Oscar Software Modules

List and instructions for software modules available on the Oscar cluster.

Currently Available Modules
Oscar: Sage
Oscar: MATLAB
Oscar: Mathematica

Currently Available Modules

If you require a software package that is not currently available on the Oscar cluster, please contact ICERM's IT staff and we will work with CCV to get the software installed.

This list is current as of January 7, 2025. To see the most up to date list of software modules, log into your Oscar account and run the command module avail.

---- /oscar/runtime/software/spack/0.20.1/share/spack/lmod/linux-rhel9-x86_64/Core ----
   abaqus-container/2021-akaeexs
   abaqus/2017-q4ghhm5
   abaqus/2021.1-i675dvw
   abaqus/2024-h5273a3                       (D)
   abaqus/2024-ir-a4m5ld5
   abaqus/2024-mbessa-ceayfuo
   abaqus/2024.1-7pcdqhp
   admixture/1.3.0-onwaqrp
   afni/23.3.07s-zm43m3u
   afni/24.2.01s-kstpoqt                     (D)
   anaconda/2023.09-0-7nso27y
   angsd/0.935-cbhuwc7
   ant/1.10.13-alpqj4j
   ants/2.4.3-75npyop
   aria2/1.36.0-lsb7zcs
   arm-forge/22.1.3-zq7lvdq
   armadillo/12.2.0-4clpczv
   atom/1.19.3-ty5sdsn
   autoconf/2.69-p4rpdx2
   avogadro2/1.99.0-5zl5qaw
   awscli/1.27.84-v22kngs
   bamtools/2.5.2-ki3mdef
   basilisk/2023.11.11s-x4isdvp
   bazel/6.1.1-vvtxktr
   bbmap/39.01-jnnkpwk
   bcftools/1.13-76jesdj
   bcftools/1.16-ewu6fpe                     (D)
   bcl2fastq2/2.20.0.422-z3wh636
   beagle/5.4-e43mqsa
   bedops/2.4.40-bjb2v2n
   bedtools2/2.31.0-lsohc7s
   bismark/0.23.0-eoksupu
   blast-legacy/2.2.26-tcdku3a
   blast-plus/2.2.30-cyxldrt
   blat/37-ebfj5e6
   blender/4.0.0s-v667vhv
   boost/1.80.0-harukoy
   bowtie/1.3.1-2kd7din
   bowtie2/2.4.2-xdquyzq
   bowtie2/2.5.3-qgscc2u                     (D)
   brotli/1.0.9-h22dril
   bwa/0.7.17-lu4b4dj
   bxh-xcede-tools-container/1.11.14-4sphv7n
   cadence/IC06.18-calibre2022.2-ascb7dw
   cadence/IC06.18.090-6famfci
   cadence/IC23.10.000-ppqll2n               (D)
   casa/6.6.0-20-py3.8.el7-dqvn5lw
   cdhit/4.8.1-bqmf4jf
   cellranger/arc-2.0.1-uamrhhu
   cellranger/atac-2.0.0-m2tfcpk
   cellranger/6.0.0-dbztt7r                  (D)
   cfitsio/4.2.0-5grfqtu
   cgal/5.4.1-64mikhl
   chrome/119.0.6045.159s-avadhvk
   cli11/2.3.2-pcucv7l
   clustal-omega/1.2.4-mbj3dq5
   cmake/3.6.1-il7bkvj
   cmake/3.26.3-xi6h36u                      (D)
   cnvnator/0.4.1-w3bkqjf
   code-server/4.20.0-tcrmrcm
   colordiff/1.0.21-ifskyqr
   comsol/5.2-ufifhtv
   comsol/5.6_yqi27-jnspqto
   comsol/6.3_yqi27-7yf67lt                  (D)
   conn/22a-nztrdv3
   connectome-workbench/1.5.0-t66riqu
   cppunit/1.14.0-h3hsjgu
   crossrate-container/2016-27ofi4r
   cuda/10.1.243-bxisbai
   cuda/10.2.89-xnfjmrt
   cuda/11.8.0-lpttyok
   cuda/12.1.1-ebglvvq
   cuda/12.2.0-4lgnkrh
   cuda/12.3.0-r72aozf                       (D)
   cudnn/7.5.1.10-10.1-hv4e2lt
   cudnn/8.7.0.84-11.8-lg2dpd5
   cudnn/8.9.6.50-12-56zgdoa                 (D)
   cufflinks/2.2.1-ogzw3z5
   cutensor/1.5.0.3-gqkzath
   datamash/1.8-ib4aakp
   dcm2niix/1.0.20220720-nwsidfo
   diamond/2.0.15-h7xx24l
   dicombrowser/20181217s-ikvqhyr
   dlib/19.22-lxah7rq
   dmtcp/3.0.0-xvfukfp
   dorado/0.8.2-s42dhri
   dos2unix/7.4.2-5a6dlgt
   dotnet/8.0.100-5lr7bga
   dropest/0.8.6-ewwx5ik
   ds/9.8.5s-zpqg2jy
   dsi-studio/chen-2023-sif-lytwlk2
   dtitk/2.3.1s-bp7yqjh
   eigen/3.4.0-uycckhi
   eigensoft/7.2.1-6ctbhoz
   emacs/28.2-rwds2pd
   expat/2.5.0-zujcztp
   fastme/2.1.5.1-kmg5til
   fastp/0.23.4-xmfbk37
   fastq-screen/0.15.3-7ymgrux
   fastqc/0.11.9-mvd2uhw
   fastqc/0.12.1-sk2rb3a                     (D)
   fasttree/2.1.11-o5kvig7
   fastx-toolkit/0.0.14-zhaxiyn
   ferret/7.6.0-rzhafh6
   ffmpeg/6.0-fy677gn
   ffmpeg/7.0-xny2fb2                        (D)
   fiji/20231107-1617-espdc7g
   filezilla/3.49.1-epfjuus
   flashpca/2.0-zr2wflq
   freebayes/1.3.6-v7rppcd
   freeglut/3.2.2-76qqoqn
   freesurfer/7.3.2-zop5n6m
   fsl/6.0.7.7s-bul4mby
   fv/5.5.2-g2ibb5x
   fzf/0.45.0-pdwl7a4
   gatk/4.3.0.0-234wqft
   gaussian/09_v1-u6klkps
   gaussian/09-D01_v2-tw73726
   gaussian/09-D01-TEST_v3-vv6ar67
   gaussian/16-C01-bb2r2gh                   (D)
   gaussview/v05-mkdyw6j
   gcc/6.5.0-lwshmxc
   gcc/10.1.0-mojgbnp
   gcc/13.1.0-nvrtbp3                        (D)
   gcm/2.4.1-lfqoarh
   gdal/3.7.0-4p4onmf
   geeqie/2.4-6vdnc4v
   geos/3.11.2-a6hfu6a
   ghostscript/10.0.0-3atesdh
   gimp/2.10.32-tlknk2n
   git-lfs/3.3.0-laphnvj
   git/2.44.0-6f7n7ni
   glew/2.2.0-plawm2j
   glm/0.9.9.8-m3s6sze
   glpk/5.0-zifs7bb
   gmap-gsnap/2024-08-20-dur7jyc
   gmp/6.2.1-qlaig4m
   gnuplot/5.4.3-pdiiquy
   go/1.17.1-f4mqosa
   go/1.20.3-xknmcqd
   go/1.23.3-d3wvs6z                         (D)
   google-cloud-cli/456.0.0-3mtj4z6
   gperf/3.1-56q4xf5
   grace/5.1.25-duvo7rn
   graphviz/8.0.1-75znavc
   gsl/2.7.1-khmyfcy
   guppy/6.0.1-wpaqayj
   guppy/6.1.2-wwvvdfu                       (D)
   gurobi/10.0.1-q7rc5dw
   hdf5/1.12.2-s6aacp3
   hdf5/1.14.1-2-rdd6y6v                     (D)
   hisat2/2.2.1-gn4pb3l
   homer/4.11.1-fpjs4l4
   hpcx-mpi/4.1.5rc2-mts-ukpby4i
   hpcx-mpi/4.1.5rc2s-yflad4v                (D)
   htop/3.2.2-kqsjlaj
   htslib/1.12-ecidzx4
   htslib/1.17-zxcat2k                       (D)
   idba/1.1.3-nrxiqtw
   idemp/201706-a45gc3d
   idl/8.8.2-d5p4srq
   igraph/0.7.1-wbiepb3
   imagej/154-linux64-java8-jd6sflr
   imagemagick/7.1.1-3-ex4k4u2
   inkscape/1.3s-gshcpwc
   intel-oneapi-compilers/2023.1.0-a7fw7qt
   intel-oneapi-mkl/2023.1.0-xbcd2g3
   intltool/0.51.0-vanhjsr
   iq-tree/2.1.3-gu64b4j
   iq-tree/2.3.6-fn5vscb                     (D)
   iraf/2.17.1s-2r7ypc5
   itk-snap-container/4.0.2-bychj73
   jags/4.3.1-4mvaxc3
   jellyfish/2.2.7-dywzm7z
   jo/1.9-ki7xc2u
   json-fortran/8.3.0-ehkzpjv
   jsoncpp/1.9.5-vhsa2iy
   julia/1.9.3s-i3zndt3
   julia/1.11.1s-kueea5s                     (D)
   kraken/1.1.1-e6r2aej
   lemon/1.3.1-jh3h4xt
   leptonica/1.81.0-pebiyok
   leveldb/1.23-f76iwfr
   lftp/4.9.2-vimt4vf
   libarchive/3.6.2-mnc5shn
   libbeef/Nov2020-xhrdwg5
   libdeflate/1.10-5yi7m3g
   libgd/2.2.4-2iyhgxa
   libgd/2.3.3-ubu4k2f                       (D)
   libgeotiff/1.6.0-voueb6b
   libgit2/1.6.4-a432pgi
   libiconv/1.17-jwjcds2
   libjpeg-turbo/2.1.5-sewtk5u
   libjpeg/9e-6djp5nd
   libnsl/1.3.0-calriiy
   libnsl/2.0.1-ed2i5hn                      (D)
   libpng/1.2.57s-lve65hz
   libpng/1.5.30-ru3zswz
   libpng/1.6.39-ryxiwrd                     (D)
   libreoffice/7.2.2.sif-drpjygp
   libsdl/2.30.7-4rxs72s
   libtiff/4.5.0-g6fga7e
   libtree/3.1.1-yxst452
   libvips/8.13.3-ex4pfpg
   libwnck/3.24.1-4gvyjhg
   libxc/4.3.4-uy5ogwb
   libxc/5.2.3-ncc5ir4                       (D)
   libzip/1.3.2-qwqikw6
   liggghts/3.8.0s-rqph5mk
   linaro-forge/23.1.2-pk2lobu
   llvm/16.0.2-mq6g5lb
   lmod/8.7.24-w2akdkb
   mafft/7.505-iuicuiv
   magma-usyd/V2.28-8-p3zylpg
   magma/2.7.1-cpueyjy
   maple/22-cp2uld4
   mark/2018.07.08-43snzfu
   materialstudio/2024s-gbc3dg6
   mathematica/13.2.0-n4i6yua
   matlab/R2019a-rjyk3ws
   matlab/R2023a-xd6f7ph                     (D)
   matlab/R2024b-ipaztju
   maven/3.8.4-w3zgh4v
   mercurial/5.8-vly6btb
   mesa/22.1.6-6dbg5gq
   meson/1.6.0-eipcwzq
   metis/5.1.0-7qoahod
   miniconda3/23.11.0s-odstpk5
   miniforge/23.11.0-0s-hwmjdtj
   minimap2/2.14-33hmvx2
   molden/6.7-isryqwj
   molden/7.3-kytvh3m                        (D)
   molpro-mpi/2023.2.0s-vyfv74n
   molpro-mpi/2024.3.1-mpipr-hwakoux         (D)
   mpc/1.3.1-zbino7j
   mpfr/4.2.0-n2tkxso
   mriconvert/2.1.0-oaq24fz
   mricrogl/2022.07.20-n3b7whc
   mricron/201909-3s2phrj
   msmc2/2.1.4-cuac55f
   multiwfn/3.8_1208-qn65pif
   mummer4/4.0.0rc1-4llgadq
   muscle/3.8.1551-pys2b76
   mysql/8.0.29-w7xdbde
   nanoflann/1.4.3-u2l24dv
   nbo/7.0-phausw3
   nccl/2.16.2-1-gjmprw5
   ncdu/1.18.1-uofylp6
   nco/5.1.5-36hru5t
   ncview/2.1.8-nxepxtw
   neovim/0.9.4-vygnfr2
   netcdf-c/4.9.2-cfggqwi
   netcdf-c/4.9.2-4ozokng                    (D)
   netcdf-cxx4/4.3.1-6gcdg5s
   netcdf-fortran/4.6.0-kl27oji
   netcdf/4.9.2-ar77jpt
   netlib-lapack/3.11.0-jdzmstx
   netlogo/6.4.0-psm765m
   netpbm/10.73.43-m2jdopk
   ngc-jax/23.10-paxml-py3-ziphcif
   ngc-pytorch/24.03-py3-a6ptiby
   ngc-tensorflow/24.03-tf2-py3-lmnuwwg
   ninja/1.11.1-k2aq3rl
   nlopt/2.7.1-fwj27pk
   nnn/4.9-r7kawsv
   node-js/18.12.1-qkps4za
   nvhpc/23.3-xa4nyqi
   nvtop/3.0.1-7r22pjl
   octopus-lunter/0.7.4-kkqrfv3
   ollama/0.3.14s-a3gonhs
   openbabel/3.1.1-nay2mkb
   openblas/0.3.23-u6k5fey
   opencv/4.6.0s-5z4piup
   openexr/3.1.5-6fapou6
   openjdk/11.0.17_8-nw5ylvi
   openjdk/17.0.5_8-pq2e7ao                  (D)
   openjpeg/2.5.0-iyu5vwb
   openmpi/4.1.2-s5wtoqb
   openmpi/4.1.4s-smqniuf
   openmpi/4.1.5-hkgv3gi
   openmpi/4.1.5-kzuexje                     (D)
   openslide/3.4.1-pzjb2kl
   openssl/1.1.1t-u2rkdft
   or-tools/9.10-k4nov4d
   ovito/3.6.0-rile7ax
   p7zip/17.05-3xtimiz
   pandoc/2.19.2-wawlx5m
   pangolin/0.6-vwij3iv
   parallel/20220522-5ah2i5h
   paraview/5.9.0s-dgv24kr
   patchelf/0.17.2-aqmx4qb
   paup/4.0a168-cwt24ux
   pcre2/10.42-xks64jg
   pdftk/2.02-gu7lpeg
   pdsh-chaos/23.12-t6ywlrp
   perl-dbi/1.643-t74vmeb
   perl/5.26.2-o4iq4b4
   perl/5.36.0-bt34quz                       (D)
   perl/5.37.9-og4osvm
   perl/5.40.0-o7hxci2
   picard/2.26.2-qabtyqy
   pigz/2.7-zgdlry3
   plink/1.9-beta6.27-nvy4vrx
   plink/2.00-b6x44xw                        (D)
   popoolation2/1.205s-luyjn2b
   postgresql/16.4-vzmexkn
   prodigal/2.6.3-vbq7usx
   proj/9.2.0-ni5rcfb
   protobuf/3.22.2-6hlkkut
   py-ase/3.21.0-pyuljod
   py-matplotlib/3.7.1-4afsjsz
   py-statsmodels/0.13.2-sbdhj4k
   py-sympy/1.11.1-gqgr7wu
   pycharm-community/2021.3.3-weqrcly
   pypy/7.3.13-6a5ma5j
   python/3.9.16s-x3wdtvt
   python/3.11.0s-ixrhc3q                    (D)
   qgis/3.28.3-5axmqsj
   qit/2023-04-04-grwuvgg
   qmcpack-mpi/3.16.0s-qp2hymx
   qscintilla/2.11.6-dq7zlcq
   qt/5.15.9-fb7mjex
   qualimap/2.2.1-mybpdoi
   quantum-espresso-mpi/7.1-gits-v4bxgtv
   quantum-espresso-mpi/7.1s-ia43pjk
   quantum-espresso-mpi/7.3s-kydgjwo         (D)
   r/4.0.0-p7gxu4e
   r/4.0.3-pvf2znb
   r/4.1.0-bfjsvw5
   r/4.2.2-z6qdiis
   r/4.3.1-lmofgb4
   r/4.4.0-yycctsj                           (D)
   raisd/2.9-svyic22
   rclone/1.62.2-o4lkrv6
   readline/6.3-rnqups2
   root/6.28.04-u7t5ax7
   rsem/1.3.3-5obucw6
   rstudio/2023.09.1-lsqy746
   ruby/3.1.0-gnoxsfm
   rust/1.73.0-647r2tw
   rust/1.81.0-3qqoayc                       (D)
   sage-container/10.3-avpqipf
   sage/9.5-drpqjkh
   sage/10.3-nntihfr                         (D)
   salmon/1.9.0-itdua6n
   salmon/1.10.1-43ljn7g                     (D)
   samtools/1.12-4v4uiz6
   samtools/1.16.1-txuglks                   (D)
   sas/9.4m8-idx4uxl
   schmutzi-container/1.5.7-vsorpcq
   schmutzi-container/1.5.7-3imwf7h          (D)
   schrodinger/2023-4-h3kvbn3
   schrodinger/2024-1-yqi27-m4qqm46          (D)
   scons/4.5.2-housgyw
   seqkit/0.10.1-qtiftw4
   seqtk/1.3-kbdjwob
   shapeit4/4.2.2-3us45un
   singular/4.4.0-aeloppr
   skewer/0.2.2-nwhklgr
   slicer/5.4.0-rb2kk4l
   slim/4.0.1-kymgtmu
   slim/4.3-u22vcwu                          (D)
   spdlog/1.11.0-qbi24my
   splash/2.1.4-unrsfpj
   spm/8-77b5myx
   spm/12_r7606-prcq7fg                      (D)
   sratoolkit/3.0.0-u4jvgps
   stacks/2.65-geg4r7a
   star/2.7.10b-fj6kao2
   stata/mp17-v7a7uoo
   stata/mp18-3wq5b4o                        (D)
   stow/2.4.0-y2q7tsn
   stringtie/2.2.1-7uti3ny
   sublime-text/4.4143-im3loi3
   subread/2.0.2-5agghnd
   swig/4.1.1-bq46cxl
   synopsys/2023.12-df4a3ab
   synopsys/2024.09-q3jwzot                  (D)
   tabix/2013-12-16-d6qvxp7
   tcsh/6.24.10-dtqo5ky
   tecplot/2022r1-q5cg2zq
   tesseract/4.1.1-l2ejycz
   tesseract/5.3.3-vq3altt                   (D)
   texlive/20220321-pocclov
   texstudio/3.0.1-64vxo64
   tmux/3.3a-zyhjvvh
   tn93/1.0.12-tcvbyl4
   tree/2.1.0-7tlhzo7
   trimal/1.4.1-ace7du2
   trimgalore/0.6.6-iwfrq4c
   trimgalore/0.6.9-hisz5xp                  (D)
   trimmomatic/0.39-w5jnhai
   udunits/2.2.28-rycabdx
   usearch/11.0.667-wx6utmj
   v8/3.14.5-aompxje
   vasp-mpi/5.4.4-mdh3hpy
   vasp-mpi/6.3.2_avandewa-chn3w3j
   vasp-mpi/6.4.2_cfgoldsm_vtst-cff5qmk
   vasp-mpi/6.4.2_cfgoldsm-7krhcss
   vasp-mpi/6.4.3_yqi27-6uzdgwn              (D)
   vcftools/0.1.14-syssqsi
   vim/9.1.0867-wl3haj7
   virtualgl/3.1-yphbrfj
   visit-container/3.3.3-qz3dni6
   visit-mpi/3.3.3s-lz2dp7m
   vmd/1.9.3-oin2dnj
   vscode/1.84.2-4tfimgp
   wcstools/3.9.7-lo4forb
   wxwidgets/3.2.2.1-mk5eiyq
   xcrysden/1.5.60-nuxe46i
   xeyes/1.2.0-nge56yb
   xgboost/1.6.2-fp3ii65
   yaml-cpp/0.7.0-6nno2ru
   zlib/1.2.13-jv5y5e7
   zoxide/0.9.2-ydhigq6
   zstd/1.5.5-zokfqsc

Oscar: Sage

Loading and Launching Sage

1. Once authenticated to Oscar, use the following commands at the command line.

2. Start an interactive job by using the interact command. This command can take additional parameters to extend the resources and time allotted to the node as well as the partition that the node operates on.

3. The Sage module provides containers. To load them use module load sage-container/10.3.

4. To start the container use apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif

5. Once inside the container's shell use sage to launch the Sage console.

Sage on Oscar OnDemand

The easiest way to run Sage on Oscar OcDemand is to run sage in an interactive job via the terminal in your OnDemand session.

Use the interact command with parameters for your specific job to start the interactive session, then load your modules and run the sage binary (steps 2-4 above).

interact -n 2 -m 32g -t 04:00:00 -f 'haswell|broadwell|skylake'

Using Sage with Batch Scripts

Thanks to Trevor Hyde from Summer@ICERM 2019 for these instructions.

One method for running computations with Sage on Oscar is to write a script and use the slurm batch scheduler to have Oscar run your script. This requires two pieces:

A shell script to configure and submit your batch job to the cluster.
Your Sage code/program you'd like to run.

Example Batch Script

sage-batch.sh

#!/bin/bash
 
#SBATCH -J test_program
#SBATCH --array=0-9
#SBATCH -t 1:00:00
#SBATCH --mem=8G
 
#SBATCH -e data/<oscar-username>/test_output/test%a.err
#SBATCH -o data/<oscar-username>/test_output/test%a.out
 
module load sage-container/10.3

apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif
 
sage test_program.sage $SLURM_ARRAY_TASK_ID

#!/bin/bash tells the system this is a bash (shell) script.
#SBATCH -J test_program sets the name of the job which appears when you check the status of your jobs.
#SBATCH –array=0-9 is an easy way of doing parallel computations. In this case it says our job will run on 10 different nodes, each node will be passed a parameter and we have specified that the parameters will take the values 0 through 9. You can specify several ranges or even list individual parameters if you prefer.
#SBATCH -t 1:00:00 specifies a time limit in HH:MM:SS for each node. Once this time runs out your program will stop running on that node. Be careful setting the time limit too high as doing so may make it take a long time for your job to get scheduled to run. Before starting a big computation try to do some smaller tests to see how long you expect to need.
#SBATCH –mem=8G specifies how much memory each node gets. Standard exploratory accounts get 123GB total to use at any one time. So if you allocate too much per job, fewer jobs will run at once. On the other hand, if you allocate too little and a computation needs more than it has, then it will terminate. If this happens an “out of memory” error will show up in the .err file for that node.
#SBATCH -e data/<ccv-username>/test_output/test%a.err and #SBATCH -o data/<ccv-username>/test_output/test%a.out specify where the error messages and output for each computation should be sent. You should store these files in your user folder, not on the submit node. We each have a folder inside the data directory which you can see from the submit node. In this example I have created a folder titled test_output where I’m putting both of these files. You need to make these folders before you run the computation otherwise the output will be dumped into the void! The %a will get replaced with the array parameter. So for example, since we set our array parameters to be 0-9 there will be 10 nodes running and each of them gets a number between 0 and 9; this node corresponding to the parameter 7 will create two files test7.err and test7.out.
module load sage-container/10.3 loads the sage container into the node.
apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif initiates the container's Sage console shell.

Everything after this in the script happens as if you typed it yourself onto the command line.

In our example, we want to run sage code, so the line sage test_program.sage $SLURM_ARRAY_TASK_ID runs our example sage program test_program.sage.
The file needs to have the .sage extension.
You should write this file in a text editor, not in a Jupyter notebook (although you can first write and test your program in a Jupyter notebook and then copy and paste it into a new file when it’s ready).
This program is written to accept one input and I have passed it $SLURM_ARRAY_TASK_ID which is the array parameter passed to each node. You can use this parameter to select which input parameters to run your program on.

Example Sage Program

test_program.sage

import sys
 
def fun_math(message):
    print message
    sys.stdout.flush()
 
job_id = int(sys.argv[1])
fun_math('hi this is a test')
fun_math('my job id is' + str(job_id))

In the Sage program, you first define all of your functions and then you include the code you want to run.
Import sys so you can access the array parameter passed to your function from the node. This is accessed in this case by sys.argv[1]. Make sure you explicitly coerce to be an integer if you want to use it as an integer; it's a string by default.
The output of the print command is appended to the .out file for this node as a new line.
Notice the line sys.stdout.flush() included in the function. This makes the program immediately send whatever output it has to the output file when called. Otherwise the program won’t output anything until it has completely finished running. If each node is running 100 potentially long computations and it finishes the first 99 but then times out on the 100th computation, and you don’t include any sys.stdout.flush() commands, everything will be lost when time runs out.

Submitting the Batch Job

To run this batch program go back to the submit node and type sbatch <NAME_OF_BATCH_FILE>. In our example here, our batch file is called sage-batch.sh, so we simply type sbatch sage-batch.sh Slurm will return a line that tells you your job has been submitted together with a job id number.
To check the progress of your jobs type myq from anywhere on Oscar. This will show you what jobs you have running, how much time they have left, and which jobs are still waiting to run. Be patient, sometimes it takes a minute for things to get started.
If you realize your code is never going to finish or that you’ve made some terrible mistake, you can cancel a batch job by typing scancel <JOB_ID>. You can specify a single node or just put the general job id for the whole run and cancel everything.

Oscar: MATLAB

Loading and Launching MATLAB

1. Open the Terminal and use the following commands at the command line.

2. module avail matlab to list all the available matlab versions.

3. module load matlab to load the latest version of matlab (R2023a). Other versions can be specified with the command module load matlab/R2019a.

4. matlab to launch the MATLAB app.

Installing MATLAB Packages such as YALMIP

MATLAB script packages, such as YALMIP, can be installed directly by the user on their Oscar account.

1. Open the Terminal and connect to Oscar.

2. Navigate to your home folder by typing cd ~

3. mkdir -p MATLAB

4. wget -O yalmip.zip https://github.com/yalmip/yalmip/archive/master.zip

5. unzip yalmip.zip

6. In MATLAB, add the YALMIP-master directory to your path.

In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. cd ~/MATLAB
Right click on the YALMIP-master folder.
Select Add to Path > Selected Folders and Subfolders. This adds the YALMIP folders to your path.

7. To save your MATLAB path, use the savepath command in the MATLAB command prompt. savepath ~/MATLAB/pathdef.m

YALMIP also requires a solver like SDPT3. The steps below add SDPT3 to MATLAB.

1. Open the Terminal.

2. cd ~/MATLAB

3. wget -O sdpt3.zip https://github.com/sqlp/sdpt3/archive/master.zip

4. unzip sdpt3.zip

5. In MATLAB, add the sdpt3 directory to your path.

In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. cd ~/MATLAB
Right click on the sdpt3-master folder.
Select Add to Path > Selected Folders and Subfolders. This adds the SDPT3 folders to your path.

6. To update/save your MATLAB path, use the savepath command in the MATLAB command prompt. savepath ~/MATLAB/pathdef.m

Oscar: Mathematica

Loading and Launching Mathematica

1. Open the Terminal and use the following commands at the command line.

2. module avail mathematica to list all the available mathematica versions.

3. module load mathematica to load the latest mathematica version.

4. mathematica to launch the Mathematica app.