Training a model in local_code mode does not work if source_dir="." #548
Description
System Information
- Python Version: 3.6
- Python SDK Version: master
- Are you using a custom image: yes
Describe the problem
I am trying to train a model using the undocumented local_code mode. In case I don't specify source_dir or set it to "." the training procedure fails to mount the volumes correctly.
I get:
Cannot create container for service algo-1-JFP46: create .: volume name is too short, names should be at least two alphanumeric characters
I am reporting this even if local_code is still not documented, hoping it can be useful anyway.
Minimal repro / logs
Please provide any logs and a bare minimum reproducible test case, as this will be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
session = LocalSession()
session.config = {'local': {'local_code': True}}
est = MyEstimator(
entry_point='code.py',
train_instance_type='local',
train_instance_count=1,
role=role,
sagemaker_session=session,
)
est.fit()
See the full traceback.
Here is the interesting part of the generated docker-compose.yaml:
networks:
sagemaker-local:
name: sagemaker-local
services:
volumes:
- /tmp/tmp_i5dhjtn/algo-1-JFP46/output/data:/opt/ml/output/data
- /tmp/tmp_i5dhjtn/algo-1-JFP46/output:/opt/ml/output
- /tmp/tmp_i5dhjtn/algo-1-JFP46/input:/opt/ml/input
- /tmp/tmp_i5dhjtn/model:/opt/ml/model
- :/opt/ml/code
- /tmp/tmp_i5dhjtn/shared:/opt/ml/shared