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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, 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 format of struct. (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, 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).)
> 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 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 format of struct.
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