Edit TensorFlow model for training
Before adding a TensorFlow model to IBM Spectrum Conductor Deep Learning Impact, edit the model to enable distributed training capabilities or deep learning insights.
By default, IBM Spectrum Conductor Deep Learning Impact supports single-node training for TensorFlow models without deep learning insights. To change the training engine or to add deep learning insight capabilities the model must be configured accordingly.
IBM Spectrum Conductor Deep Learning Impact supports the following
training engines:
- Single node training
- Distributed training with TensorFlow
- Distributed training with IBM Fabric
Each TensorFlow model has the following files:
- main.py: TensorFlow train model program main entrance
- inference.py: TensorFlow inference model program main entrance
- fabricmodel.py: Callback program to convert training model into TensorFlow compute graph
- ps.conf: Training parameters which is optional.
To get the deep learning insights feature for TensorFlow models, edit the TensorFlow model to include deep learning insights, see Edit a TensorFlow training model for deep learning insights.