Commands

run ‘amlhpc –help’ for command-specific options

# sinfo

Show the available partitions. sinfo does not take any options.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sinfo PARTITION AVAIL VM_SIZE NODES STATE f16s UP STANDARD_F16S_V2 37 hc44 UP STANDARD_HC44RS 3 hbv2 UP STANDARD_HB120RS_V2 4 login-vm UP STANDARD_DS12_V2 None

# squeue

Show the queue with historical jobs. squeue does not take any options.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ squeue JOBID NAME PARTITION STATE TIME crimson_root_52y4l9yfjd sbatch f16s polite_lock_v8wyc9gnx9 runscript.sh f16s

# qstat / bjobs

Job-control listing for the PBS (`qstat`) and LSF (`bjobs`) users. Both are backed by the same
Azure Machine Learning job list as `squeue`, but add the job STATE column. Neither takes any options.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ qstat JOBID NAME PARTITION STATE jolly_card_p6yh0phzxm jolly_card_p6yh login-5n2kkmvhk Completed cool_pig_mwhdcjs72n localtest-f4s f4s Failed

`bjobs` produces identical output; use whichever matches your scheduler muscle memory.

# qdel / bkill / scancel

Cancel one or more running jobs by JOBID. `qdel` (PBS), `bkill` (LSF) and `scancel` (Slurm) are
equivalent; all accept one or more JOBIDs (as shown by `qstat`/`bjobs`/`squeue`).

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ qdel jolly_card_p6yh0phzxm qdel: cancellation requested for job ‘jolly_card_p6yh0phzxm’

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ bkill jobid1 jobid2

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ scancel wheat_sand_gr2xcdpl2w scancel: cancellation requested for job ‘wheat_sand_gr2xcdpl2w’

# qsub

Submit a job the PBS way. `qsub` is a thin, PBS-style front-end for `sbatch`: `-q` selects the queue,
`-l nodes=N` (or `select=N`, or `nodes=N:ppn=P`) sets the node count, and `-N` is an informational
job name. A single existing file is treated as a script; anything else is run as a command line.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ qsub -q f16s hostname gifted_engine_yq801rygm2

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ qsub -q hbv2 -l nodes=4 ./runscript.sh

PBS-style options map onto sbatch: `-q/--queue` -> partition, `-l nodes=/select=` -> nodes,
`-N/--name` is informational (AML assigns the JOBID). `--container`/`-e` pass through.

# bsub

Submit a job the LSF way. `bsub` is a thin, LSF-style front-end for `sbatch`: the job command
follows the options (rather than using `--wrap`), and `-q` selects the queue. A single existing
file is treated as a script; anything else is run as a command line.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ bsub -q f16s hostname gifted_engine_yq801rygm2

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ bsub -q hbv2 -n 4 ./runscript.sh

LSF-style options map onto sbatch: `-q/--queue` -> partition, `-n/--num-slots` -> nodes,
`-J/--job-name` is informational (AML assigns the JOBID). `--container`/`-e` pass through.

# sbatch

Submit a job, either as a command through the `--wrap` option or a (shell) script. sbatch uses several options, which are explained in sbatch --help.
Quite a bit of sbatch options are supported such as running multi-node MPI jobs with the option to set the amount of nodes to be used.
Also array jobs are supported with the default `--array` option.

Some additional options are introduced to support e.g. the data-handling methods available in AML. These are explaned in [data.md](data.md). 

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch -p f16s –wrap=”hostname” gifted_engine_yq801rygm2

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch –help usage: sbatch [-h] [-a ARRAY] -p PARTITION [-N NODES] [-w WRAP] [script]

sbatch: submit jobs to Azure Machine Learning

positional arguments: script script to be executed

optional arguments: -h, –help show this help message and exit -a ARRAY, –array ARRAY index for array jobs -p PARTITION, –partition PARTITION set compute partition where the job should be run. Use to view available partitions -N NODES, –nodes NODES amount of nodes to use for the job -w WRAP, –wrap WRAP command line to be executed, should be enclosed with quotes

# srun

Run a job on the login ComputeInstance instead of an AmlCompute cluster. `srun` mirrors `sbatch`, but the
compute target is a ComputeInstance. With no `-p` the login CI is auto-discovered (the single computeinstance
in the workspace). Use this to run control-plane tasks on the login node itself without SSH.

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ srun –wrap=”hostname” stoic_beach_c4jpkltdrl

(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ srun –help usage: srun [-h] [–container CONTAINER] [-e ENVIRONMENT] [-p PARTITION] [-v] [-w WRAP] [script]

srun: run jobs on the login ComputeInstance

positional arguments: script runscript to be executed

optional arguments: -h, –help show this help message and exit –container CONTAINER container image for the job to run in -e ENVIRONMENT, –environment ENVIRONMENT Azure Machine Learning environment, may use @latest -p PARTITION, –partition PARTITION ComputeInstance to run on. Defaults to auto-discovered login CI. Use to view partitions -w WRAP, –wrap WRAP command line to be executed, should be enclosed with quotes

Note: like every AML command job, the command runs inside the environment's container (sharing the CI's
network namespace, so it sees the CI private IP), not the bare CI host OS.

If you encounter a scenario or option that is not supported yet or behaves unexpected, please create an issue and explain the option and the scenario.