Pytorch Sagemaker Container STDERR output

In Pytorch images all the prints in stderr are not catched and are ignored:

### Describe the problem

### Minimal repro / logs
Entrypoint.py:

```
if __name__ == '__main__':
import sys
sys.stderr.write('Coucou stderr')
sys.stdout.write('Coucou stdout')
```

```
from sagemaker.pytorch import PyTorch
estimator = PyTorch(entry_point='entrypoint.py',
role=role,
framework_version='1.1.0',
train_instance_count=1,
train_instance_type='local',
)
estimator.fit({'config': 's3://sagemaker-eu-*************/config/test_sagemaker_1.json'})
```

LOGS

Creating tmpqp7i_4w3_algo-1-8gd7b_1 ...
Attaching to tmpqp7i_4w3_algo-1-8gd7b_12mdone
algo-1-8gd7b_1 | 2019-10-22 09:06:21,345 sagemaker-containers INFO Imported framework sagemaker_pytorch_container.training
algo-1-8gd7b_1 | 2019-10-22 09:06:21,349 sagemaker-containers INFO No GPUs detected (normal if no gpus installed)
algo-1-8gd7b_1 | 2019-10-22 09:06:21,363 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.
algo-1-8gd7b_1 | 2019-10-22 09:06:21,365 sagemaker_pytorch_container.training INFO Invoking user training script.
algo-1-8gd7b_1 | 2019-10-22 09:06:21,489 sagemaker-containers INFO Module entrypoint does not provide a setup.py.
algo-1-8gd7b_1 | Generating setup.py
algo-1-8gd7b_1 | 2019-10-22 09:06:21,489 sagemaker-containers INFO Generating setup.cfg
algo-1-8gd7b_1 | 2019-10-22 09:06:21,489 sagemaker-containers INFO Generating MANIFEST.in
algo-1-8gd7b_1 | 2019-10-22 09:06:21,490 sagemaker-containers INFO Installing module with the following command:
algo-1-8gd7b_1 | /usr/bin/python -m pip install .
algo-1-8gd7b_1 | Processing /opt/ml/code
algo-1-8gd7b_1 | Building wheels for collected packages: entrypoint
algo-1-8gd7b_1 | Running setup.py bdist_wheel for entrypoint ... done
algo-1-8gd7b_1 | Stored in directory: /tmp/pip-ephem-wheel-cache-44kbrxy0/wheels/35/24/16/37574d11bf9bde50616c******356bc7164af8ca3
algo-1-8gd7b_1 | Successfully built entrypoint
algo-1-8gd7b_1 | Installing collected packages: entrypoint
algo-1-8gd7b_1 | Successfully installed entrypoint-1.0.0
algo-1-8gd7b_1 | You are using pip version 18.1, however version 19.3.1 is available.
algo-1-8gd7b_1 | You should consider upgrading via the 'pip install --upgrade pip' command.
algo-1-8gd7b_1 | 2019-10-22 09:06:23,054 sagemaker-containers INFO No GPUs detected (normal if no gpus installed)
algo-1-8gd7b_1 | 2019-10-22 09:06:23,069 sagemaker-containers INFO Invoking user script
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | Training Env:
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | {
algo-1-8gd7b_1 | "additional_framework_parameters": {},
algo-1-8gd7b_1 | "channel_input_dirs": {
algo-1-8gd7b_1 | "config": "/opt/ml/input/data/config"
algo-1-8gd7b_1 | },
algo-1-8gd7b_1 | "current_host": "algo-1-8gd7b",
algo-1-8gd7b_1 | "framework_module": "sagemaker_pytorch_container.training:main",
algo-1-8gd7b_1 | "hosts": [
algo-1-8gd7b_1 | "algo-1-8gd7b"
algo-1-8gd7b_1 | ],
algo-1-8gd7b_1 | "hyperparameters": {},
algo-1-8gd7b_1 | "input_config_dir": "/opt/ml/input/config",
algo-1-8gd7b_1 | "input_data_config": {
algo-1-8gd7b_1 | "config": {
algo-1-8gd7b_1 | "TrainingInputMode": "File"
algo-1-8gd7b_1 | }
algo-1-8gd7b_1 | },
algo-1-8gd7b_1 | "input_dir": "/opt/ml/input",
algo-1-8gd7b_1 | "is_master": true,
algo-1-8gd7b_1 | "job_name": "sagemaker-pytorch-2019-10-22-09-06-18-353",
algo-1-8gd7b_1 | "log_level": 20,
algo-1-8gd7b_1 | "master_hostname": "algo-1-8gd7b",
algo-1-8gd7b_1 | "model_dir": "/opt/ml/model",
algo-1-8gd7b_1 | "module_dir": "s3://sagemaker-eu-west-1-*********/sagemaker-pytorch-2019-10-22-09-06-18-353/source/sourcedir.tar.gz",
algo-1-8gd7b_1 | "module_name": "entrypoint",
algo-1-8gd7b_1 | "network_interface_name": "eth0",
algo-1-8gd7b_1 | "num_cpus": 2,
algo-1-8gd7b_1 | "num_gpus": 0,
algo-1-8gd7b_1 | "output_data_dir": "/opt/ml/output/data",
algo-1-8gd7b_1 | "output_dir": "/opt/ml/output",
algo-1-8gd7b_1 | "output_intermediate_dir": "/opt/ml/output/intermediate",
algo-1-8gd7b_1 | "resource_config": {
algo-1-8gd7b_1 | "current_host": "algo-1-8gd7b",
algo-1-8gd7b_1 | "hosts": [
algo-1-8gd7b_1 | "algo-1-8gd7b"
algo-1-8gd7b_1 | ]
algo-1-8gd7b_1 | },
algo-1-8gd7b_1 | "user_entry_point": "entrypoint.py"
algo-1-8gd7b_1 | }
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | Environment variables:
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | SM_HOSTS=["algo-1-8gd7b"]
algo-1-8gd7b_1 | SM_NETWORK_INTERFACE_NAME=eth0
algo-1-8gd7b_1 | SM_HPS={}
algo-1-8gd7b_1 | SM_USER_ENTRY_POINT=entrypoint.py
algo-1-8gd7b_1 | SM_FRAMEWORK_PARAMS={}
algo-1-8gd7b_1 | SM_RESOURCE_CONFIG={"current_host":"algo-1-8gd7b","hosts":["algo-1-8gd7b"]}
algo-1-8gd7b_1 | SM_INPUT_DATA_CONFIG={"config":{"TrainingInputMode":"File"}}
algo-1-8gd7b_1 | SM_OUTPUT_DATA_DIR=/opt/ml/output/data
algo-1-8gd7b_1 | SM_CHANNELS=["config"]
algo-1-8gd7b_1 | SM_CURRENT_HOST=algo-1-8gd7b
algo-1-8gd7b_1 | SM_MODULE_NAME=entrypoint
algo-1-8gd7b_1 | SM_LOG_LEVEL=20
algo-1-8gd7b_1 | SM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main
algo-1-8gd7b_1 | SM_INPUT_DIR=/opt/ml/input
algo-1-8gd7b_1 | SM_INPUT_CONFIG_DIR=/opt/ml/input/config
algo-1-8gd7b_1 | SM_OUTPUT_DIR=/opt/ml/output
algo-1-8gd7b_1 | SM_NUM_CPUS=2
algo-1-8gd7b_1 | SM_NUM_GPUS=0
algo-1-8gd7b_1 | SM_MODEL_DIR=/opt/ml/model
algo-1-8gd7b_1 | SM_MODULE_DIR=s3://sagemaker-eu-west-1-***********/sagemaker-pytorch-2019-10-22-09-06-18-353/source/sourcedir.tar.gz
algo-1-8gd7b_1 | SM_TRAINING_ENV={"additional_framework_parameters":{},"channel_input_dirs":{"config":"/opt/ml/input/data/config"},"current_host":"algo-1-8gd7b","framework_module":"sagemaker_pytorch_container.training:main","hosts":["algo-1-8gd7b"],"hyperparameters":{},"input_config_dir":"/opt/ml/input/config","input_data_config":{"config":{"TrainingInputMode":"File"}},"input_dir":"/opt/ml/input","is_master":true,"job_name":"sagemaker-pytorch-2019-10-22-09-06-18-353","log_level":20,"master_hostname":"algo-1-8gd7b","model_dir":"/opt/ml/model","module_dir":"s3://sagemaker-eu-west-1-**********/sagemaker-pytorch-2019-10-22-09-06-18-353/source/sourcedir.tar.gz","module_name":"entrypoint","network_interface_name":"eth0","num_cpus":2,"num_gpus":0,"output_data_dir":"/opt/ml/output/data","output_dir":"/opt/ml/output","output_intermediate_dir":"/opt/ml/output/intermediate","resource_config":{"current_host":"algo-1-8gd7b","hosts":["algo-1-8gd7b"]},"user_entry_point":"entrypoint.py"}
algo-1-8gd7b_1 | SM_USER_ARGS=[]
algo-1-8gd7b_1 | SM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate
algo-1-8gd7b_1 | SM_CHANNEL_CONFIG=/opt/ml/input/data/config
algo-1-8gd7b_1 | PYTHONPATH=/usr/local/bin:/usr/lib/python36.zip:/usr/lib/python3.6:/usr/lib/python3.6/lib-dynload:/usr/local/lib/python3.6/dist-packages:/usr/lib/python3/dist-packages
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | Invoking script with the following command:
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | /usr/bin/python -m entrypoint
algo-1-8gd7b_1 |
algo-1-8gd7b_1 |
algo-1-8gd7b_1 | Coucou stdout2019-10-22 09:06:23,102 sagemaker-containers INFO Reporting training SUCCESS
tmpqp7i_4w3_algo-1-8gd7b_1 exited with code 0
Aborting on container exit...
===== Job Complete =====

As you see the coucou stdout has been printed, stderr has been ignored. In distant mode same result.

Reproduce the issue with entrypoint.py and the minimal PyTorch estimator.fit example. Start with the sagemaker_pytorch_container.training:main entry point shown in the logs, comparing local and distant runs; done means both the supplied stderr and stdout messages are visible in the training logs.

Written by the indexing model from the issue text.