System Information
- Framework (e.g. TensorFlow) / Algorithm (e.g. KMeans): Tensorflow
- Framework Version: 1.11.0
- Python Version: 3.6.6
- CPU or GPU: CPU
- Python SDK Version: 1.16.0
- Are you using a custom image: No
Describe the problem
When calling fit(), Tensorflow script mode does not save a trained model to s3://(default-bucket)/(default-job-name)/output/model.tar.gz, but to s3://(default-bucket)/(default-job-name)/model/ (see a picture below). However, the estimator assumes that the model is saved as model.tar.gz, estimator.deploy fails without explicitly compressing the model files and specifying the directory. I think the trained model should be saved asmodel.tar.gz in the directory output as well as other SageMaker containers do.
Minimal repro / logs
Notebook: https://gist.github.com/harusametime/ffaee7c96cfc2a50279923391dff435f
entry point: https://gist.github.com/harusametime/d0a92d715fe0610b4b8fbd11f6d42359
System Information
Describe the problem
When calling fit(), Tensorflow script mode does not save a trained model to
s3://(default-bucket)/(default-job-name)/output/model.tar.gz, but tos3://(default-bucket)/(default-job-name)/model/(see a picture below). However, the estimator assumes that the model is saved asmodel.tar.gz,estimator.deployfails without explicitly compressing the model files and specifying the directory. I think the trained model should be saved asmodel.tar.gzin the directoryoutputas well as other SageMaker containers do.Minimal repro / logs
Notebook: https://gist.github.com/harusametime/ffaee7c96cfc2a50279923391dff435f
entry point: https://gist.github.com/harusametime/d0a92d715fe0610b4b8fbd11f6d42359