Oscar Software Modules
Below are instructions for software modules available on the Oscar clutter.

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 May 6, 2025. Please scroll across the code snippet to see the full list. For the most up-to-date list of software modules, log into your Oscar account and run the command  module avail . 

 Note: D is the default module.  

 abaqus-container/2021-akaeexs filezilla/3.49.1-epfjuus mafft/7.505-iuicuiv qit/2023-04-04-grwuvgg

 abaqus/2017-q4ghhm5 flashpca/2.0-zr2wflq magma-usyd/V2.28-8-p3zylpg qmcpack-mpi/3.16.0s-qp2hymx

 abaqus/2021.1-i675dvw freebayes/1.3.6-v7rppcd magma/2.7.1-cpueyjy qscintilla/2.11.6-dq7zlcq

 abaqus/2024-h5273a3 (D) freeglut/3.2.2-76qqoqn maple/22-cp2uld4 qt/5.15.9-fb7mjex

 abaqus/2024-ir-a4m5ld5 freesurfer/7.3.2-zop5n6m mark/2018.07.08-43snzfu qualimap/2.2.1-mybpdoi

 abaqus/2024-mbessa-ceayfuo freesurfer/8.0.0-jotmypd (D) materialstudio/2024s-gbc3dg6 quantum-espresso-mpi/7.1-gits-v4bxgtv

 abaqus/2024.1-7pcdqhp fsl/6.0.7.7s-bul4mby mathematica/13.2.0-n4i6yua quantum-espresso-mpi/7.1s-ia43pjk

 admixture/1.3.0-onwaqrp fv/5.5.2-g2ibb5x matlab/R2019a-rjyk3ws quantum-espresso-mpi/7.3s-kydgjwo (D)

 afni/23.3.07s-zm43m3u fzf/0.45.0-pdwl7a4 matlab/R2023a-xd6f7ph (D) r/4.0.0-p7gxu4e

 afni/24.2.01s-kstpoqt (D) gatk/4.3.0.0-234wqft matlab/R2024b-ipaztju r/4.0.3-pvf2znb

 anaconda/2023.09-0-7nso27y gaussian/09_v1-u6klkps maven/3.8.4-w3zgh4v r/4.1.0-bfjsvw5

 angsd/0.935-cbhuwc7 gaussian/09-D01_v2-tw73726 mercurial/5.8-vly6btb r/4.2.2-z6qdiis

 ant/1.10.13-alpqj4j gaussian/09-D01-TEST_v3-vv6ar67 mesa/22.1.6-yi2tztm r/4.3.1-lmofgb4

 ants/2.4.3-75npyop gaussian/16-C01-bb2r2gh (D) meson/1.6.0-eipcwzq r/4.4.0-yycctsj

 aria2/1.36.0-lsb7zcs gaussview/v05-mkdyw6j metis/5.1.0-7qoahod r/4.4.2-re5rjx3 (D)

 arm-forge/22.1.3-zq7lvdq gcc/6.5.0-lwshmxc miniconda3/23.11.0s-odstpk5 raisd/2.9-svyic22

 armadillo/12.2.0-4clpczv gcc/10.1.0-mojgbnp miniforge/23.11.0-0s-hwmjdtj rclone/1.62.2-o4lkrv6

 atom/1.19.3-ty5sdsn gcc/13.1.0-nvrtbp3 (D) minimap2/2.14-33hmvx2 readline/6.3-rnqups2

 autoconf/2.69-p4rpdx2 gcm/2.4.1-lfqoarh molden/6.7-isryqwj readline/8.2-5xbuyjt (D)

 autoconf/2.71-opdgqnq (D) gdal/3.7.0-4p4onmf molden/7.3-kytvh3m (D) root/6.28.04-u7t5ax7

 avogadro2/1.99.0-5zl5qaw gdal/3.10.3-4vc4f76 (D) molpro-mpi/2023.2.0s-vyfv74n rsem/1.3.3-5obucw6

 awscli/1.27.84-v22kngs geeqie/2.4-6vdnc4v molpro-mpi/2024.3.1-mpipr-hwakoux (D) rstudio/2023.09.1-lsqy746

 bamtools/2.5.2-ki3mdef geos/3.11.2-a6hfu6a mpc/1.3.1-zbino7j ruby/3.1.0-gnoxsfm

 basilisk/2023.11.11s-x4isdvp ghostscript/10.0.0-3atesdh mpfr/4.2.0-n2tkxso rust/1.73.0-647r2tw

 bazel/6.1.1-vvtxktr gimp/2.10.32-tlknk2n mriconvert/2.1.0-oaq24fz rust/1.81.0-3qqoayc (D)

 bbmap/39.01-jnnkpwk git-lfs/3.3.0-laphnvj mricrogl/2022.07.20-n3b7whc sage-container/10.3-avpqipf

 bcftools/1.13-76jesdj git/2.44.0-6f7n7ni mricron/201909-3s2phrj sage/9.5-drpqjkh

 bcftools/1.16-ewu6fpe (D) glew/2.2.0-plawm2j msmc2/2.1.4-cuac55f sage/10.3-nntihfr (D)

 bcl2fastq2/2.20.0.422-z3wh636 glm/0.9.9.8-m3s6sze multiwfn/3.8_1208-qn65pif salmon/1.9.0-itdua6n

 beagle/5.4-e43mqsa glpk/5.0-zifs7bb mummer4/4.0.0rc1-4llgadq salmon/1.10.1-43ljn7g (D)

 bedops/2.4.40-bjb2v2n gmap-gsnap/2024-08-20-dur7jyc muscle/3.8.1551-pys2b76 samtools/1.12-4v4uiz6

 bedtools2/2.31.0-lsohc7s gmp/6.2.1-qlaig4m mysql/8.0.29-w7xdbde samtools/1.16.1-txuglks (D)

 bismark/0.23.0-eoksupu gnuplot/5.4.3-pdiiquy nanoflann/1.4.3-u2l24dv sas/9.4m8-f2f3xdp

 blast-legacy/2.2.26-tcdku3a go/1.17.1-f4mqosa nbo/7.0-phausw3 schmutzi-container/1.5.7-vsorpcq

 blast-plus/2.2.30-cyxldrt go/1.20.3-xknmcqd nccl/2.16.2-1-gjmprw5 schmutzi-container/1.5.7-3imwf7h (D)

 blat/37-ebfj5e6 go/1.23.3-d3wvs6z (D) ncdu/1.18.1-uofylp6 schrodinger/2023-4-h3kvbn3

 blender/4.4.0-446jdgt google-cloud-cli/456.0.0-3mtj4z6 nco/5.1.5-36hru5t schrodinger/2024-1-yqi27-m4qqm46 (D)

 boost/1.80.0-harukoy gperf/3.1-56q4xf5 ncview/2.1.8-nxepxtw scons/4.5.2-housgyw

 bowtie/1.3.1-2kd7din grace/5.1.25-duvo7rn neovim/0.9.4-67stov2 seqkit/0.10.1-qtiftw4

 bowtie2/2.4.2-xdquyzq graphviz/8.0.1-75znavc neovim/0.10.4-7b4mer5 (D) seqtk/1.3-kbdjwob

 bowtie2/2.5.3-qgscc2u (D) gsl/2.7.1-khmyfcy netcdf-c/4.9.2-cfggqwi shapeit4/4.2.2-3us45un

 brotli/1.0.9-h22dril guppy/6.0.1-wpaqayj netcdf-c/4.9.2-4ozokng (D) singular/4.4.0-aeloppr

 bwa/0.7.17-lu4b4dj guppy/6.1.2-wwvvdfu (D) netcdf-cxx4/4.3.1-6gcdg5s skewer/0.2.2-nwhklgr

 bxh-xcede-tools-container/1.11.14-4sphv7n gurobi/10.0.1-q7rc5dw netcdf-fortran/4.6.0-kl27oji slicer/5.4.0-rb2kk4l

 cadence/IC06.18-calibre2022.2-ascb7dw hdf5/1.12.2-s6aacp3 netcdf/4.9.2-ar77jpt slim/4.0.1-kymgtmu

 cadence/IC06.18.090-6famfci hdf5/1.14.1-2-rdd6y6v (D) netlib-lapack/3.11.0-jdzmstx slim/4.3-u22vcwu (D)

 cadence/IC23.10.000-ppqll2n (D) hisat2/2.2.1-gn4pb3l netlogo/6.4.0-psm765m spdlog/1.11.0-qbi24my

 casa/6.6.0-20-py3.8.el7-dqvn5lw homer/4.11.1-fpjs4l4 netpbm/10.73.43-m2jdopk splash/2.1.4-unrsfpj

 cdhit/4.8.1-bqmf4jf hpcx-mpi/4.1.5rc2-mts-ukpby4i ngc-jax/23.10-paxml-py3-ziphcif spm/8-77b5myx

 cellranger/arc-2.0.1-uamrhhu hpcx-mpi/4.1.5rc2s-yflad4v (D) ngc-pytorch/24.03-py3-a6ptiby spm/12_r7606-prcq7fg (D)

 cellranger/atac-2.0.0-m2tfcpk htop/3.2.2-kqsjlaj ngc-tensorflow/24.03-tf2-py3-lmnuwwg sratoolkit/3.0.0-u4jvgps

 cellranger/6.0.0-dbztt7r (D) htslib/1.12-ecidzx4 ninja/1.11.1-k2aq3rl stacks/2.65-geg4r7a

 cfitsio/4.2.0-5grfqtu htslib/1.17-zxcat2k (D) nlopt/2.7.1-fwj27pk star/2.7.10b-fj6kao2

 cgal/5.4.1-64mikhl idba/1.1.3-nrxiqtw nnn/4.9-r7kawsv stata/mp17-v7a7uoo

 chrome/119.0.6045.159s-avadhvk idemp/201706-a45gc3d node-js/18.12.1-qkps4za stata/mp18-3wq5b4o (D)

 cli11/2.3.2-pcucv7l idl/8.9s-jocgnbh nvhpc/23.3-xa4nyqi stow/2.4.0-y2q7tsn

 clustal-omega/1.2.4-mbj3dq5 igraph/0.7.1-wbiepb3 nvhpc/23.7-alnzdzw stringtie/2.2.1-7uti3ny

 cmake/3.6.1-il7bkvj imagej/154-linux64-java8-jd6sflr nvhpc/25.1-scvc2ac (D) sublime-text/4.4143-im3loi3

 cmake/3.26.3-xi6h36u (D) imagemagick/7.1.1-3-ex4k4u2 nvtop/3.0.1-7r22pjl subread/2.0.2-5agghnd

 cnvnator/0.4.1-w3bkqjf inkscape/1.3s-gshcpwc octopus-lunter/0.7.4-kkqrfv3 swig/4.1.1-bq46cxl

 code-server/4.20.0-tcrmrcm intel-oneapi-compilers/2023.1.0-a7fw7qt ollama/0.3.14s-a3gonhs synopsys/2023.12-df4a3ab

 code-server/4.97.2-33bvsj5 (D) intel-oneapi-mkl/2023.1.0-xbcd2g3 ollama/0.5.12s-mjarasi (D) synopsys/2024.09-q3jwzot (D)

 colordiff/1.0.21-ifskyqr intltool/0.51.0-vanhjsr openbabel/3.1.1-nay2mkb tabix/2013-12-16-d6qvxp7

 comsol/5.2-ufifhtv iq-tree/2.1.3-gu64b4j openblas/0.3.23-u6k5fey tcl/8.6.12-dziqp2l

 comsol/5.6_yqi27-jnspqto iq-tree/2.3.6-fn5vscb (D) opencv/4.6.0s-5z4piup tcsh/6.24.10-dtqo5ky

 comsol/6.3_yqi27-7yf67lt (D) iraf/2.17.1s-2r7ypc5 openexr/3.1.5-6fapou6 tecplot/2022r1-q5cg2zq

 conn/22a-nztrdv3 itk-snap-container/4.0.2-bychj73 openjdk/11.0.17_8-nw5ylvi tesseract/4.1.1-l2ejycz

 connectome-workbench/1.5.0-t66riqu jags/4.3.1-4mvaxc3 openjdk/17.0.5_8-pq2e7ao (D) tesseract/5.3.3-vq3altt (D)

 cppunit/1.14.0-h3hsjgu jellyfish/2.2.7-dywzm7z openjpeg/2.5.0-iyu5vwb texlive/20220321-pocclov

 crossrate-container/2016-27ofi4r jo/1.9-ki7xc2u openmpi/4.1.2-s5wtoqb texstudio/3.0.1-64vxo64

 cuda/10.1.243-bxisbai json-fortran/8.3.0-ehkzpjv openmpi/4.1.4s-smqniuf tk/8.6.11-uqpqlly

 cuda/10.2.89-xnfjmrt jsoncpp/1.9.5-vhsa2iy openmpi/4.1.5-kzuexje tmux/3.3a-zyhjvvh

 cuda/11.8.0-lpttyok julia/1.9.3s-i3zndt3 openmpi/5.0.1s-6ti4ij7 tmux/3.5a-kcjtgnd (D)

 cuda/12.1.1-ebglvvq julia/1.11.1s-kueea5s (D) openmpi/5.0.2s-mfj2kfp (D) tn93/1.0.12-tcvbyl4

 cuda/12.2.0-4lgnkrh kraken/1.1.1-e6r2aej openslide/3.4.1-pzjb2kl tree/2.1.0-7tlhzo7

 cuda/12.3.0-r72aozf lemon/1.3.1-jh3h4xt openssl/1.1.1t-u2rkdft trimal/1.4.1-ace7du2

 cuda/12.4.0-piq32fy (D) leptonica/1.81.0-pebiyok or-tools/9.10-k4nov4d trimgalore/0.6.6-iwfrq4c

 cudnn/7.5.1.10-10.1-hv4e2lt leveldb/1.23-f76iwfr ovito/3.6.0-rile7ax trimgalore/0.6.9-hisz5xp (D)

 cudnn/8.7.0.84-11.8-lg2dpd5 lftp/4.9.2-vimt4vf p7zip/17.05-3xtimiz trimmomatic/0.39-w5jnhai

 cudnn/8.9.6.50-12-56zgdoa libarchive/3.6.2-mnc5shn pandoc/2.19.2-wawlx5m udunits/2.2.28-rycabdx

 cudnn/9.8.0.87-12-j5i4iki (D) libbeef/Nov2020-xhrdwg5 pangolin/0.6-vwij3iv usearch/11.0.667-wx6utmj

 cufflinks/2.2.1-ogzw3z5 libdeflate/1.10-5yi7m3g parallel/20220522-5ah2i5h v8/3.14.5-aompxje

 cutensor/1.5.0.3-gqkzath libgd/2.2.4-2iyhgxa paraview/5.9.0s-dgv24kr vasp-mpi/5.4.4-mdh3hpy

 datamash/1.8-ib4aakp libgd/2.3.3-ubu4k2f (D) patchelf/0.17.2-aqmx4qb vasp-mpi/5.4.4-wannier-cqhzfma

 dcm2niix/1.0.20220720-nwsidfo libgeotiff/1.6.0-voueb6b paup/4.0a168-cwt24ux vasp-mpi/6.3.2_avandewa-chn3w3j

 diamond/2.0.15-h7xx24l libgit2/1.6.4-a432pgi pcre2/10.42-xks64jg vasp-mpi/6.4.2_cfgoldsm_vtst-cff5qmk

 dicombrowser/20181217s-ikvqhyr libiconv/1.17-jwjcds2 pdftk/2.02-gu7lpeg vasp-mpi/6.4.2_cfgoldsm-7krhcss

 dlib/19.22-lxah7rq libjpeg-turbo/2.1.5-sewtk5u pdsh-chaos/23.12-t6ywlrp vasp-mpi/6.4.3_cfgoldsm-5dioee2

 dmtcp/3.0.0-xvfukfp libjpeg/9e-6djp5nd perl-dbi/1.643-t74vmeb vasp-mpi/6.4.3_yqi27-wannier-livyes6

 dorado/0.8.2-s42dhri libnsl/1.3.0-calriiy perl/5.26.2-o4iq4b4 vasp-mpi/6.4.3_yqi27-6uzdgwn (D)

 dos2unix/7.4.2-5a6dlgt libnsl/2.0.1-ed2i5hn (D) perl/5.36.0-bt34quz (D) vcftools/0.1.14-syssqsi

 dotnet/8.0.100-5lr7bga libpng/1.2.57s-lve65hz perl/5.37.9-og4osvm vim/9.1.0867-wl3haj7

 dropest/0.8.6-ewwx5ik libpng/1.5.30-ru3zswz perl/5.40.0-o7hxci2 virtualgl/3.1-yphbrfj

 ds/9.8.5s-zpqg2jy libpng/1.6.39-ryxiwrd (D) picard/2.26.2-qabtyqy visit-container/3.3.3-qz3dni6

 dsi-studio/chen-2023-sif-lytwlk2 libreoffice/7.2.2.sif-drpjygp pigz/2.7-zgdlry3 visit-mpi/3.3.3s-lz2dp7m

 dtitk/2.3.1s-bp7yqjh libsdl/2.30.7-4rxs72s plink/1.9-beta6.27-nvy4vrx vmd/1.9.3-oin2dnj

 eigen/3.4.0-uycckhi libsodium/1.0.20-4o75gk3 plink/2.00-b6x44xw (D) vscode/1.84.2-4tfimgp

 eigensoft/7.2.1-6ctbhoz libtiff/4.5.0-g6fga7e popoolation2/1.205s-luyjn2b vscode/1.97.2-txhgk3l (D)

 emacs/28.2-rwds2pd libtree/3.1.1-yxst452 postgresql/16.4-vzmexkn wcstools/3.9.7-lo4forb

 expat/2.5.0-zujcztp libvips/8.13.3-ex4pfpg prodigal/2.6.3-vbq7usx wxwidgets/3.2.2.1-mk5eiyq

 fastme/2.1.5.1-kmg5til libwnck/3.24.1-4gvyjhg proj/9.2.0-ni5rcfb xcrysden/1.5.60-nuxe46i

 fastp/0.23.4-xmfbk37 libx11/1.8.4-qdzkebe protobuf/3.22.2-6hlkkut xerces-c/3.3.0-djupwmt

 fastq-screen/0.15.3-7ymgrux libxc/4.3.4-uy5ogwb py-ase/3.21.0-pyuljod xeyes/1.2.0-nge56yb

 fastqc/0.11.9-mvd2uhw libxc/5.2.3-ncc5ir4 (D) py-matplotlib/3.7.1-4afsjsz xgboost/1.6.2-fp3ii65

 fastqc/0.12.1-sk2rb3a (D) libyaml/0.2.5-wdpye7g py-statsmodels/0.13.2-sbdhj4k yaml-cpp/0.7.0-6nno2ru

 fasttree/2.1.11-o5kvig7 libzip/1.3.2-qwqikw6 py-sympy/1.11.1-gqgr7wu zlib/1.2.13-jv5y5e7

 fastx-toolkit/0.0.14-zhaxiyn liggghts/3.8.0s-rqph5mk pycharm-community/2021.3.3-weqrcly zoxide/0.9.2-ydhigq6

 ferret/7.6.0-i6u5m7q linaro-forge/23.1.2-pk2lobu pypy/7.3.13-6a5ma5j zstd/1.5.5-zokfqsc

 ffmpeg/6.0-fy677gn llvm/16.0.2-mq6g5lb python/3.9.16s-x3wdtvt

 ffmpeg/7.0-xny2fb2 (D) lmod/8.7.24-w2akdkb python/3.11.0s-ixrhc3q (D)

 fiji/20231107-1617-espdc7g macaulay2-container/1.2-vupc5gy qgis/3.28.3-5axmqsj

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 (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 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 (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 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. 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. Command  module avail mathematica to list all the available Mathematica versions (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 4. Command  module load mathematica to load the latest Mathematica version. 

 5. Command  mathematica  to launch the Mathematica app.

Oscar: R
Loading and Launching R 

 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. To load R,  use module load r/4.4.2-re5rjx3 , or another version of your preference (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 4. Once inside the container's shell, use R to launch the R console. 

 Installing R packages  

 1. R packages need to be installed locally. 

 2. First, load the R version that you want to use the package with: module load r/4.2.2 . 

 3. Start R Session R . 

 4. For packages that require code to be complied, install them in the log in node. Refer to more information  here . 

 5. To reinstall packages, start a new session by:  

 module load r/4.2.2

R 

 6. Then, update packages with  update.packages(checkBuilt=TRUE, ask=FALSE) . 

 7. To uninstall packages, start an R session. Then run remove.packages("wordcloud") (word cloud can be replaced with string of another package name).

Oscar: Macaulay 2
Loading and launching Macaulay 2 

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

 1. 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. 

 2. The Macaulay 2 module provides containers. To load them, use module load macaulay2-container/1.2-vupc5g (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 3. To start the container use apptainer shell $MACAULAY2_CONTAINER . 

 4. Once inside the container's shell, use M2 to launch the Macaulay console.

Oscar: Magma
Loading and launching Magma  

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

 2. Magma requires a GPU partition. Start an interactive node by commanding interact -q gpu (if running Oscar in PuTTY, pasting text is done by right-clicking). 

 3. To load Magma, use module load magma-usyd/V2.28-8-p3zylpg . 

 4. Use magma to launch the Magma console.