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From "Matthias Boehm (JIRA)" <>
Subject [jira] [Commented] (SYSTEMML-2299) API design of the paramserv function
Date Mon, 14 May 2018 18:39:00 GMT


Matthias Boehm commented on SYSTEMML-2299:

[~Guobao] initially I would leave it up to the user by allowing the specification via a parameter
such as {{mode}} (maybe rename the existing mode to utype?). This is similar to parfor were
a user can specify the execution mode as {{LOCAL}}, {{REMOTE_MR}} or {{REMOTE_SPARK}}. While
building the runtime, let's make this parameter mandatory. Later we can generalize that and
automatically decide the execution mode if not provided by the user: for example, we could
compute a cost estimate based on the number of floating point operations per batch, scaled
by the number of epochs and datasize. If a certain minimum cost threshold is exceeded and
all memory constraints are met, we could automatically route it to distributed operations.

> API design of the paramserv function
> ------------------------------------
>                 Key: SYSTEMML-2299
>                 URL:
>             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, features=X, labels=Y, val_features=X_val, val_labels=Y_val, upd="fun1",
agg="fun2", mode="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
> *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: BSP, ASP, SSP): the updating mode
>  * freq <string> (options: EPOCH, BATCH): the frequence of updates
>  * epochs <integer>: the number of epoch
>  * batchsize <integer> [optional]: the size of batch, if the update frequence is
"EPOCH", this argument will be ignored
>  * k <integer>: the degree of parallelism
>  * scheme <string> (options: disjoint_contiguous, disjoint_round_robin, disjoint_random,
overlap_reshuffle): the scheme of data partition, i.e., how the data is distributed across
>  * hyperparams <list> [optional]: a list consisting of the additional hyper parameters,
e.g., learning rate, momentum
>  * checkpointing <string> (options: NONE(default), EPOCH, EPOCH10) [optional]:
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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