different name for training job inside estimator than step input

- sagemaker contrainer : conda_pytorch_p36
- estimator mode : 'script mode'
While it is a MUST param that I have to give a name for `TrainingJobName` from step functions for data science sdk.
```
pytorch_estimator = PyTorch(entry_point='HRC_0818_final.py',
train_instance_type='ml.m4.xlarge',
role=role,
train_instance_count=1,
framework_version='1.4.0',
base_job_name = 'kanto-base-job',
)
```

```
import stepfunctions
training_step = steps.TrainingStep(
'Model Training',
estimator=pytorch_estimator,
data={
'training': s3_input(s3_data=execution_input['TrainTargetLocation'])
} ,
job_name=execution_input['TrainingJobName'],
wait_for_completion=True
)

model_step = steps.ModelStep(
'Save model',
model=training_step.get_expected_model(),
model_name=execution_input['ModelName'] ,
instance_type='ml.m4.xlarge',

)

execution = workflow.execute(
inputs={

'ModelName': 'kanto-mode-{}'.format(uuid.uuid4().hex),
'TrainTargetLocation' : 's3://hrms-train/traindata/train.jsonl'
}
)
```

it is still the default training job name inside the estimator with current `strtime` following` base_job_name`
` "module_dir": "s3://sagemaker-{aws-region}-{aws-id}/{training-job-name}/source/sourcedir.tar.gz",`

Then you link a wrong dir to a Model consequently.
`SAGEMAKER_SUBMIT_DIRECTORY | s3://sagemaker-{aws-region}-{aws-id}/{base-job-name}-2020-08-20-17-47-50-751/source/sourcedir.tar.gz`

I guess the reason is that I have two difference folder for model.tar.gz and sourcedir.tar.gz then leads to a awkward behavior that you can't create consolidated model.tar.gz when you deploy it to server. I can only copy sourcedir.tar.gz to a mms server as this is a default job name. I am missing model.pth consequently.

So, that just leads to put a lambda function that just copies model.tar.gz (model.pth) from TrainTargetLocation folder to default training job folder (strtime named) to make it work correctly.

Commencez par retracer la manière dont TrainingStep gère job_name avec base_job_name de l’estimateur, puis examinez comment les chemins de model.tar.gz et sourcedir.tar.gz sont construits. Reproduisez le comportement avec les extraits PyTorch, TrainingStep et ModelStep ; le travail est terminé lorsque le nom du job d’entraînement et les emplacements des artefacts restent cohérents sans le workaround de copie signalé.

Rédigé par le modèle d'indexation à partir du texte de l'issue.