content/v0.3.0/en/_sources/docs/programming-guide.txt

# Programming Guide

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To submit a training job, users must provide the configuration of the
four components shown in Figure 1:

  * a [NeuralNet](neural-net.html) describing the neural net structure with the detailed layer setting and their connections;
  * a [TrainOneBatch](train-one-batch.html) algorithm which is tailored for different model categories;
  * an [Updater](updater.html) defining the protocol for updating parameters at the server side;
  * a [Cluster Topology](distributed-training.html) specifying the distributed architecture of workers and servers.

The *Basic user guide* section describes how to submit a training job using
built-in components; while the *Advanced user guide* section presents details
on writing user's own main function to register components implemented by
themselves. In addition, the training data must be prepared, which has the same
[process](data.html) for both advanced users and basic users.

<img src="../_static/images/overview.png" align="center" width="400px"/>

<span><strong>Figure 1 - SINGA overview.</strong></span>



## Basic user guide

Users can use the default main function provided SINGA to submit the training
job. For this case, a job configuration file written as a google protocol
buffer message for the [JobProto](../api/classsinga_1_1JobProto.html) must be provided in the command line,

    ./bin/singa-run.sh -conf <path to job conf> [-resume]

`-resume` is for continuing the training from last
[checkpoint](checkpoint.html).
The [MLP](mlp.html) and [CNN](cnn.html)
examples use built-in components. Please read the corresponding pages for their
job configuration files. The subsequent pages will illustrate the details on
each component of the configuration.

## Advanced user guide

If a user's model contains some user-defined components, e.g.,
[Updater](updater.html), he has to write a main function to
register these components. It is similar to Hadoop's main function. Generally,
the main function should

  * initialize SINGA, e.g., setup logging.

  * register user-defined components.

  * create and pass the job configuration to SINGA driver


An example main function is like

    #include "singa.h"
    #include "user.h"  // header for user code

    int main(int argc, char** argv) {
      singa::Driver driver;
      driver.Init(argc, argv);
      bool resume;
      // parse resume option from argv.

      // register user defined layers
      driver.RegisterLayer<FooLayer>(kFooLayer);
      // register user defined updater
      driver.RegisterUpdater<FooUpdater>(kFooUpdater);
      ...
      auto jobConf = driver.job_conf();
      //  update jobConf

      driver.Train(resume, jobConf);
      return 0;
    }

The Driver class' `Init` method will load a job configuration file provided by
users as a command line argument (`-conf <job conf>`). It contains at least the
cluster topology and returns the `jobConf` for users to update or fill in
configurations of neural net, updater, etc. If users define subclasses of
Layer, Updater, Worker and Param, they should register them through the driver.
Finally, the job configuration is submitted to the driver which starts the
training.

We will provide helper functions to make the configuration easier in the
future, like [keras](https://github.com/fchollet/keras).

Users need to compile and link their code (e.g., layer implementations and the main
file) with SINGA library (*.libs/libsinga.so*) to generate an
executable file, e.g., with name *mysinga*.  To launch the program, users just pass the
path of the *mysinga* and base job configuration to *./bin/singa-run.sh*.

    ./bin/singa-run.sh -conf <path to job conf> -exec <path to mysinga> [other arguments]

The [RNN application](rnn.html) provides a full example of
implementing the main function for training a specific RNN model.