[jira] [Created] (SYSTEMML-2316) Data type propagation issue with mixed type accumulate
Matthias Boehm created SYSTEMML-2316:
Summary: Data type propagation issue with mixed type accumulate
Key: SYSTEMML-2316
URL: https://issues.apache.org/jira/browse/SYSTEMML-2316
Project: SystemML
Issue Type: Bug
Reporter: Matthias Boehm
Mixed type accumulation such as {{B += i}} fail with validation errors due to
mistakenly inferred scalar type of B.
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[jira] [Closed] (SYSTEMML-2315) JMLC API extension for passing multiple dml scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-2315?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Matthias Boehm closed SYSTEMML-2315. Resolution: Fixed Assignee: Matthias Boehm Fix Version/s: SystemML 1.2 > JMLC API extension for passing multiple dml scripts > --- > > Key: SYSTEMML-2315 > URL: https://issues.apache.org/jira/browse/SYSTEMML-2315 > Project: SystemML > Issue Type: Task >Reporter: Matthias Boehm >Assignee: Matthias Boehm >Priority: Major > Fix For: SystemML 1.2 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Resolved] (SYSTEMML-2298) Preparation of dev environment
[ https://issues.apache.org/jira/browse/SYSTEMML-2298?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] LI Guobao resolved SYSTEMML-2298. - Resolution: Fixed Fix Version/s: SystemML 1.2 > Preparation of dev environment > -- > > Key: SYSTEMML-2298 > URL: https://issues.apache.org/jira/browse/SYSTEMML-2298 > Project: SystemML > Issue Type: Sub-task >Reporter: LI Guobao >Assignee: LI Guobao >Priority: Major > Fix For: SystemML 1.2 > > > During the bonding time, all the development environment should be well > prepared. The native library OpenBLAS should be installed in order to run the > MNIST LeNet example. And then by leveraging the MNIST LeNet data generator > ([http://leon.bottou.org/projects/infimnist]), we could generate 256k > instances to train the model. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (SYSTEMML-2299) API design of the paramserv function
[ 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 (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 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 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.) > 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 (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 as well as the checkpointing strategy > (e.g. rollback recovery). And the function will return a trained model in > struct format. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (SYSTEMML-2299) API design of the paramserv function
[ 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
