instance_type="ml...xlarge",
instance_count=1,
volume_size=225,
hyperparameters=hparams
)
estimator.fit()
```

**Expected behavior**
The model is expected to start to train and log metrics and losses.

**Screenshots or logs**
```python
[2023-09-10 23:08:59,329][sagemaker][INFO] - Creating training-job with name: xmtc-2023-09-11-02-08-56-094
2023-09-11 02:09:00 Starting - Starting the training job...
2023-09-11 02:09:18 Starting - Preparing the instances for training......
2023-09-11 02:10:27 Downloading - Downloading input data
2023-09-11 02:10:27 Training - Downloading the training image..................
2023-09-11 02:13:33 Training - Training image download completed. Training in progress..[FATAL tini (7)] exec train failed: No such file or directory

2023-09-11 02:14:15 Uploading - Uploading generated training model
2023-09-11 02:14:15 Failed - Training job failed
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/celso/projects/LightningPrototype/run_on_sagemaker.py", line 32, in run_on_sagemaker
estimator.fit()
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/workflow/pipeline_context.py", line 311, in wrapper
return run_func(*args, **kwargs)
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/estimator.py", line 1292, in fit
self.latest_training_job.wait(logs=logs)
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/estimator.py", line 2474, in wait
self.sagemaker_session.logs_for_job(self.job_name, wait=True, log_type=logs)
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/session.py", line 4849, in logs_for_job
_logs_for_job(self.boto_session, job_name, wait, poll, log_type, timeout)
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/session.py", line 6760, in _logs_for_job
_check_job_status(job_name, description, "TrainingJobStatus")
File "/home/celso/projects/LightningPrototype/venv/lib/python3.10/site-packages/sagemaker/session.py", line 6813, in _check_job_status
raise exceptions.UnexpectedStatusException(
sagemaker.exceptions.UnexpectedStatusException: Error for Training job xmtc-2023-09-11-02-08-56-094: Failed. Reason: AlgorithmError: , exit code: 127

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Process finished with exit code 1
```

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: sagemaker 2.177.1
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Pytorch 2.0.1
- **Python version**: Python 3.10
- **Custom Docker image (Y/N)**: Yes, on ECR.

Start with the custom ECR image used by the PyTorch estimator and inspect how its entry point invokes the `train` command, then compare that with the `main.py` entry point passed to `estimator.fit()`. Reproduce the job using the shown SageMaker SDK configuration and confirm completion when the training container starts successfully and logs metrics and losses.

Written by the indexing model from the issue text.