Apologies I don't think I understand your response. I usually have a submission script as job.sub that specifies the executable path. If I could instead use the actually executable script as the submission script that would be great too e.g. I usually do that with qsub as follows:
```
#!/homes/miranda9/.conda/envs/automl-meta-learning/bin/python
#PBS -V
#PBS -M me@xxxxxxxxx
#PBS -m abe
#PBS -lselect=1:ncpus=112
import sys
import os
for p in sys.path:
print(p)
print(os.environ)
```
alternatively if I could do string manipulation and get the filename from the end of my Exectuable path that would work too. Let me share my submission scripts.
```
####################
#
# Experiments script
# Simple HTCondor submit description file
#
# reference: https://gitlab.engr.illinois.edu/Vision/vision-gpu-servers/-/wikis/HTCondor-user-guide#submit-jobs
#
# chmod a+x test_condor.py
# chmod a+x experiments_meta_model_optimization.py
# chmod a+x meta_learning_experiments_submission.py
# chmod a+x download_miniImagenet.py
# chmod a+x ~/meta-learning-lstm-pytorch/main.py
# chmod a+x /home/miranda9/automl-meta-learning/automl-proj/meta_learning/datasets/rand_fc_nn_vec_mu_ls_gen.py
# chmod a+x /home/miranda9/automl-meta-learning/automl-proj/experiments/meta_learning/supervised_experiments_submission.py
# chmod a+x /home/miranda9/automl-meta-learning/results_plots/is_rapid_learning_real.py
# chmod a+x /home/miranda9/automl-meta-learning/test_condor.py
# condor_submit -i
# condor_submit job.sub
#
####################
# Executable = /home/miranda9/automl-meta-learning/automl-proj/experiments/meta_learning/supervised_experiments_submission.py
# Executable = /home/miranda9/automl-meta-learning/automl-proj/experiments/meta_learning/meta_learning_experiments_submission.py
# Executable = /home/miranda9/meta-learning-lstm-pytorch/main.py
# Executable = /home/miranda9/automl-meta-learning/automl-proj/meta_learning/datasets/rand_fc_nn_vec_mu_ls_gen.py
# Executable = /home/miranda9/automl-meta-learning/results_plots/is_rapid_learning_real.py
Executable = /home/miranda9/automl-meta-learning/test_condor.py
# Output Files
Log = $(SUBMIT_FILE).log$(CLUSTER)
Output = $(SUBMIT_FILE).o$(CLUSTER)
Error = $(SUBMIT_FILE).e$(CLUSTER)
# Use this to make sure 1 gpu is available. The key words are case insensitive.
# REquest_gpus = 1
# requirements = (CUDADeviceName != "Tesla K40m")
# requirements = (CUDADeviceName == "Quadro RTX 6000")
# requirements = ((CUDADeviceName = "Tesla K40m")) && (TARGET.Arch == "X86_64") && (TARGET.OpSys == "LINUX") && (TARGET.Disk >= RequestDisk) && (TARGET.Memory >= RequestMemory) && (TARGET.Cpus >= RequestCpus) && (TARGET.gpus >= Requestgpus) && ((TARGET.FileSystemDomain == MY.FileSystemDomain) || (TARGET.HasFileTransfer))
# requirements = (CUDADeviceName == "Tesla K40m")
# requirements = (CUDADeviceName == "GeForce GTX TITAN X")
# Note: to use multiple CPUs instead of the default (one CPU), use request_cpus as well
# Request_cpus = 4
Request_cpus = 16
# E-mail option
Notify_user = me@xxxxxxxxx
Notification = always
Environment = MY_CONDOR_JOB_ID= $(CLUSTER)
# "Queue" means add the setup until this line to the queue (needs to be at the end of script).
Queue
```
Thanks for the help!
Sincerely, Brando