- : a list consisting of the weight and bias matrices
* features

- [optional]: a list consisting of the additional hyper parameters, e.g., learning rate, momentum
* checkpointing

- : a list consisting of the updated weight and bias matrices
was:
The objective of “paramserv” built-in function is to update an initial or existing model with configuration. An initial function signature would be:
{code:java}
model'=paramserv(model, features=X, labels=Y, val_features=X_val, val_labels=Y_val, upd="fun1", agg="fun2", mode="BSP", freq="BATCH", epochs=100, batchsize=64, k=7, scheme="disjoint_contiguous", hyperparams=params, checkpointing="NONE"){code}
We are interested in providing the model (which will be a struct-like data structure consisting of the weights, the biases and the hyperparameters), the training features and labels, the validation features and labels, the batch update function (i.e., gradient calculation func), the update strategy (e.g. sync, async, hogwild!, stale-synchronous), the update frequency (e.g. epoch or mini-batch), the gradient aggregation function, the number of epoch, the batch size, the degree of parallelism, the data partition scheme, a list of additional hyper parameters, as well as the checkpointing strategy. And the function will return a trained model in struct format.
*Inputs*:
* model

- : a list consisting of the weight and bias matrices
* features

- [optional]: a list consisting of the additional hyper parameters, e.g., learning rate, momentum
* checkpointing

- : a list consisting of the updated weight and bias matrices
> API design of the paramserv function
> ------------------------------------
>
> Key: SYSTEMML-2299
> URL: https://issues.apache.org/jira/browse/SYSTEMML-2299
> Project: SystemML
> Issue Type: Sub-task
> Reporter: LI Guobao
> Assignee: LI Guobao
> Priority: Major
>
> The objective of “paramserv” built-in function is to update an initial or existing model with configuration. An initial function signature would be:
> {code:java}
> model'=paramserv(model, features=X, labels=Y, val_features=X_val, val_labels=Y_val, upd="fun1", agg="fun2", mode="LOCAL", utype="BSP", freq="BATCH", epochs=100, batchsize=64, k=7, scheme="disjoint_contiguous", hyperparams=params, checkpointing="NONE"){code}
> We are interested in providing the model (which will be a struct-like data structure consisting of the weights, the biases and the hyperparameters), the training features and labels, the validation features and labels, the batch update function (i.e., gradient calculation func), the update strategy (e.g. sync, async, hogwild!, stale-synchronous), the update frequency (e.g. epoch or mini-batch), the gradient aggregation function, the number of epoch, the batch size, the degree of parallelism, the data partition scheme, a list of additional hyper parameters, as well as the checkpointing strategy. And the function will return a trained model in struct format.
> *Inputs*:
> * model

- : a list consisting of the weight and bias matrices
> * features

- [optional]: a list consisting of the additional hyper parameters, e.g., learning rate, momentum
> * checkpointing

- : a list consisting of the updated weight and bias matrices
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