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From "LI Guobao (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-2299) API design of the paramserv function
Date Sun, 13 May 2018 13:50:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-2299?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

LI Guobao updated SYSTEMML-2299:
--------------------------------
    Description: 
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, 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 as well as the checkpointing strategy (e.g. rollback recovery).
And the function will return a trained model in struct format.

  was:The objective of “paramserv” built-in function is to update an initial or existing
model with configuration. An initial function signature would be _model'=paramserv(model,
X, y, X_val, y_val, upd=fun1, mode=SYNC, freq=EPOCH, agg=fun2, epochs=100, batchsize=64, k=7,
checkpointing=rollback)_. 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 as well as the checkpointing strategy
(e.g. rollback recovery). And the function will return a trained model in struct format.


> 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, X, y, X_val, y_val, upd=fun1, agg=fun2, mode=BSP, freq=EPOCH,
epochs=100, batchsize=64, k=7, 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 as well as the checkpointing strategy (e.g. rollback recovery).
And the function will return a trained model in struct format.



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