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From "LI Guobao (JIRA)" <>
Subject [jira] [Commented] (SYSTEMML-2299) API design of the paramserv function
Date Sun, 13 May 2018 16:39:00 GMT


LI Guobao commented on SYSTEMML-2299:

[~mboehm7] I still have a question about the function design. How could we decide whether
the local or spark backend should execute the function? Should we need to specify it explicitly
or infer it according to the data size?

> API design of the paramserv function
> ------------------------------------
>                 Key: SYSTEMML-2299
>                 URL:
>             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, X, y, X_val, y_val, upd="fun1", agg="fun2", mode="BSP", freq="EPOCH",
epochs=100, batchsize=64, k=7, scheme="disjoint_contiguous", hyperparam=params, checkpoint="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
> *Inputs*:
>  * model <list>: a list consisting of the weight and bias matrices
>  * X <matrix>: training features matrix
>  * y <matrix>: training label matrix
>  * X_val <matrix>: validation features matrix
>  * y_val <matrix>: validation label matrix
>  * upd <string>: the name of gradient calculation function
>  * agg <string>: the name of gradient aggregation function
>  * mode <string> (options: BSP, ASP, SSP): the updating mode
>  * freq <string> (options: EPOCH, BATCH): the frequence of updates
>  * epochs <integer>: the number of epoch
>  * batchsize <integer>: the size of batch
>  * k <integer>: the degree of parallelism
>  * scheme <string> (options: disjoint_contiguous, disjoint_round_robin, disjoint_random,
overlap_reshuffle): the scheme of data partition, i.e., how the data is distributed across
>  * hyperparam <list> [optional]: a list consisting of the additional hyper parameters,
e.g., learning rate, momentum
>  * checkpoint <string> (options: NONE(default), EPOCH, EPOCH10) [optional]: the
checkpoint strategy, we could set a checkpoint for each epoch or each 10 epochs 
> *Output*:
>  * model' <list>: a list consisting of the updated weight and bias matrices

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