Oscar
Oscar is Brown's high-performance computing cluster managed by the Brown Center for Computation and Visualization (CCV). Long-term visitors to ICERM with a Brown sponsored ID are provided with an exploratory account on Oscar upon arrival. Oscar access may also be requested by short-term visitors on a request-basis with advance notice.
Overview of Oscar
Long-term visitors to ICERM who receive a Brown ID are provided with an exploratory account on the Oscar high performance computing cluster maintained by Brown's Center for Computation and Visualization. Oscar access may also be provided to short-term visitors on request-basis with advance notice.
If you are coming for a short-term visit and would like to request Oscar access, please contact ICERM IT staff no later than three weeks before your planned visit to ensure enough time to process a sponsored ID and enable Oscar access.
Oscar can be used via SSH terminal or via Oscar OnDemand through a web browser.
Using Oscar
Oscar provides both command line (SSH) access and VNC through Open OnDemand access to the computing cluster. All Oscar accounts are capable of using both interfaces, so you should choose the method that will work best for what you are trying to accomplish.
Oscar accounts are tied to Brown Shibboleth accounts, so you must have an ICERM Sponsored Brown ID to use Oscar. Once Oscar access is enabled for your Brown account, you can log in to CCV resources using your Brown Shibboleth username and password.
SSH Usage
Oscar's SSH interface provides a standard command line shell for interacting with the cluster. This is the best option for using simple command line scripts and submitting scripts for batch job processing. See the SSH Login Instructions for information on how to connect and basic usage. It's important to note that you should never run complex scripts or computations within the SSH login nodes. Computations should either be submitted as batch jobs or run in an interactive compute session using the interact
command.
Open OnDemand
Oscar's OOD is a web portal to the Oscar computing cluster. This is the easiest way to access a number of CCV resources including an Oscar Shell, interactive applications like MATLAB, and a fully featured Linux GUI. See the OOD Login Instructions for information on how to get started with the web portal.
Available Software
Oscar maintains a large library of software packages for use on the HPC cluster. Some of the most commonly used mathematical applications and languages available include:
-
Julia
-
Python
-
Maple
- Macaulay 2
To see the most up to date list of available software, log in to your Oscar account and run the terminal command module avail
. When using Oscar over the Linux GUI, you must open the Terminal Emulator to run these commands.
Loading Software Modules
Oscar has a large library of software available on the cluster, but only a few apps are pre-loaded in to your sessions. The commands below will allow you to list all available modules, search the list of modules, and load/unload software packages.
-
To view all available software packages, type
module avail
. -
To search the list of available packages, type
module avail <package>
.For example, to search for all available versions of Mathematica command:
module avail mathematica
.Many packages, like Mathematica, have multiple versions available. This command lets you see all available versions of the package you searched for.
-
To load a package into your session, type
module load <package/version>
. -
For example,
module load mathematica/11.0
. This will load the Mathematica 11.0 into your session and make it available for use. -
To unload a package you are no longer using, type
module unload <packagename>
-
More information about Oscar's software packages are available in the Oscar User Manual. 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.
Running Jobs
Oscar supports two main methods of running jobs: batch jobs and interactive sessions.
-
Batch jobs are pre-scripted and can be submitted to the cluster's scheduler via the
sbatch
command. -
Interactive jobs are command-line sessions run directly on a compute node via the
interact
command that can be used in real time.
The Running Jobs page of the Oscar User Manual provides the most detailed and up-to-date instructions on scripting and submitting jobs.
Important Notes About the Oscar Cluster
-
Please do not run any computations or simulations on the login nodes, as they are shared with other users. Use SLURM to submit a batch job to the queue for computations or start an interactive session on one of the compute nodes with the command
interact
. -
The full Oscar User Manual is available on the CCV website.
-
Oscar uses SLURM for managing batch jobs and interactive sessions on the cluster. Detailed instructions on submitting jobs is available on the Running Jobs page of the Oscar User Manual.
-
Users can install sub-packages for some modules (like Sage and Python) to their home folders on their own by using the
–user
as an option in the install command. For example, with Sage, the command would be something likesage -i <package name> –user
.
If you have questions about these instructions or require further assistance, please contact the ICERM IT staff by dropping by the administrative offices or emailing support@icerm.brown.edu.
SSH Access to Oscar
1. Open your preferred terminal application. Windows users need to install an SSH client. We recommend PuTTY, a free SSH client for Windows.
2. In the terminal, type ssh <your ccv username>@ssh.ccv.brown.edu
. If you are asked to verify the authenticity of the host 'ssh.ccv.brown.edu', type yes
.
3. You will now be prompted for your password. Enter your password (nothing will show up when you type in the terminal password prompt) and press enter.
4. Once logged in, you should see a “Welcome to Oscar!” message. This means you're now connected to one of the login nodes, which you can use to manage your files and submit batch jobs.
Additional Information:
1. To authenticate to Oscar with SSH keys, refer to this documentation.
2. Please DO NOT run any computations directly on the login nodes. Use the batch system to submit your computations to the queue to be processed on the computation nodes or start an interactive session on one of the compute nodes with the command interact
. See CCV's Oscar Documentation page on Running Jobs for detailed instructions on batch jobs and interactive sessions.
Open OnDemand Access to Oscar
Open OnDemand (OOD) is a web portal to the Oscar computing cluster. An Oscar account is required to access Open OnDemand. Visit this link in a web browser and sign in with your Brown username and password to access this portal.
Benefits of OOD:
- No installation needed. Just use your favorite browser!
- No need to enter your password again. SSH into Oscar in seconds!
- No need to use two-factor authentication multiple times. Just do it once, when you log into OOD.
- Workflow remains the same with or without VPN.
Once logged in you'll see a landing page that looks similar to the photo below:
Guides to help you figure your way out once logged in:
Using Python or Conda environments in the Jupyter App
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 inHH: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 thedata
directory which you can see from the submit node. In this example I have created a folder titledtest_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 be0-9
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 filestest7.err
andtest7.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 programtest_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 bysys.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 anysys.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 calledsage-batch.sh
, so we simply typesbatch 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.
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.