gpu-job.submit

# Job requirements - ensure we are running on a newer GPU
# node and have enough resources to execute our code
# Tensorflow also requires AVX instruction set
Requirements = GPUs_Capability >= 8.0 && GPUs_DriverVersion > 11.0 && HAS_AVX2 == True && OSG_HOST_KERNEL_VERSION >= 31000
request_cpus = 1
request_gpus = 1
request_memory = 8 GB
request_disk = 1 GB

# Container image to run the job in
container_image = /cvmfs/singularity.opensciencegrid.org/opensciencegrid/tensorflow-gpu:2.2-cuda-10.1/

# Executable is the program your job will run It's often useful
# to create a shell script to "wrap" your actual work.
Executable = job-wrapper.sh
Arguments = 

# Inputs/outputs - in this case we just need our python code.
# If you leave out transfer_output_files, all generated files comes back
transfer_input_files = test.py
#transfer_output_files = 

# Error and output are the error and output channels from your job
# that HTCondor returns from the remote host.
Error = $(Cluster).$(Process).error
Output = $(Cluster).$(Process).output

# The LOG file is where HTCondor places information about your
# job's status, success, and resource consumption.
Log = $(Cluster).log

# Send the job to Held state on failure. 
#on_exit_hold = (ExitBySignal == True) || (ExitCode != 0)

# Periodically retry the jobs every 1 hour, up to a maximum of 5 retries.
#periodic_release =  (NumJobStarts < 5) && ((CurrentTime - EnteredCurrentStatus) > 60*60)

# queue is the "start button" - it launches any jobs that have been
# specified thus far.
queue 1