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From "LI Guobao (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-2299) API design of the paramserv function
Date Fri, 11 May 2018 14:39:00 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 _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, 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 the weights,
the biases and the hyperparameters), the training features and labels, the validation features
and labels, the batch update function, 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 _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, 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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