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https://issues.apache.org/jira/browse/SYSTEMML-2299?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16540715#comment-16540715
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LI Guobao commented on SYSTEMML-2299:
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[~mboehm7], in fact, until now, it seems that the arguments "val_features" and
"val_labels" have not been used inside paramserv function. For me, they are
leveraged to calculate the model precision and we use a UDF to do it but not
inside our paramserv function. So do they have some other utilities?
> 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
> Fix For: SystemML 1.2
>
>
> 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=paramsList, features=X, labels=Y, val_features=X_val,
> val_labels=Y_val, upd="fun1", agg="fun2", mode="LOCAL", utype="BSP",
> freq="BATCH", epochs=100, batchsize=64, k=7, scheme="disjoint_contiguous",
> hyperparams=params, checkpointing="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 format.
> *Inputs*:
> * model <list>: a list consisting of the weight and bias matrices
> * features <matrix>: training features matrix
> * labels <matrix>: training label matrix
> * val_features <matrix>: validation features matrix
> * val_labels <matrix>: validation label matrix
> * upd <string>: the name of gradient calculation function
> * agg <string>: the name of gradient aggregation function
> * mode <string> (options: LOCAL, REMOTE_SPARK): the execution backend where
> the parameter is executed
> * utype <string> (options: BSP, ASP, SSP): the updating mode
> * freq <string> [optional] (default: BATCH) (options: EPOCH, BATCH) : the
> frequence of updates
> * epochs <integer>: the number of epoch
> * batchsize <integer> [optional] (default: 64): the size of batch, if the
> update frequence is "EPOCH", this argument will be ignored
> * k <integer> [optional] (default: number of vcores, otherwise vcores / 2 if
> using openblas): the degree of parallelism
> * scheme <string> [optional] (default: disjoint_contiguous) (options:
> disjoint_contiguous, disjoint_round_robin, disjoint_random,
> overlap_reshuffle): the scheme of data partition, i.e., how the data is
> distributed across workers
> * hyperparams <list> [optional]: a list consisting of the additional hyper
> parameters, e.g., learning rate, momentum
> * checkpointing <string>[optional] (default: NONE) (options: NONE, EPOCH,
> EPOCH10) : 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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