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Runtime error
| # ====== About run.pl, queue.pl, slurm.pl, and ssh.pl ====== | |
| # Usage: <cmd>.pl [options] JOB=1:<nj> <log> <command...> | |
| # e.g. | |
| # run.pl --mem 4G JOB=1:10 echo.JOB.log echo JOB | |
| # | |
| # Options: | |
| # --time <time>: Limit the maximum time to execute. | |
| # --mem <mem>: Limit the maximum memory usage. | |
| # -–max-jobs-run <njob>: Limit the number parallel jobs. This is ignored for non-array jobs. | |
| # --num-threads <ngpu>: Specify the number of CPU core. | |
| # --gpu <ngpu>: Specify the number of GPU devices. | |
| # --config: Change the configuration file from default. | |
| # | |
| # "JOB=1:10" is used for "array jobs" and it can control the number of parallel jobs. | |
| # The left string of "=", i.e. "JOB", is replaced by <N>(Nth job) in the command and the log file name, | |
| # e.g. "echo JOB" is changed to "echo 3" for the 3rd job and "echo 8" for 8th job respectively. | |
| # Note that the number must start with a positive number, so you can't use "JOB=0:10" for example. | |
| # | |
| # run.pl, queue.pl, slurm.pl, and ssh.pl have unified interface, not depending on its backend. | |
| # These options are mapping to specific options for each backend and | |
| # it is configured by "conf/queue.conf" and "conf/slurm.conf" by default. | |
| # If jobs failed, your configuration might be wrong for your environment. | |
| # | |
| # | |
| # The official documentaion for run.pl, queue.pl, slurm.pl, and ssh.pl: | |
| # "Parallelization in Kaldi": http://kaldi-asr.org/doc/queue.html | |
| # =========================================================~ | |
| # Select the backend used by run.sh from "local", "stdout", "sge", "slurm", or "ssh" | |
| cmd_backend="local" | |
| # Local machine, without any Job scheduling system | |
| if [ "${cmd_backend}" = local ]; then | |
| # The other usage | |
| export train_cmd="utils/run.pl" | |
| # Used for "*_train.py": "--gpu" is appended optionally by run.sh | |
| export cuda_cmd="utils/run.pl" | |
| # Used for "*_recog.py" | |
| export decode_cmd="utils/run.pl" | |
| # Local machine, without any Job scheduling system | |
| elif [ "${cmd_backend}" = stdout ]; then | |
| # The other usage | |
| export train_cmd="utils/stdout.pl" | |
| # Used for "*_train.py": "--gpu" is appended optionally by run.sh | |
| export cuda_cmd="utils/stdout.pl" | |
| # Used for "*_recog.py" | |
| export decode_cmd="utils/stdout.pl" | |
| # "qsub" (SGE, Torque, PBS, etc.) | |
| elif [ "${cmd_backend}" = sge ]; then | |
| # The default setting is written in conf/queue.conf. | |
| # You must change "-q g.q" for the "queue" for your environment. | |
| # To know the "queue" names, type "qhost -q" | |
| # Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler. | |
| export train_cmd="utils/queue.pl" | |
| export cuda_cmd="utils/queue.pl" | |
| export decode_cmd="utils/queue.pl" | |
| # "sbatch" (Slurm) | |
| elif [ "${cmd_backend}" = slurm ]; then | |
| # The default setting is written in conf/slurm.conf. | |
| # You must change "-p cpu" and "-p gpu" for the "partion" for your environment. | |
| # To know the "partion" names, type "sinfo". | |
| # You can use "--gpu * " by defualt for slurm and it is interpreted as "--gres gpu:*" | |
| # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}". | |
| export train_cmd="utils/slurm.pl" | |
| export cuda_cmd="utils/slurm.pl" | |
| export decode_cmd="utils/slurm.pl" | |
| elif [ "${cmd_backend}" = ssh ]; then | |
| # You have to create ".queue/machines" to specify the host to execute jobs. | |
| # e.g. .queue/machines | |
| # host1 | |
| # host2 | |
| # host3 | |
| # Assuming you can login them without any password, i.e. You have to set ssh keys. | |
| export train_cmd="utils/ssh.pl" | |
| export cuda_cmd="utils/ssh.pl" | |
| export decode_cmd="utils/ssh.pl" | |
| else | |
| echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2 | |
| return 1 | |
| fi | |