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https://issues.apache.org/jira/browse/SYSTEMML-2299?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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LI Guobao updated SYSTEMML-2299:
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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, g_cal_fun, upd=fun1,
mode=SYNC, freq=EPOCH, agg=fun2, epochs=100, batchsize=64, k=7,
checkpointing=rollback)_. We are interested in providing the model, the
training features and labels, the validation features and labels, the gradient
calculation function, the batch update function, the update strategy (e.g.
sync, async, hogwild!, stale-synchronous), the update frequency (e.g. epoch or
batch), the aggregation function, the number of epoch, the batch size, the
degree of parallelism as well as the checkpointing strategy (e.g. rollback
recovery). (was: The objective of “paramserv” built-in function is to update
an initial or existing model with configuration. An initial function signature
is illustrated in Figure 1. We are interested in providing the model, the
training features and labels, the validation features and labels, the gradient
calculation function, the batch update function, the update strategy (e.g.
sync, async, hogwild!, stale-synchronous), the update frequency (e.g. epoch or
batch), the aggregation function, the number of epoch, the batch size, the
degree of parallelism as well as the checkpointing strategy (e.g. rollback
recovery).)
> 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, g_cal_fun, upd=fun1, mode=SYNC,
> freq=EPOCH, agg=fun2, epochs=100, batchsize=64, k=7,
> checkpointing=rollback)_. We are interested in providing the model, the
> training features and labels, the validation features and labels, the
> gradient calculation function, the batch update function, the update strategy
> (e.g. sync, async, hogwild!, stale-synchronous), the update frequency (e.g.
> epoch or batch), the aggregation function, the number of epoch, the batch
> size, the degree of parallelism as well as the checkpointing strategy (e.g.
> rollback recovery).
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