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https://issues.apache.org/jira/browse/SINGA-81?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15076286#comment-15076286
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ASF subversion and git services commented on SINGA-81:
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Commit 7d43e27330581c3eecbd44a04f0c8691c3502ec6 in incubator-singa's branch
refs/heads/master from chonho
[ https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;h=7d43e27 ]
SINGA-81 Add Python Helper, which enables users to construct a model (JobProto)
and run Singa in Python
- Add wrapper API "Driver::Train(const std::string Job_conf)" for python.
- Add wrapper API "Driver::Test(const std::string Job_conf)" for python.
- Python codes (1) construct a model (JobProto), and (2) run singa
- Users are supposed to generate 'usermodel.py'
. examples are provided, e.g., cifar10_cnn.py, mnist_mlp.py, mnist_rbm.py
. 'cluster.conf' is required to maintain cluster information
- Users are supposed to run it as follows. e.g.,
{code}
cd SINGA_ROOT
bin/singa-run.sh -conf tool/python/examples/cluster.conf -exe
tool/python/examples/mnist_mlp.py
{code}
- Note: in job.proto, 'required' rule of the following fields should be changed
to 'optional'
. JobProto: name, neuralnet, train_one_batch, updater, train_steps
. ClusterProto: workspace
. workspace field can be set in either (i) cluster.conf or (ii) python code
- __init__.py is required in the following directories
. singa
. singa/utils
. singa/datasets
. examples
- Add StoreResult() that takes care of training results
. in SingaRun() called by fit() or evaluate()
. read logfile
. store accuracy, loss, ppl, se, etc. in dictionary format
- Parameter initialization
. Parameter class is internally used
. Weight follows gaussian distribution at default
. Bias follows constant at default
. As an option, users can explicitly specify parameter (e.g., *_parameter.py)
- Removed dataset/ae.py and dataset/rbm.py
. RBM and Autoencoder examples use Mnist dataset
> Add Python Helper, which enables users to construct a model (JobProto) and
> run Singa in Python
> ----------------------------------------------------------------------------------------------
>
> Key: SINGA-81
> URL: https://issues.apache.org/jira/browse/SINGA-81
> Project: Singa
> Issue Type: New Feature
> Reporter: Lee Chonho
>
> Proposed design v1
> - (1) have a class named Builder
> * (2) use Boost::parameter library (+ associated necessary header files)
> (1)
> Builder class implements api-like functions to configure JobProto including
> NetProto (LayerProto), ClusterProto, UpdaterProto, etc.
> Two options
> a. users call Builder's functions in main.cc like
> {code}
> JobProto jobproto;
> Builder builder( &jobproto, "job name" );
> builder.xxx(xxx) // add data layer
> builder.xxx(xxx) // add parser layer
> ... etc. ...
> {code}
> b. we set main.cc like below and users call Builders functions in Construct()
> {code}
> JobProto jobproto;
> Builder builder( &jobproto, "model name" );
> builder.Construct()
> {code}
> (2)
> Planning to use header-only files from Boost library
> - if the necessary files are small enough
> - because we can use "named arguments" feature with no restriction of # of
> arguments, order, types.
> - because function will be intuitive, and adding users' own proto in a
> straightforward way.
> Example is here. http://theboostcpplibraries.com/boost.parameter
> By following the example, we can do like
> {code}
> BOOST_PARAMETER_MEMBER_FUNCTION(
> (char*), AddLayerData, tag,
> (required
> (type, (int)) (name, (char*)) (src, (char*))
> )
> (optional
> (path, (char*), *) (bsize (int) *)
> ) )
> {
> ... // set values
> return name;
> }
> {code}
> Then, users can add a datalayer by
> {code}
> L1 = builder.AddLayerData(kShardData, "data", null, _path="train_shard",
> _bsize=1000);
> {code}
> (TODO)
> - make use of google protobuf reflection for efficient parameter setting
> - need to avoid multiple calls for adding same/similar layers
> Any suggestion, design idea, comments please.
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