ENH: Computer Vision pipelines

[radheyakale]

[radheyakale]

• [ ] TODO: Need a robust way to find if the MLHandler expects image data for training/testing
• [ ] TODO: Separate class is required for training and testing CV models
• [ ] TODO: Code has to be made generic to dynamically select Resnet50/VGG, etc. models
• [ ] TODO: Model training parameters viz. epochs, batch size have to be taken from the user with defaults set
• [ ] TODO: setup and get_model functions have to be made more scalable
• [ ] TODO: ModelStore init has to be made scalable

@jaidevd @sanand0 Please add/edit/delete any more TODOs that you feel may be required.

[jaidevd]

@radheyakale

1. Start by creating a wrapper class for Keras apps which inherits from this. Refer to the HFTransformer class for an example.
2. Move as much code away from MLHandler as possible. It's okay if the example only supports ResNet50 for now.
3. Provide a sample gramex.yaml which shows the usage.

[radheyakale]

@radheyakale

1. Start by creating a wrapper class for Keras apps which inherits from this. Refer to the HFTransformer class for an example.
2. Move as much code away from MLHandler as possible. It's okay if the example only supports ResNet50 for now.
3. Provide a sample gramex.yaml which shows the usage.

@jaidevd Please look at the recent commit https://github.com/gramener/gramex/pull/560/commits/fe2c85e905dc81c8d593da7e4b3b3949424572a8. Is this the correct approach to implement it?