== Dev branch           ==
This branch contains some features that are not yet checked into main branch. To enlist this branch, run 

git checkout origin/Dev 

== To-do                ==
Add descriptions to LSTMnode
Add descriptions to 0/1 mask segmentation in feature reader, delay node, and crossentropywithsoftmax node
Change criterion node to use the 0/1 mask, following example in crossentropywithsoftmax node
Add description of encoder-decoder simple network builder
Add description of time-reverse node, simple network builder and NDL builder for bi-directional models

== Author of the README ==
   	Kaisheng Yao
        Microsoft Research
        email: kaisheny@microsoft.com

	Wengong Jin,
	Shanghai Jiao Tong University
	email: acmgokun@gmail.com

    Yu Zhang, Leo Liu
    CSAIL, Massachusetts Institute of Technology
    email: yzhang87@csail.mit.edu
    email: leoliu_cu@sbcglobal.net

    Guoguo Chen
    CLSP, Johns Hopkins University
    email: guoguo@jhu.edu

== Preeliminaries ==
To build the cpu version, you have to install intel MKL blas library or ACML library first. Note that ACML is free, where MKL may not be.

for MKL:
1. Download from https://software.intel.com/en-us/intel-mkl
2. You can modify variable MKL_PATH in makefile.cpu to change your mkl path.
Then add ${MKL_PATH}/mkl/lib/intel64, ${MKL_PATH}/mkl/lib/mic, ${MKL_PATH}/compiler/lib/intel64. ${MKL_PATH}/compiler/lib/mic to your ${LD_LIBRARY_PATH} to make sure the program links the library correctly.

for ACML:
1. Download from http://developer.amd.com/tools-and-sdks/cpu-development/amd-core-math-library-acml/
2. Modify ACML_PATH in the makefile.cpu and makefile.gpu to provide your ACML library path.
You need to add ${ACML_PATH}/lib to your ${LD_LIBRARY_PATH}.

for Kaldi:
1. In kaldi-trunk/tools/Makefile, uncomment # OPENFST_VERSION = 1.4.1, and
   re-install OpenFst using the makefile.
2. In kaldi-trunk/src/, do ./configure --shared; make depend -j 8; make -j 8;
   and re-compile Kaldi (the -j option is for parallelization).
3. Set KALDI_PATH in kaldi_vars.mk
4. When running the binaries, make sure you add $KALDI_PATH/tools/openfst/lib
   and $KALDI_PATH/src/lib/ to your $LD_LIBRARY_PATH 

To build the gpu version, you have to install NIVIDIA CUDA first
You can modify the path CUDA_PATH in makefile.cpu to change your cuda path
We use cuda-7.0 as default.
Then add ${CUDA_PATH}/lib, ${CUDA_PATH}/lib64 to your ${LD_LIBRARY_PATH} to make sure the program links to the library correctly.

... TODO: add documentation on nvml lib

== Build ==
To build the cpu version, run
	make DEVICE=cpu 
To build the gpu version, run
	make
To clean the compile, just run
	make DEVICE=cpu clean
or
	make clean

For release version, just add BUILDTYPE=release to the make command line.

== Run ==
All executables are in bin/ directory:
	cn.exe: The main executable for CNTK
	*.so: shared library for corresponding reader, these readers will be linked and loaded dynamically at runtime.

To run the executable, make sure bin/ is in your ${LD_LIBRARY_PATH}, if not, running cn.exe will fail when cn.exe tries to link the corresponding reader. Once it's done, run in command line:
	./cn.exe configFile=${your config file}

== Kaldi Reader ==
This is a HTKMLF reader and kaldi writer (for decode)

To build the cpu/gpu version, run
    make -f Makefile_kaldi.cpu/gpu

The feature section is like:

writer=[
    writerType=KaldiReader
    readMethod=blockRandomize
    frameMode=false
    miniBatchMode=Partial
    randomize=Auto
    verbosity=1
    ScaledLogLikelihood=[
        dim=$labelDim$
        Kaldicmd="ark:-" # will pipe to the Kaldi decoder latgen-faster-mapped
        scpFile=$outputSCP$ # the file key of the features
    ]
]

== Kaldi2 Reader ==
This is a kaldi reader and kaldi writer (for decode)

To build the cpu/gpu version, run
    make -f Makefile_kaldi2.cpu/gpu


The features section is different:

features=[
    dim=
    rx=
    scpFile=
    featureTransform=
]

rx is a text file which contains:

    one Kaldi feature rxspecifier readable by RandomAccessBaseFloatMatrixReader.
    'ark:' specifiers don't work; only 'scp:' specifiers work.

scpFile is a text file generated by running:

    feat-to-len FEATURE_RXSPECIFIER_FROM_ABOVE ark,t:- > TEXT_FILE_NAME

    scpFile should contain one line per utterance.

    If you want to run with fewer utterances, just shorten this file.
    (It will load the feature rxspecifier but ignore utterances not present in scpFile).

featureTransform is the name of a Kaldi feature transform file:
    
    Kaldi feature transform files are used for stacking / applying transforms to features.

    An empty string (if permitted by the config file reader?) or the special string: NO_FEATURE_TRANSFORM
    says to ignore this option.

********** Labels **********

The labels section is also different.

labels=[
    mlfFile=
    labelDim=
    labelMappingFile=
]

Only difference is mlfFile. mlfFile is a different format now. It is a text file which contains:

    one Kaldi label rxspecifier readable by Kaldi's copy-post binary.