This will be illustrated using an actual question that came to our support team:

Support Request: 
================
I’ve been trying for some time to get gene regulatory analysis to run on a large dataset on Gowonda. Originally I tried doing this using Genie3, which begins running but errors out after about 5 hours due to exceeding memory, even if I run it on bigmem requesting 900GB.

There’s a new package – arboreto – that has a better version of Genie3 but also a much more computationally efficient methodology called grnboost2. I’ve made a new conda environment and have installed arboreto, however can’t get grnboost2 to run. The files load fine, but it stalls at the grnboost2 step with the error ‘TypeError: descriptor '__call__' for 'type' objects doesn't apply to a 'property' object’. Help forums indicate that that this may be due to an issue with the Python and Dask versions. I’ve tried making different Conda environments using different version combinations, but still can’t get it to work.

Our Answer:
===========
We looked at this page: https://github.com/aertslab/pySCENIC/issues/163
and we think the 1st solution using singularity containers is good. Containers are better suited when issues with version mismatch occurs (e.g specific versions of python, dask etc).

We went to this link as directed from above link.

This is the copy and paste of the section using Apptainer (used to be called Singularity)
>>>>>> Clip from the link >>>>>>>>>>>>
Singularity/Apptainer
Singularity/Apptainer images can be build from the Docker Hub image as source:
# pySCENIC CLI version.
singularity build aertslab-pyscenic-0.12.1.sif docker://aertslab/pyscenic:0.12.1
apptainer build aertslab-pyscenic-0.12.1.sif docker://aertslab/pyscenic:0.12.1

# pySCENIC CLI version + ipython kernel + scanpy.
singularity build aertslab-pyscenic-scanpy-0.12.1-1.9.1.sif docker://aertslab/pyscenic_scanpy:0.12.1_1.9.1
apptainer build aertslab-pyscenic-0.12.1-1.9.1.sif docker://aertslab/pyscenic_scanpy:0.12.1_1.9.1
>>>>>>>>>End of the clip from the link >>>>>>>>>>

To download the container from any docker link at Griffith, we have to alter the commands slightly (also documented on the singularity page of our wiki) 
(The change is to replace docker:// with docker://public.docker.itc.griffith.edu.au/)
So the altered command becomes:
singularity build aertslab-pyscenic-0.12.1.sif docker://public.docker.itc.griffith.edu.au/aertslab/pyscenic:0.12.1
singularity build aertslab-pyscenic-scanpy-0.12.1-1.9.1.sif docker://public.docker.itc.griffith.edu.au/aertslab/pyscenic_scanpy:0.12.1_1.9.1

After sourcing the proxy file (source /usr/local/bin/s3proxy.sh), you can run the above two commands to download those containers.
We have done this for you and placed the containers in this directory: ~/Containers

To check the functionality of the container, please do this:
singularity shell -B /scratch/snumber:/scratch --pwd /scratch/snumber:/scratch --pwd /scratch  /export/home/snumber/Containers/aertslab-pyscenic-0.12.1.sif 
>>>>>>>>>
From inside the container shell, you can run a few commands to check for functionality
e.g:
Apptainer>cd scripts
Apptainer> python grnboost3.py 
INFO:root:Initializing Dask client...
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
DEBUG:asyncio:Using selector: EpollSelector
INFO:root:Dask client initialized: <Client: 'tcp://127.0.0.1:41411' processes=9 threads=72, memory=251.23 GiB>
INFO:root:Loading and transposing expression data...
ERROR:root:An error occurred: [Errno 2] No such file or directory: 'FinalCounts.csv'
<snip>
exit

>>>>>>>>>>>>>
This looks ok (you need to supply the missing files FinalCounts.csv)

If you are happy with this, go to the next step:
(Please note: We have put all the sample pbs and run scripts in your ~scratch/scripts folder)


Next part is to create a pbs script and a run script 

mkdir /scratch/snumber/scripts
mkdir /scratch/snumber/scripts/data
cd  /scratch/snumber/scripts
You can copy all the scripts into this folder as well as create a run script
e.g: cp grnboost3.py ~/scratch/scripts/grnboost3.py

Here is a sample pbs script to use 6 cpus
>>>>>>>>>>>>>>>
#!/bin/bash
#PBS -m e
#PBS -M YourEmail@griffith.edu.au
#PBS -N GRN_pearl
#PBS -q workq
#PBS -l select=1:ncpus=6:mem=66gb,walltime=30:00:00
cd  $PBS_O_WORKDIR
singularity exec -B /scratch/:/scratch --pwd /scratch/snumber:/scratch --pwd /scratch  /export/home/snumber/Container/aertslab-pyscenic-0.12.1.sif "/scratch/scripts/run1.sh"
#singularity exec -B /scratch/snumber:/scratch --pwd /scratch/snumber:/scratch --pwd /scratch  /export/home/snumber/Container/aertslab-pyscenic-scanpy-0.12.1-1.9.1.sif "/scratch/scripts/run2.sh"
exit
sleep 2

>>>>>>>>>>>>>>>>


Next create a run script inside : /scratch/snumber/scripts

cat run1.sh
>>>>>>>>>>>>
cd /scratch/scripts
python /scratch/scripts/grnboost3.py

>>>>>>>>>>>>
make it executable:
chmod +x d_run1.sh

Now simply submit the pbs job:
cd  /scratch/snumber/scripts
qsub pbs.01

After the run has completed, check through the output and error files and fix any errors. ViolaVoilà!