Mapping data_location to train script for scikit_bring_your_own

Hi,

How can we pass s3 path after the model is hosted on sagemaker and use this path to fetch the csv in the `train` code? How can we map this `data_location` directly in the `train` code?

```
account = sess.boto_session.client('sts').get_caller_identity()['Account']
region = sess.boto_session.region_name
image = '{}.dkr.ecr.{}.amazonaws.com/sagemaker-decision-trees:latest'.format(account, region)

tree = sage.estimator.Estimator(image,
role, 1, 'ml.c4.2xlarge',
output_path="s3://{}/output".format(sess.default_bucket()),
sagemaker_session=sess)

tree.fit(data_location)
```

train code -

```
def train():
print('Starting the training.')
try:
# Read in any hyperparameters that the user passed with the training job
with open(param_path, 'r') as tc:
trainingParams = json.load(tc)

# Take the set of files and read them all into a single pandas dataframe
input_files = [ os.path.join(training_path, file) for file in os.listdir(training_path) ]
if len(input_files) == 0:
raise ValueError(('There are no files in {}.\n' +
'This usually indicates that the channel ({}) was incorrectly specified,\n' +
'the data specification in S3 was incorrectly specified or the role specified\n' +
'does not have permission to access the data.').format(training_path, channel_name))
raw_data = [ pd.read_csv(file, header=None) for file in input_files ]
train_data = pd.concat(raw_data)

# labels are in the first column
train_y = train_data.ix[:,0]
train_X = train_data.ix[:,1:]

# Here we only support a single hyperparameter. Note that hyperparameters are always passed in as
# strings, so we need to do any necessary conversions.
max_leaf_nodes = trainingParams.get('max_leaf_nodes', None)
if max_leaf_nodes is not None:
max_leaf_nodes = int(max_leaf_nodes)

# Now use scikit-learn's decision tree classifier to train the model.
clf = tree.DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
clf = clf.fit(train_X, train_y)

# save the model
with open(os.path.join(model_path, 'decision-tree-model.pkl'), 'w') as out:
pickle.dump(clf, out)
print('Training complete.')
except Exception as e:
# Write out an error file. This will be returned as the failureReason in the
# DescribeTrainingJob result.
trc = traceback.format_exc()
with open(os.path.join(output_path, 'failure'), 'w') as s:
s.write('Exception during training: ' + str(e) + '\n' + trc)
# Printing this causes the exception to be in the training job logs, as well.
print('Exception during training: ' + str(e) + '\n' + trc, file=sys.stderr)
# A non-zero exit code causes the training job to be marked as Failed.
sys.exit(255)

if __name__ == '__main__':
train()

# A zero exit code causes the job to be marked a Succeeded.
sys.exit(0)
```

Does `fit` directly call `train` method when we execute `tree.fit(data_location)` ?

Start by tracing the SageMaker Estimator.fit call and the train() entry point shown in the issue. Check how data_location, training_path, channel_name, and param_path are connected in the referenced example, then document whether fit invokes train and how the S3 CSV path reaches it.

Written by the indexing model from the issue text.