I am trying to build HMM-GMM model by using GPU for my own data, Nvidia drivers are also installed properly, CUDA 7.5 is also installed properly but I am getting following error while running.
umar@R730_UP:/data/shushant/speech_to_text/speech_combine_ng_dnn$ ./myrun.sh
============================================================================
MFCC Feature Extration & CMVN for Training and Test set
============================================================================
steps/make_mfcc.sh --cmd
queue.pl --nj 4 data/train exp/make_mfcc/train mfcc
utils/validate_data_dir.sh: Successfully validated data-directory data/train
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
queue.pl: Error submitting jobs to queue (return status was 32512)
queue log file is exp/make_mfcc/train/q/make_mfcc_train.log, command was qsub -v PATH -cwd -S /bin/bash -j y -l arch=*64* -o exp/make_mfcc/train/q/make_mfcc_train.log -t 1:4 /data/shushant/speech_to_text/speech_combine_ng_dnn/exp/make_mfcc/train/q/make_mfcc_train.sh >>exp/make_mfcc/train/q/make_mfcc_train.log 2>&1
Output of qsub was: sh: 1: qsub: not found
I opened the log file and error is "sh: 1: qsub: not found"
Here is the ./configure command output.
umar@R730_UP:/data/shushant/speech_to_text/kaldi/src$ ./configure --cudatk-dir=/usr/local/cuda-7.5/
Configuring ...
Checking compiler g++ ...
Checking OpenFst library in /data/shushant/speech_to_text/kaldi/tools/openfst ...
Checking cub library in /data/shushant/speech_to_text/kaldi/tools/cub ...
Doing OS specific configurations ...
On Linux: Checking for linear algebra header files ...
Using ATLAS as the linear algebra library.
Successfully configured ATLAS with ATLASLIBS=/usr/lib/libatlas.so.3 /usr/lib/libf77blas.so.3 /usr/lib/libcblas.so.3 /usr/lib/liblapack_atlas.so.3
Using CUDA toolkit /usr/local/cuda-7.5 (nvcc compiler and runtime libraries)
Info: configuring Kaldi not to link with Speex (don't worry, it's only needed if you
intend to use 'compress-uncompress-speex', which is very unlikely)
SUCCESS
To compile: make clean -j; make depend -j; make -j
... or e.g. -j 10, instead of -j, to use a specified number of CPUs.
I attached here my run.sh and cmd.sh. Can you help me how to use GPU for this training?