update the docs:

- update the history (inputs from Honza Cernocky),
- fix URLs in nnet1 docs,
- add 'usage' to cuda-gpu-available.

src/doc/dnn1.dox

@@ -35,13 +35,13 @@ show some \ref dnn1_advanced_features, and do a light introduction to the \ref d
 <hr><!-- #################################################################################################################### -->
 
 \section dnn1_toplevel_scripts Top-level script
-Let's have a look at the script <b><a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/local/nnet/run_dnn.sh">egs/wsj/s5/local/nnet/run_dnn.sh</a></b>.
+Let's have a look at the script <b><a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/local/nnet/run_dnn.sh">egs/wsj/s5/local/nnet/run_dnn.sh</a></b>.
 This script assumes to use a single CUDA GPU, and that kaldi was compiled with CUDA (check for 'CUDA = true' in src/kaldi.mk).
 Also we assume that 'cuda_cmd' is set properly in egs/wsj/s5/cmd.sh either to a GPU cluster node using 'queue.pl' or to a local machine using 'run.pl'.
 And finally the script assumes we already have a SAT GMM system exp/tri4b and corresponding fMLLR transforms, as generated by egs/wsj/s5/run.sh.
 Note that for other databases the run_dnn.sh is typically in the same location s5/local/nnet/run_dnn.sh.
 
-The script <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/local/nnet/run_dnn.sh">egs/wsj/s5/local/nnet/run_dnn.sh</a> is split into several stages:
+The script <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/local/nnet/run_dnn.sh">egs/wsj/s5/local/nnet/run_dnn.sh</a> is split into several stages:
 
 0. <b>storing 40-dimensional fMLLR features to disk, steps/nnet/make_fmllr_feats.sh,</b>
 this simplifies the training scripts, the 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN
@@ -100,7 +100,7 @@ Besides the DNN recipe, there are also other example scripts which can be handy:
 <hr><!-- #################################################################################################################### -->
 
 \section dnn1_training_script_internals Training script internals
-The main neural network training script <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train.sh">steps/nnet/train.sh</a> is invoked as:
+The main neural network training script <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train.sh">steps/nnet/train.sh</a> is invoked as:
 
 \verbatim
 steps/nnet/train.sh <data-train> <data-dev> <lang-dir> <ali-train> <ali-dev> <exp-dir>
@@ -111,11 +111,11 @@ The <lang-dir> is used only in the special case when using LDA feature-transform
 The output (i.e. the trained networks and logfiles) goes into <exp-dir>.
 
 Internally the script prepares the feature+target pipelines, generates a neural-network prototype and initialization, creates feature_transform and calls the scheduler script 
-<a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a>,
+<a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a>,
 which runs the training epochs and controls the learning rate.
 
 
-<b>While looking inside <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train.sh">steps/nnet/train.sh</a> we see:</b>
+<b>While looking inside <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train.sh">steps/nnet/train.sh</a> we see:</b>
 
 1. CUDA is required, the scripts exit if no GPU was detected or was CUDA not compiled in (one can still use '--skip-cuda-check true' to run on CPU, but it is 10-20x slower)
 
@@ -165,12 +165,12 @@ $ cat exp/dnn5b_pretrain-dbn_dnn/nnet.proto
 
 7. the network is initialized by : \ref nnet-initialize.cc , the DBN gets prepended in the next step using \ref nnet-concat.cc
 
-8. finally the training gets called by running scheduler script <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a>
+8. finally the training gets called by running scheduler script <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a>
 
 Note : both neural networks and feature transforms can be viewed by \ref nnet-info.cc, or shown in ascii by \ref nnet-copy.cc
 
 
-<b>While looking inside <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a> we see:</b>
+<b>While looking inside <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a> we see:</b>
 
 the initial cross-validation run and the main for-loop over $iter which runs the epochs and controls the learning rate. Typically, the train_scheduler.sh is called from train.sh.
 - the default learning-rate scheduling is based on the relative improvement of the objective function: 
@@ -310,7 +310,7 @@ AddMat 174.307s
 AddMatMat 1922.11s
 \endverbatim
 
-<b> Running <a href="http:https://sourceforge.net/p/kaldi/code/HEAD/tree/trunk/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a> directly:</b>
+<b> Running <a href="https:https://github.com/kaldi-asr/kaldi/blob/master/egs/wsj/s5/steps/nnet/train_scheduler.sh">steps/nnet/train_scheduler.sh</a> directly:</b>
 - The script train_scheduler.sh can be called outside train.sh, it allows to override the default NN-input and NN-target streams, which can be handy.
 - However the script assumes everything is set-up correctly, and there are almost no sanity checks, which makes it suitable for more advanced users only.
 - It is highly recommended to have a look at how train_scheduler.sh is usually called before trying to call it directly.

src/doc/history.dox

@@ -54,21 +54,25 @@
  Sandeep Boda, Sandeep Reddy and Haihua Xu (who helped with coding, code cleanup
  and documentation); we were visited by Michael Riley (who helped us to understand
  OpenFst and gave some lectures on FSTs), and would like to acknowledge the help of
- Honza Cernocky (for allowing us to have the workshop and helping to organize it),
+ Honza Cernocky (for negotiating the venue and some support for the workshop from
+ the Faculty of Information Technology of BUT and helping to organize it),
  Renata Kohlova (administration), and Tomas Kasparek (system administration).
  It is possible that this list of contributors contains
  oversights; any important omissions are unlikely to be intentional.
 
  A lot of code was written during the summer of 2010 but we still did not have a
  complete working system. Some of the participants of the 2010 workshop
  continued working to complete the toolkit and get a working set of training scripts.
- The code was released on May 14th, 2011.
+ The code was released on May 14th, 2011, and presented to public at ICASSP 2011 
+ in Prague,
+ <a href="https://www.superlectures.com/icassp2011/category.php?lang=en&id=131">
+ see the recordings</a>.
 
  Since the initial release, Kaldi has been maintained and developed to a large
  extent by Daniel Povey, working at Microsoft Research until early 2012 and
  since then at Johns Hopkins University; but also with major contributions by
  others: notably Karel Vesely, who developed the neural-net training framework,
- and Arnab Ghoshal, who co-ordinated the acoustic modeling work early on; but
+ and Arnab Ghoshal, who coordinated the acoustic modeling work early on; but
  also other major contributors whom we do not name here because it is too hard
  to determine where to cut off the list; and a long tail of minor contributors;
  the total number of people who have contributed code or scripts or patches is

src/nnetbin/cuda-gpu-available.cc

@@ -40,6 +40,17 @@ void TestGpuComputation() {
 #endif
 
 int main(int argc, char *argv[]) try {
+
+ /* only for Doxygen documentation, never shown in command line */
+ const char *usage =
+ "Test if there is a GPU available, and if the GPU setup is correct.\n"
+ "A GPU is acquired and a small computation is done\n"
+ "(generating a random matrix and computing softmax for its rows).\n"
+ "\n"
+ "exit-code: 0 = success, 1 = compiled without GPU support, -1 = error\n"
+ "\n"
+ "Usage: cuda-gpu-available\n";
+
  char hostname[100] = "UNKNOWN-HOSTNAME";
 #if !defined(_MSC_VER) && !defined(__CYGWIN__)
  if (gethostname(hostname, 100)) {