NerlPlanner
NerlPlanner is a tool that assists with generating configuration files of type JSON for Nerlnet.
There are 3 types of configurations files that configure a Nerlnet distribution ML experiment:
Distributed Configuration File (dc_<name>.json), describes the layout of the distributed ML. It includes the devices allocation of ports and address and the mapping of entities to devices.
TODO - update when generating is supported by NP
TODO - update when generating is supported by NP
- Load json: A worker configuration json to load former network model.
- Select Json File Output Directory.
- Name of worker network model json file.
This section is responsible for the definition of model.
Model type allows user to select between several pre-defined models. A custom neural network is the NN type where user should config all layers of the network. Pre-defined projects add layers before and after hidden layer to achieve the desired functionality (E.g., classification, regression or text prediction network types).
Declare the number of neurons of each layer. Each layer must have a size definition. Sizes of layers are separated by ",".
There is a simple layer size which is an integer positive value that defines the number of neurons for a 1D dimension layer. In addition, there is a complex layer size string which declares a multi-dimensional layers, E.g., CNN layers.
Example of NN sizes definitions:
First layer complex size of CNN: "128x128k3x3s2x2x1p1x1"
Second layer size: "4096"
Third layer size: "128"
Last layer size: "4"
As one string: "128x128k3x3s2x2x1p1x1,4096,128,4"
Explain here about fields and optional arguments
