MEG analysis on Biowulf: Difference between revisions
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== MNE python data analysis == |
== MNE python data analysis == |
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!Under Construction! |
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===To Access Additional MEG modules=== |
===To Access Additional MEG modules=== |
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#Add the following line to your ${HOME}/.bashrc |
#Add the following line to your ${HOME}/.bashrc |
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===Load MNE modules=== |
===Load MNE modules=== |
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module load MNE/0.24.1 |
module load MNE/0.24.1 |
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ipython |
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import mne |
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==Best Practices for Group Data Preprocessing== |
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===Build a swarm file=== |
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for i in ${GROUP_FOLDER}/*.ds; do echo my_process.py -in1 input1 -in2 -input2 -dataset $i >> swarm_file_preprocess.sh ; done |
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#Make sure the process runs on at least one subject/dataset |
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tail -1 swarm_file_preprocess.sh | bash |
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#Verify the results on the single subject / possibly look at how much RAM / CPU was used before submitting the full batch to swarm |
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swarm -f ./swarm_file_preprocess.sh -g ${GigsOfRAM} -t ${CPUcores} # -b ${How many subjects to run in row on 1 computer} - can be useful if you have a fast process |
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== Making your own python module == |
== Making your own python module == |
Revision as of 12:40, 17 March 2022
!!Under Construction!!
Biowulf brief intro
Biowulf (biowulf.nih.gov) is the head node of the Biowulf cluster at NIH - https://hpc.nih.gov/docs/userguide.html
Helix - is the storage server attached to the biowulf cluster
Analysis of data should not be performed on the biowulf head node, but run through an sinteractive node or swarm process.
To start with, there are a limited number of commands loaded on the system. To access more programs use module load. To search, use module spider.
e.g. module load afni
SAM MEG Data Analysis
module load afni module load ctf module load samsrcv3/20180713-c5e1042
MNE python data analysis
To Access Additional MEG modules
#Add the following line to your ${HOME}/.bashrc module use --append /data/MEGmodules/modulefiles
Load MNE modules
module load MNE/0.24.1 ipython import mne
Best Practices for Group Data Preprocessing
Build a swarm file
for i in ${GROUP_FOLDER}/*.ds; do echo my_process.py -in1 input1 -in2 -input2 -dataset $i >> swarm_file_preprocess.sh ; done #Make sure the process runs on at least one subject/dataset tail -1 swarm_file_preprocess.sh | bash #Verify the results on the single subject / possibly look at how much RAM / CPU was used before submitting the full batch to swarm swarm -f ./swarm_file_preprocess.sh -g ${GigsOfRAM} -t ${CPUcores} # -b ${How many subjects to run in row on 1 computer} - can be useful if you have a fast process
Making your own python module
Build the python conda environment
It is recommended to create an install script so that this can be sent to a slurm job
# Load conda - if set up according to the HPC page, this should work source /data/${USER}/conda/etc/profile.d/conda.sh; conda activate base # echo mamba create -p ${PATH_TO_OUTPUT} condaPackage1 condaPackage2 conda-forge::condaForgePackage1 -y > installFile.sh # Make sure to include the -y or the job will hang waiting for user response # Also make sure you have an active conda prompt when submitting the swarm, or else it will fail echo mamba create -p /data/ML_MEG/python_modules/mne0.24.1 jupyter ipython conda-forge::mne -y > python_install.sh swarm -f ./python_install.sh -g 4 -t 4
Make a module file
To display most of the contents of a module file run
module display python #For the python module
Output:
---------------------------------------------------------------------------------- /usr/local/lmod/modulefiles/python/3.8.lua: ---------------------------------------------------------------------------------- family("python") prepend_path("PATH","/usr/local/Anaconda/envs/py3.8/bin") pushenv("OMP_NUM_THREADS","1")
Copy Template to your module folder
#MyModule is the family name of the code / ${Version}.lua cp /usr/local/lmod/modulefiles/python/3.8.lua ${myModuleFilesDir}/${MyModule}/0.1.lua
Add module files to the search path
module use --append ${PathToUserModuleFiles}
Final Step Load Your Module
module load ${MyModuleName}
#Example [$USERd@$NODE python_modules]$ module load mne [$USERd@$NODE python_modules]$ ipython Python 3.9.10 | packaged by conda-forge | (main, Feb 1 2022, 21:24:11) Type 'copyright', 'credits' or 'license' for more information IPython 8.1.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: import mne In [2]: mne.__path__ Out[2]: ['/data/ML_MEG/python_modules/mne0.24.1/lib/python3.9/site-packages/mne']