# Software # DCSR Software Stack ### What is it? The DCSR provides a software environment including commonly used scientific tools and libraries. The software is optimised to make best use of the CPUs, GPUs and high speed Infiniband interconnect. In order to create the environment we use the [Spack](https://github.com/spack/spack) package manager and [Lmod](https://lmod.readthedocs.io/en/latest/). For information on the deprecated Vital-IT software stack please [see here](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/old-software-stack). ### Release and lifecycle Each year we provide a new release of the software stack which fixes versions for key tools and libraries. The following table list all the software stacks avaiable: | Name | Date | Comments | | ----------- | ----------- |----| | Arolle | 2022 | SSL library incompatible with OS (after 2025 update)| | 20240303 | 2024 | | | 20240704 | 2024 | New stack based on Open MPI | | 20241118 | 2025 | R is provided by r-light module which uses a container, remove of miniconda3 (license problems)| Newer versions of tools may be made available during the year but the default versions will remain the default. ### How to use it The latest software stack is loaded by default. You just have to list the module using the `module` command: ```bash module available ``` To load a given software: ```bash module load python ``` If you want to change of software stack you have to use the command: `dcsrsoft` ```bash dcsrsoft use arolle ``` Do not forge to do a `module purge` before changing software stack. ### How to use it on jobs You need to start your jobs with: ```bash #!/bin/bash -l #SBATCH ... dcsrsoft use 20241118 ``` You need to put the name of the stack you are using. If you want to know the name of the stack that it is currently used, you can type: ```bash dcsrsoft show ``` Please keep in mind that old software stack would eventually removed. Therefore, you should migrate your script to the current software stack, if any problem arises please send us a ticket via: ( with DCSR on the subject of the mail) ### Common problems #### SSL problem in old software stacks If you observe one of the following errors: ```bash ImportError: cannot import name 'HTTPSConnection' from 'http.client' ``` or ```bash ImportError: cannot import name 'ssl' from 'urllib3.util.ssl_ ``` You should do define the following environment variable: ```bash export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/dcsrsoft/arolle_libs ``` Before executing your script # Old software stack The old (Vital-IT) software stack can be accessed on Curnagl via the following commands ``` $ source /dcsrsoft/bin/use_old_software ################################## # # # WARNING - USING OLD SOFTWARE # # # ################################## $ module load Bioinformatics/Software/vital-it ``` Please note that the old stack is not updated, no new tools can be added and there is no guarantee that it will work. # R on the clusters R is provided via the [DCSR software stack](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/dcsr-software-stack) A tutorial video on using R on the cluster is available [here](https://formations.unil.ch/course/view.php?id=511). ### Interactive mode To load R: ```shell $> module load r-light $> R # Then you can use R interactively > ... ``` By default, you get the last version available (4.4.1 when this page is written). If you need an older version, you can list the available versions as follows: ```bash $> module spider r-light ---------------------------------------------------------------------------- r-light: ---------------------------------------------------------------------------- Versions: r-light/3.6.3 r-light/4.0.5 r-light/4.1.3 r-light/4.2.3 r-light/4.3.3 r-light/4.4.1 ``` Then you can load a specific version: ```bash $> module load r-light/4.0.5 $> R --version R version 4.0.5 (2021-03-31) -- "Shake and Throw" ``` ### Batch mode While using R in batch mode, you have to use `Rscript` to launch your script. Here is an example of sbatch script, `run_r.sh`: ```shell #!/bin/bash #SBATCH --time 00-00:20:00 #SBATCH --cpus-per-task 1 #SBATCH --mem 4G module load r-light Rscript my_r_script.R ``` Then, just submit the job to Slurm: ```shell sbatch run_r.sh ``` ### Package installation A few core packages are installed centrally - you can see what is available by using the `library()` function. Given the number of packages and multiple versions available, other packages should be installed by the user. #### Library relocation By default, when you install R packages, R will try to install them in the central installation. Since this central installation is shared among all users on the cluster, it's obviously impossible to install directly your packages there. This is why this location is not writable and you will get this kind of message: ```bash $> R > install.packages("ggplot2") Warning in install.packages("ggplo2t") : 'lib = "/opt/R-4.4.1/lib/R/library"' is not writable Would you like to use a personal library instead? (yes/No/cancel) ``` This is why you have to answer **yes** to this "Would you like to use a personal library instead?" question. By default, this personal library is located in your home directory. On DCSR clusters, this home directory is pretty limited regarding the amount of data (50 GB at most) and the number of files (200'000 files at most) you can store. Installing R packages in your home directory could quickly fill all the available space. This is why your personal library should be relocated. A good practice is to relocate your R library to a location in one of your work project. Let's consider your work project is located in `/work/FAC/Lettres/GREAT/ulambda/default`, you create a sub-directory inside, for instance `/work/FAC/Lettres/GREAT/ulambda/default/RLIB_for_ursula`. Then you have several options to tell R that you want to use this new personal library, but the easiest way is to define the `R_LIBS_USER` variable. Thus, you can either add the following line in all your Slurm scripts (before R is invoked): ```bash export R_LIBS_USER=/work/FAC/Lettres/GREAT/ulambda/default/RLIB_for_ursula Rscript … ``` Or you can also define it in the `~/.Renviron`. You just have to add the following line to the file: `R_LIBS_USER=/work/FAC/Lettres/GREAT/ulambda/default/RLIB_for_ursula` The second option using `~/.Renviron` is probably cleaner but the first option is more versatile, especially if you want to use several personal libraries depending on different projects and requirements. #### CRAN packages Installing R packages from CRAN is pretty straightforward thanks to [install.packages()](https://stat.ethz.ch/R-manual/R-devel/library/utils/html/install.packages.html) function. For instance: ```bash $> module load r-light $> R > install.packages(c("ggplot2", "dplyr")) ``` #### BioConductor packages The first step is to install the BioConductor package manager, and then to install packages with `BiocManager::install()`. For instance: ```bash $> module load r-light $> R > install.packages("BiocManager") > BiocManager::install("biomaRt") ``` #### Github/development packages To install packages from Github/Gitlab or random websites, you can use the `devtools` library as follows: ``` $> module load r-light $> R > library(devtools) > install_github("N-SDM/covsel") > install_url("https://cran.r-project.org/src/contrib/Archive/rgdal/rgdal_1.6-7.tar.gz") ``` #### Missing dependencies In some cases, it's possible that package installation fails because of missing dependencies. In such case, please send us an email to with the subject starting with "DCSR R package installation". And please provide us with the name of the package that you cannot install. # Rstudio on the Curnagl cluster Rstudio can be run on the curnagl cluster from within a singularity container, with an interactive interface provided on the web browser of any given workstation. Running interactively with Rstudio on the clusters is only meant for testing. Development must be carried out on the users workstations, and production runs must be accomplished [from within R scripts/codes in batch mode](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/r-on-the-clusters-old/).

The command Rstudio is now available in r-light module. You have to do a reservation first with Sinteractive, ask the right amount of resources and then launch the command 'Rstudio'.

### Procedure ```bash Sinteractive # specify here the right amount of resources module load r-light Rstudio ```

The procedure below is now deprecated !!

### Preparatory steps 1. If the workstation is outside of the campus, first [connect to the VPN](https://www.unil.ch/ci/reseau-unil-chez-soi#guides-dinstallation) 2. [Login to the cluster](https://wiki.unil.ch/ci/books/service-de-calcul-haute-performance-(hpc)/page/curnagl#bkmrk-how-to-connect) 3. Create/choose a folder under the **/scratch** or the **/work** filesystems under your project (ex. */work/FAC/.../rstudio*); this folder will appear as your HOME inside the Rstudio environment, and we will refer to it as ${WORK} 4. *(This step is **optional** and only applies if you need a R version not available in the r-light module)* Create the singularity image inside the cluster (substitute **${WORK}** appropriately): ``` [me@curnagl ~]$ module load singularityce [me@curnagl ~]$ singularity pull --dir="${WORK}" --name=rstudio-server.sif docker://rocker/rstudio ``` This last step might take a while... ### The batch script Create a file **rstudio-server.sbatch** with the following contents (it must be on the cluster, but the exact location does not matter): ```bash #!/bin/bash -l #SBATCH --account ACCOUNT_NAME #SBATCH --mail-type BEGIN #SBATCH --mail-user @unil.ch #SBATCH --chdir ${WORK} #SBATCH --job-name rstudio-server #SBATCH --signal=USR2 #SBATCH --output=rstudio-server.job.%j #SBATCH --partition interactive #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --cpus-per-task 1 #SBATCH --mem 8G #SBATCH --time 01:59:59 #SBATCH --export NONE set -e RVERSION=4.4.1 #See module spider r-light to get all available versions LOCAL_PORT=8787 RSTUDIO_CWD=$(pwd) RSTUDIO_SIF="/dcsrsoft/singularity/containers/r-light.sif" module load python singularityce # Create temp directory for ephemeral content to bind-mount in the container RSTUDIO_TMP=$(mktemp --tmpdir -d rstudio.XXX) mkdir -p -m 700 \ ${RSTUDIO_TMP}/run \ ${RSTUDIO_TMP}/tmp \ ${RSTUDIO_TMP}/var/lib/rstudio-server mkdir -p ${RSTUDIO_CWD}/.R cat > ${RSTUDIO_TMP}/database.conf < ${RSTUDIO_TMP}/rsession.sh <&2 <&2 exit $SINGULARITY_EXIT_CODE ``` You need to carefully replace, at the beginning of the file, the following elements: - On line 3: ***ACCOUNT\_NAME*** with the project id that was attributed to your PI for the given project - On line 5: ***<first.lastname>@unil.ch*** with your e-mail address - On line 7: ***${WORK}*** must be replaced with the **absolute path** (ex. */work/FAC/.../rstudio*) to the chosen folder you created on the preparatory steps - On line 21: you can modify the R version. All available versions can be obtained from the following command `module spider r-light` - On line 24: if (and only if) you went through the optional fourth preparatory step, then you need to redefine **RSTUDIO\_SIF** so that the line reads **RSTUDIO\_SIF=${RSTUDIO\_CWD}/rstudio-server.sif** ### Running Rstudio Submit a job for running Rstudio from within the cluster with: ``` [me@curnagl ~]$ sbatch rstudio-server.sbatch ``` You will receive a notification by e-mail as soon as the job is running. A new file ${WORK}/rstudio-server.job.### (with ### some given job id number) is then automatically created. Its contents will give you instructions on how to proceed in order to start a new Rstudio remote session from your workstation. You will have 2h time to test your code. # MATLAB on the clusters The full version of MATLAB is only installed on the login and interactive nodes so in order to run MATLAB jobs on the cluster you first need to compile your .m files then run them using the MATLAB runtime. This is because the UNIL has a limited number of licences and with an HPC cluster it's easy to use them all. The number of licences and available toolboxes is detailed [here](https://wiki.unil.ch/ci/books/distribution-de-logiciels/page/matlab#bkmrk-quelles-toolboxes-so) Thankfully the compilation process isn't too complicated but there are a number of steps to follow and a few issues to be aware of. Let's start with our MatrixCAB.m file ``` disp("Matrix A:"); A = [1, 2; 3, 4]; disp(A); disp("Matrix B:"); B = [5, 6; 7, 8]; disp(B); disp("Matrix C = A * B:"); C = A * B; disp(C); ``` First of all we need to load the module that provides MATLAB ``` [ulambda@login ~]$ module load matlab [ulambda@login ~]$ module list Currently Loaded Modules: 1) matlab/2021b ``` We now compile the MatrixCAB.m file with the `mcc` compiler which is now in the path. ``` $ mcc -v -m MatrixCAB.m Compiler version: 8.1 (R2021b) Dependency analysis by REQUIREMENTS. Parsing file "/users/ulambda/MatrixCAB.m" (referenced from command line). Generating file "/users/ulambda/readme.txt". Generating file "MatrixCAB.sh". ``` The compiler documentation can be found at [https://ch.mathworks.com/help/compiler/mcc.html](https://ch.mathworks.com/help/compiler/mcc.html) Note that there are now 3 new files: `readme.txt` `run_MatrixCAB.sh` `MatrixCAB` If we take a look at the last file we see that it's an executable file ``` $ file MatrixCAB MatrixCAB: ELF 64-bit LSB executable, x86-64, version 1 (SYSV), dynamically linked (uses shared libs), for GNU/Linux 2.6.32, BuildID[sha1]=ad76a4654419e7968208a77a172f103afe2d77c2, stripped ``` The curious are welcome to look at the output from `ldd` which shows what the executable is linked to. ``` $ module load matlab-runtime $ ldd MatrixCAB ``` The `readme.txt` explains in great detail how to run the compiled object and the `run_MatrixCAB.sh` script is for launching the job. In order to make use of the executable we need to load the MATLAB runtime environment module ``` module load matlab-runtime ``` Please note that the runtime has to correspond to the version of mcc used to compile the .m file. Please see the following page for the corresponding runtime and compiler versions: [https://ch.mathworks.com/products/compiler/matlab-runtime.html](https://ch.mathworks.com/products/compiler/matlab-runtime.html) On the DCSR clusters the modules are configured to have the same version naming scheme: ``` matlab-runtime/2021b matlab/2021b ``` The runtime module sets the `MCR_PATH` variable which is needed by the `run_MatrixCAB.sh` script. To launch the compiled MatrixCAB object we need to put all the elements together: `sh run_MatrixCAB.sh $MCR_PATH` Obviously this should be done on a compute node using a job script: ```shell #!/bin/bash #SBATCH --time 00-00:05:00 #SBATCH --cpus-per-task 1 #SBATCH --mem 4000M module load matlab-runtime/2021b MATLAB_SCRIPT=MatrixCAB sh run_$MATLAB_SCRIPT.sh $MCR_PATH echo "Finished - next time I'll port my code to Julia" ``` ## Task farming with Matlab When processing numerous Matlab jobs in parallel on the clusters, you will likely encounter stability issues with some jobs failing randomly, other hanging (see below the explanations from Matlab support). To solve the issue, you must set the MCR\_CACHE\_ROOT environment variable (see [https://ch.mathworks.com/help/compiler\_sdk/ml\_code/mcr-component-cache-and-ctf-archive-embedding.html](https://ch.mathworks.com/help/compiler_sdk/ml_code/mcr-component-cache-and-ctf-archive-embedding.html)) in order that the same location (by default in your home directory) is not used by all jobs. For job arrays, you can adopt the following: ``` #!/bin/bash #SBATCH --array=1-5 #SBATCH --partition cpu #SBATCH --mem=8G #SBATCH --time=00:15:00 module load matlab-runtime/2021b # Create a task-specific MCR_CACHE_ROOT directory mcr_cache_root=/tmp/$USER/MCR_CACHE_ROOT_${SLURM_ARRAY_JOB_ID}_${SLURM_ARRAY_TASK_ID} mkdir -pv $mcr_cache_root export MCR_CACHE_ROOT=$mcr_cache_root ### YOUR MATLAB ANALYSIS HERE MATLAB_SCRIPT=MatrixCAB sh run_$MATLAB_SCRIPT.sh $MCR_PATH ### # Tidy up the place rm -rv $mcr_cache_root ``` #### #### Explanations from Matlab support > When running a MATLAB Compiler standalone executable, the MCR\_CACHE\_ROOT location is used by the standalone executable to extract the [deployable archive](https://www.mathworks.com/help/compiler/deployable-archive.html) into. As the name suggests, the extracted archive is cached in this location, meaning the archive is extracted the very first time you run the application and then for consecutive runs the already extracted data from the cache is used. > > There are mechanisms in place which try to ensure that when you run multiple instances of the same application at the same time, you do not run into any concurrency issues with this cache (e.g. a second instance should not also try to extract the archive if the first instance was already in the process of doing this). However, there are some limitations to these mechanisms; they were designed to deal with concurrency issues which might occur if an interactive user would run a handful of concurrent instances of the application; when doing this interactively this implies that you are not starting all those instances at exactly the same point in time and there are at least a few seconds between starting each instance. If you are somehow starting *a lot* of instances at *virtual the same time* (through some shell script, or possible even some cluster scheduler), this mechanism may break down. The likelihood of running into issues increases even more if the cache is in located on a shared network drive, shared by multiple machines (which can definitely be the case for a home directory), and all these machines are running instances of the same application. > > This is probably what you are running into then. Giving each instance its own cache location would prevent those issues altogether as there would be no concurrency in the first place. # Using Conda and Anaconda Conda is a package manager system for Python and other tools and is widely used in some areas such as bioinformatics and data science. On personal computers it is a useful way to install a stack of tools. The full documentation can be found at [https://docs.conda.io/projects/conda/en/latest/user-guide/index.html](https://docs.conda.io/projects/conda/en/latest/user-guide/index.html) ***Warning: Conda can be used freely for research purposes but pay attention to never use the "default" channel since it is not free in a research context like UNIL ([https://www.anaconda.com/blog/is-conda-free)](https://www.anaconda.com/blog/is-conda-free)). As a replacement to "default" channel, please use "conda-forge". If you have any doubt about that please contact us at (and start the subject with DCSR).*** A tutorial video on using Conda on the cluster is available [here](https://formations.unil.ch/course/view.php?id=511). #### Setting up Conda First load the appropriate modules ```bash dcsrsoft use 20241118 module load miniforge3/24.11.3-2 conda_init ``` Please ignore any messages about updating to a newer version of conda! #### Configuring Conda By default Conda will put everything including downloads in your home directory. Due to the limited space available this is probable not what you want. We strongly recommend that you create a `.condarc` file in your home directory with the following options: ```yaml pkgs_dirs: - /work/path/to/my/project/space/conda_pkgs auto_activate_base: false channels: - conda-forge ``` where the path is the path to your project space on /work - we do not recommend installing things in /scratch as they might be automatically deleted. You may also wish to add a non standard `env_dirs` ```yaml envs_dirs: - ~/myproject-envs ``` Please see the full `condarc` documentation for all the possible configuration options [https://docs.conda.io/projects/conda/en/latest/user-guide/configuration/use-condarc.html](https://docs.conda.io/projects/conda/en/latest/user-guide/configuration/use-condarc.html) #### Using Conda virtual environments The basic commands for creating conda environments are: ##### Creation ```bash conda create --name $MY_CONDA_ENV_NAME ``` ##### Activation ```bash conda activate $MY_CONDA_ENV_NAME ``` ##### Deactivation ```bash conda deactivate ``` ##### Environment in specific location If you need to create an environment in a non standard location: ```bash conda create --prefix $MY_CONDA_ENV_PATH conda activate $MY_CONDA_ENV_PATH conda deactivate ``` ##### Installing packages The base commands are: ``` conda search $PACKAGE_NAME conda install $PACKAGE_NAME ``` #### Running Slurm jobs with conda Since Conda needs some initialization before being used, a Sbatch script must explicitly ask to run bash in *login* mode. This can be performed by adding `--login` option to the shebang. Here is an example of Sbatch script using Conda: ```shell #!/bin/bash --login #SBATCH --time 00-00:05:00 #SBATCH --cpus-per-task 1 #SBATCH --mem 4G dcsrsoft use 20241118 module load miniforge3 conda_init conda activate $MY_CONDA_ENV_PATH … ``` # Using Mamba to install Conda packages [Mamba](https://mamba.readthedocs.io/en/latest/) is an alternative to Conda package manager. The main advantage is its speed regarding dependency resolution. #### Setting up Mamba The proposed installation is based on `micromamba` and doesn't require any installation on the cluster. You just have to add the following line to your `~/.bashrc` file: ``` export MAMBA_ROOT_PREFIX="/work/FAC/INSTITUTE/PI/PROJECT/mamba_root" ``` Of course, replace `/work/FAC/INSTITUTE/PI/PROJECT` with the path corresponding to your project. Then, you just have to load the module and run the initialization process with the following command: ``` module load micromamba mamba_init ``` Finally, you have to logout from the cluster and the environment will be properly configured at the next login. #### Using Mamba Instead of using `conda` commands, you can replace `conda` with `micromamba`. For instance: ``` micromamba create --prefix ./my_mamba_env micromamba activate ./my_mamba_env micromamba install busco -c conda-forge -c bioconda busco -v micromamba deactivate ``` #### Restriction You cannot use Mamba with virtual environment created previously with Conda. Such environments must be recreated. # AlphaFold The project home page where you can find the latest information is at [https://github.com/deepmind/alphafold](https://github.com/deepmind/alphafold) For details on how to run the model please see the [Supplementary Information article](https://www.nature.com/articles/s41586-021-03819-2) For some ideas on how to separate the CPU and GPU parts: [https://github.com/Zuricho/ParallelFold](https://github.com/Zuricho/ParallelFold). Alternatively - check out what has [already been calculated](https://www.alphafold.ebi.ac.uk) #### Note on GPU usage Whilst Alphafold makes use of GPUs for the inference part of the modelling, depending on the use case, this can be a small part of the running time as shown by the `timings.json` file that is produced for every run: For the T1024 test case: ``` { "features": 6510.152379751205, "process_features_model_1_pred_0": 3.555035352706909, "predict_and_compile_model_1_pred_0": 124.84101128578186, "relax_model_1_pred_0": 25.707252502441406, "process_features_model_2_pred_0": 2.0465400218963623, "predict_and_compile_model_2_pred_0": 104.1096305847168, "relax_model_2_pred_0": 14.539108514785767, "process_features_model_3_pred_0": 1.7761900424957275, "predict_and_compile_model_3_pred_0": 82.07982850074768, "relax_model_3_pred_0": 13.683411598205566, "process_features_model_4_pred_0": 1.8073537349700928, "predict_and_compile_model_4_pred_0": 82.5819890499115, "relax_model_4_pred_0": 15.835367441177368, "process_features_model_5_pred_0": 1.9143474102020264, "predict_and_compile_model_5_pred_0": 77.47663712501526, "relax_model_5_pred_0": 14.72615647315979 } ``` That means that out of the ~2 hour run time 1h48 is spend running "classical" code (mostly hhblits) and only ~10 minutes is spent on the GPU. ***As such do not request 2 GPUs as the potential speedup is negligible and this will block resources for other users*** For multimer modelling the GPU part can take longer and depending on what you need it might be worth turning off relaxation. Always check the **timings.json** file to see where time is being spent! If we look at the overall efficiency of the job using seff we see: ``` Nodes: 1 Cores per node: 24 CPU Utilized: 03:28:24 CPU Efficiency: 7.33% of 1-23:21:36 core-walltime Job Wall-clock time: 01:58:24 Memory Utilized: 81.94 GB Memory Efficiency: 40.97% of 200.00 GB ``` #### #### Reference databases The reference databases needed for AlphaFold have been made available in `/reference/alphafold` so there is no need to download them - the directory name is the date on which the databases were downloaded. ``` $ ls /reference/alphafold/ 20210719 20211104 20220414 20221206 ``` New versions will be downloaded if required. The versions correspond to: - `20210719` - Initial Alphafold 2.0 release - `20211104` - 2.1 release with multimer data - `20220414` - Updated weights - `20221206` - Updated weights #### Using containers The Alphafold project recommend using Docker to run the code which works on cloud or personal resources but not when using shared HPC systems as administrative access (required for Docker) is obviously not permitted. ##### Singularity container We provide Singularity image which can be used on the DCSR clusters and these can be found in /dcsrsoft/singularity/containers/ The currently available image is: - alphafold-032e2f2.sif When running the image directly it is necessary to provide all the paths to databases which is error prone and tedious. ``` $ singularity run /dcsrsoft/singularity/containers/alphafold-032e2f2.sif --helpshort Full AlphaFold protein structure prediction script. flags: /app/alphafold/run_alphafold.py: --[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins. (default: 'false') --bfd_database_path: Path to the BFD database for use by HHblits. --data_dir: Path to directory of supporting data. --db_preset: : Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs) (default: 'full_dbs') --fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique basename as the basename is used to name the output directories for each prediction. (a comma separated list) --hhblits_binary_path: Path to the HHblits executable. (default: '/opt/conda/bin/hhblits') --hhsearch_binary_path: Path to the HHsearch executable. (default: '/opt/conda/bin/hhsearch') --hmmbuild_binary_path: Path to the hmmbuild executable. (default: '/usr/bin/hmmbuild') --hmmsearch_binary_path: Path to the hmmsearch executable. (default: '/usr/bin/hmmsearch') --is_prokaryote_list: Optional for multimer system, not used by the single chain system. This list should contain a boolean for each fasta specifying true where the target complex is from a prokaryote, and false where it is not, or where the origin is unknown. These values determine the pairing method for the MSA. (a comma separated list) --jackhmmer_binary_path: Path to the JackHMMER executable. (default: '/usr/bin/jackhmmer') --kalign_binary_path: Path to the Kalign executable. (default: '/usr/bin/kalign') --max_template_date: Maximum template release date to consider. Important if folding historical test sets. --mgnify_database_path: Path to the MGnify database for use by JackHMMER. --model_preset: : Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model (default: 'monomer') --obsolete_pdbs_path: Path to file containing a mapping from obsolete PDB IDs to the PDB IDs of their replacements. --output_dir: Path to a directory that will store the results. --pdb70_database_path: Path to the PDB70 database for use by HHsearch. --pdb_seqres_database_path: Path to the PDB seqres database for use by hmmsearch. --random_seed: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic. (an integer) --small_bfd_database_path: Path to the small version of BFD used with the "reduced_dbs" preset. --template_mmcif_dir: Path to a directory with template mmCIF structures, each named .cif --uniclust30_database_path: Path to the Uniclust30 database for use by HHblits. --uniprot_database_path: Path to the Uniprot database for use by JackHMMer. --uniref90_database_path: Path to the Uniref90 database for use by JackHMMER. --[no]use_precomputed_msas: Whether to read MSAs that have been written to disk. WARNING: This will not check if the sequence, database or configuration have changed. (default: 'false') Try --helpfull to get a list of all flags. ``` To run the container - here we are using a GPU so the `--nv` flag must be used to make the GPU visible inside the container ``` module load singularity singularity run --nv /dcsrsoft/singularity/containers/alphafold-032e2f2.sif ``` ##### Helper Scripts In order to make life simpler there is a wrapper script: run\_alphafold\_032e2f2.sh - this can be found at: /dcsrsoft/singularity/containers/run\_alphafold\_032e2f2.sh Please copy it to your working directory ``` $ bash /dcsrsoft/singularity/containers/run_alphafold_032e2f2.sh --help Please make sure all required parameters are given Usage: /dcsrsoft/singularity/containers/run_alphafold_032e2f2.sh Required Parameters: -d Path to directory of supporting data -o Path to a directory that will store the results. -f Path to FASTA files containing sequences. If a FASTA file contains multiple sequences, then it will be folded as a multimer. To fold more sequences one after another, write the files separated by a comma -t Maximum template release date to consider (ISO-8601 format - i.e. YYYY-MM-DD). Important if folding historical test sets Optional Parameters: -g Enable NVIDIA runtime to run with GPUs (default: true) -r Whether to run the final relaxation step on the predicted models. Turning relax off might result in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage (default: true) -e Run relax on GPU if GPU is enabled (default: true) -n OpenMM threads (default: all available cores) -a Comma separated list of devices to pass to 'CUDA_VISIBLE_DEVICES' (default: 0) -m Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model (default: 'monomer') -c Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs) (default: 'full_dbs') -p Whether to read MSAs that have been written to disk. WARNING: This will not check if the sequence, database or configuration have changed (default: 'false') -l How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer (default: 5) -b Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins (default: 'false') ``` An example batch script using the helper script is: ``` #!/bin/bash #SBATCH -c 24 #SBATCH -p gpu #SBATCH --gres=gpu:1 #SBATCH --gres-flags=enforce-binding #SBATCH --mem 200G #SBATCH -t 6:00:00 module purge module load singularityce export SINGULARITY_BINDPATH="/scratch,/dcsrsoft,/users,/work,/reference" bash /dcsrsoft/singularity/containers/run_alphafold_032e2f2.sh -d /reference/alphafold/20221206 -t 2022-12-06 -n 24 -g true -f ./T1024.fasta -o /scratch/ulambda/alphafold/runtest ``` #### Alphafold without containers Fans of Conda may also wish to check out [https://github.com/kalininalab/alphafold\_non\_docker](https://github.com/kalininalab/alphafold_non_docker). Just make sure to `module load gcc miniconda3` rather than following the exact procedure! # Alphafold 3 **Disclaimer:** this page is provided for experimental support only! **Disclaimer 2**: pay attention to the terms of use provided [here](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md)! The project home page where you can find the latest information [there](https://github.com/google-deepmind/alphafold3). ### Using Alphafold 3 through a container The Apptainer/Singularity container for Alphafold 3 is available at `/dcsrsoft/singularity/containers/alphafold-v3.sif`. As stated on the Github page, it is possible to test Alphafold 3 with the following JSON input (named `fold_input.json`): ```json {   "name": "2PV7",   "sequences": [     {       "protein": {         "id": ["A", "B"],         "sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"       }     }   ],   "modelSeeds": [1],   "dialect": "alphafold3",   "version": 1 } ``` To ease the use of Alphafold 3, we have downloaded: - the databases to `/reference/alphafold3/db` - the model to `/reference/alphafold3/model` Here an example of Slurm job that can be used to run Alphafold 3 with the above JSON file: ```bash #!/bin/bash -l #SBATCH --time 2:00:00 #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --partition gpu #SBATCH --gres gpu:1 #SBATCH --gres-flags enforce-binding #SBATCH --cpus-per-task 8 #SBATCH --mem=64G dcsrsoft use 20241118 module load apptainer export APPTAINER_BINDPATH="/scratch,/work,/users,/reference" mkdir -p output apptainer run --nv /dcsrsoft/singularity/containers/alphafold-v3.sif --json_path=fold_input.json --output_dir=output --model_dir=/reference/alphafold3/model --db_dir=/reference/alphafold3/db ``` # CryoSPARC First of all, if you plan to use CryoSPARC on the cluster, please contact us to get a port number (you will understand later why it's important). CryoSPARC can be used on Curnagl and take benefit from Nvidia A100 GPUs. This page presents the installation in the /work storage location, so that it can be shared among the members of the same project. The purpose is to help you with installation, but in case of problem, don't hesitate to look at the [official documentation](https://guide.cryosparc.com/setup-configuration-and-management/how-to-download-install-and-configure). ## 1. Get a license A free license can be obtained for non-commercial use from [Structura Biotechnology](https://guide.cryosparc.com/setup-configuration-and-management/how-to-download-install-and-configure/obtaining-a-license-id). You will receive an email containing your license ID. It is similar to: 235e3142-d2b0-17eb-c43a-9c2461c1234d ## 2. Prerequisites Before starting the installation we suppose that: - DCSR gave you the following port number: 45678 - you want to install Cryosparc to the following location: /work/FAC/FBM/DMF/ulambda/cryosparc - your license ID is: 235e3142-d2b0-17eb-c43a-9c2461c1234d Obviously you must not use those values and they must be modified. ## 3. Install CryoSPARC First, connect to the Curnagl login node using your favourite SSH client and follow the next steps. #### Define the 3 prerequisites variables ```shell export LICENSE_ID="235e3142-d2b0-17eb-c43a-9c2461c1234d" export CRYOSPARC_ROOT=/work/FAC/FBM/DMF/ulambda/cryosparc export CRYOSPARC_PORT=45678 ``` #### Create some directories and download the packages ```shell mkdir -p $CRYOSPARC_ROOT mkdir -p $CRYOSPARC_ROOT/database mkdir -p $CRYOSPARC_ROOT/scratch mkdir -p $CRYOSPARC_ROOT/curnagl_config cd $CRYOSPARC_ROOT curl -L https://get.cryosparc.com/download/master-latest/$LICENSE_ID -o cryosparc_master.tar.gz curl -L https://get.cryosparc.com/download/worker-latest/$LICENSE_ID -o cryosparc_worker.tar.gz tar xf cryosparc_master.tar.gz tar xf cryosparc_worker.tar.gz ``` #### Create `$CRYOSPARC_ROOT/curnagl_config/cluster_info.json` Use your favourite editor to fill the file with the following content: ```JSON { "qdel_cmd_tpl": "scancel {{ cluster_job_id }}", "worker_bin_path": "/work/FAC/FBM/DMF/ulambda/cryosparc/cryosparc_worker/bin/cryosparcw", "title": "curnagl", "cache_path": "/work/FAC/FBM/DMF/ulambda/cryosparc/scratch", "qinfo_cmd_tpl": "sinfo --format='%.8N %.6D %.10P %.6T %.14C %.5c %.6z %.7m %.7G %.9d %20E'", "qsub_cmd_tpl": "sbatch {{ script_path_abs }}", "qstat_cmd_tpl": "squeue -j {{ cluster_job_id }}", "cache_quota_mb": 1000000, "send_cmd_tpl": "{{ command }}", "cache_reserve_mb": 10000, "name": "curnagl" } ```

Pay attention to `worker_bin_path` and `cache_path` variables, they must be adapted to your setup. `cache_reserve_mb` and `cache_quota_mb` might have to be modified, depending on your needs.

#### Create `$CRYOSPARC_ROOT/curnagl_config/cluster_script.sh` Use your favourite editor to fill the file with the following content: ```shell #!/bin/bash #SBATCH --job-name=cryosparc_{{ project_uid }}_{{ job_uid }} #SBATCH --partition={{ "gpu" if num_gpu > 0 else "cpu" }} #SBATCH --time=12:00:00 #SBATCH --output={{ job_log_path_abs }} #SBATCH --error={{ job_log_path_abs }} #SBATCH --nodes=1 #SBATCH --mem={{ (ram_gb*1024*2)|int }}M #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task={{ num_cpu }} #SBATCH --gres=gpu:{{ num_gpu }} #SBATCH --gres-flags=enforce-binding srun {{ run_cmd }} ``` #### Install CryoSPARC master ``` cd $CRYOSPARC_ROOT/cryosparc_master ./install.sh --license $LICENSE_ID --hostname curnagl --dbpath $CRYOSPARC_ROOT/database --port $CRYOSPARC_PORT ```

At the end of the installation process, the installer asks you if you want to modify your `~/.bashrc` file, please answer yes.

#### Start CryoSPARC and create a user ``` export PATH=$CRYOSPARC_ROOT/cryosparc_master/bin:$PATH cryosparcm start cryosparcm createuser --email "ursula.lambda@unil.ch" --password "ursulabestpassword" --username "ulambda" --firstname "Ursula" --lastname "Lambda" ```

Of course, when creating the user, you have to use appropriate information, the password shouldn't be your UNIL password.

#### Install CryoSPARC worker First you have to connect to a GPU node: ```shell Sinteractive -G1 -m8G ``` Once you are connected to the node: ```shell export LICENSE_ID="235e3142-d2b0-17eb-c43a-9c2461c1234d" export CRYOSPARC_ROOT=/work/FAC/FBM/DMF/ulambda/cryosparc cd $CRYOSPARC_ROOT/cryosparc_worker ./install.sh --license $LICENSE_ID ``` At the end of the process, you can logout. #### Configure the cluster workers ```shell cd $CRYOSPARC_ROOT/curnagl_config cryosparcm cluster connect ``` ## 4. Connection to the web interface You have to create a tunnel from your laptop to the Curnagl login node: ```shell ssh -N -L 8080:localhost:45678 ulambda@curnagl.dcsr.unil.ch ```

Please note that the port 45678 **must** be modified according to the one that DCSR gave you, and ulambda **must** be replaced with your UNIL login.

Then you can open a Web browser the following address: [http://localhost:8080](http://localhost:8080). [![image-1643304261513.png](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/scaled-1680-/image-1643304261513.png)](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/image-1643304261513.png) Here you have to use the credentials defined when you created a user. ## 5. Working with CryoSPARC When you start working with CryoSPARC on Curnagl, you have to start it from the login node: ```shell cryosparcm start ``` When you have finished, you should stop CryoSPARC in order to avoid wasting resources on Curnagl login node: ```shell cryosparcm stop ``` # Compiling and running MPI codes
To illustrate the procedure we will compile and run a MPI hello world example from [mpitutorial.com](https://mpitutorial.com/). First we download the source code:
``` $ wget https://raw.githubusercontent.com/mpitutorial/mpitutorial/gh-pages/tutorials/mpi-hello-world/code/mpi_hello_world.c ``` ### Compiling with GCC To compile the code, we first need to load the gcc and mvapich2 modules: ``` $ module load mvapich2 ```
Then we can produce the executable called `mpi_hello_world` by compiling the source code `mpi_hello_world.c`:
``` $ mpicc mpi_hello_world.c -o mpi_hello_world ```
The `mpicc` tool is a wrapper around the gcc compiler that adds the correct options for linking MPI codes and if you are curious you can run `mpicc -show` to see what it does.
To run the executable we create a Slurm submission script called `run_mpi_hello_world.sh`, where we ask to run a total of 4 MPI tasks with (at max) 2 tasks per node:
``` #!/bin/bash #SBATCH --time 00-00:05:00 #SBATCH --mem=2G #SBATCH --ntasks 4 #SBATCH --ntasks-per-node 2 #SBATCH --cpus-per-task 1 module purge module load gcc module load mvapich2 module list EXE=mpi_hello_world [ ! -f $EXE ] && echo "EXE $EXE not found." && exit 1 srun $EXE ```
Finally, we submit our MPI job with:
``` $ sbatch run_mpi_hello_world.sh ```
Upon completion you should get something like:
``` ... Hello world from processor dna001.curnagl, rank 1 out of 4 processors Hello world from processor dna001.curnagl, rank 3 out of 4 processors Hello world from processor dna004.curnagl, rank 0 out of 4 processors Hello world from processor dna004.curnagl, rank 2 out of 4 processors ``` It is important to check is that you have a single group of 4 processors and not 4 groups of 1 processor. If that's the case, you can now compile and run your own MPI application. The important bit of the script is the `srun $EXE` as MPI jobs but be started with a job launcher in order to run multiple processes on multiple nodes. # Software local installation This page gives an example of a local installation of a software, i.e. a software that will be only available to yourself. For simplicity we assume here that the software you want to install is available as a single binary file. To be executed from anywhere the binary must be placed in a directory contained in your PATH environment variable. We use here a directory called "bin" in your home directory: ``` $ mkdir ~/bin ``` Then, edit your ~/.bashrc file to add the newly created directory to your search path by adding this line: `export PATH=~/bin:$PATH` Then reload your .bashrc to take into account this change: ``` $ source ~/.bashrc ``` Now, you can simply copy your binary to ~/bin and it will be available from anywhere for execution: ``` $ cp /path/to/downloaded/my_binary ~/bin ``` Finally, make sure your binary is executable: ``` $ chmod +x ~/bin/my_binary ``` # Rstudio on the Urblauna cluster Rstudio can be run on the Urblauna cluster from within a singularity container, with an interactive interface provided on the web browser of a [Guacamole](https://u-web.dcsr.unil.ch/) session. Running interactively with Rstudio on the clusters is only meant for testing. Development must be carried out on the users workstations, and production runs must be accomplished [from within R scripts/codes in batch mode](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/r-on-the-clusters-old/).

The command Rstudio is now available in r-light module. You have to do a reservation first with Sinteractive, ask the right amount of resources and then launch the command 'Rstudio'.

### Procedure ```bash Sinteractive # specify here the right amount of resources module load r-light Rstudio ```

The procedure below is now deprecated !!

#### Preparatory steps on Curnagl side A few operations have to be executed on the Curnagl cluster: 1. Create a directory in your /work project dedicated to be used as an R library, for instance: `mkdir /work/FAC/FBM/DBC/mypi/project/R_ROOT` 2. Optional : install required R packages, for instance `ggplot2` `module load gcc rexport R_LIBS_USER=/work/FAC/FBM/DBC/mypi/project/R_ROOTR>>>install.packages("ggplot2")` #### The batch script Create a file **rstudio-server.sbatch** with the following contents (it must be on the cluster, but the exact location does not matter): ```bash #!/bin/bash -l #SBATCH --account <<>> #SBATCH --job-name rstudio-server #SBATCH --signal=USR2 #SBATCH --output=rstudio-server.job #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --cpus-per-task 1 #SBATCH --mem 8G #SBATCH --time 02:00:00 #SBATCH --partition interactive #SBATCH --export NONE RLIBS_USER_DIR=<<>> RSTUDIO_CWD=~ RSTUDIO_SIF="/dcsrsoft/singularity/containers/rstudio-4.3.2.sif" module load python singularityce module load r RLIBS_DIR=${R_ROOT}/rlib/R/library module unload r # Create temp directory for ephemeral content to bind-mount in the container RSTUDIO_TMP=$(mktemp --tmpdir -d rstudio.XXX) mkdir -p -m 700 \ ${RSTUDIO_TMP}/run \ ${RSTUDIO_TMP}/tmp \ ${RSTUDIO_TMP}/var/lib/rstudio-server mkdir -p ${RSTUDIO_CWD}/.R cat > ${RSTUDIO_TMP}/database.conf < ${RSTUDIO_TMP}/rsession.sh <&2 <&2 exit $SINGULARITY_EXIT_CODE ``` You need to carefully replace, at the beginning of the file, the following elements: - On line 3: **<<<*ACCOUNT\_NAME>>>*** with the project id that was attributed to your PI for the given project - On line 14: **<<<*RLIBS\_PATH>>>*** must be replaced with the **absolute path** (ex. */work/FAC/.../R\_ROOT*) to the chosen folder you created on the preparatory steps #### Running Rstudio Submit a job for running Rstudio from within the cluster with: ``` [me@urblauna ~]$ sbatch rstudio-server.sbatch ``` Once the job is running (you can check that with Squeue), a new file rstudio-server.job is then automatically created. Its contents will give you instructions on how to proceed in order to start a new Rstudio remote session from Guacamole. In this script we have reserved 2 hours # DCSR GitLab service **What is it?** The DCSR hosted version control service ([https://gitlab.dcsr.unil.ch](https://gitlab.dcsr.unil.ch)) is primarily intended for the users of the "sensitive" data clusters which do not have direct internet access. It is not an official UNIL wide version control service! It is accessible from both the sensitive data services and the UNIL network. From outside the UNIL network a VPN connection is required. It is open to all registered users of the DCSR facilities and is hosted on reliable hardware. **Should I use it?** If you are a user of the sensitive data clusters/services then the answer is yes. For other users it may well be more convenient to use internet accessible services such as c4science.ch or GitHub.com as these allow for external collaborations and do not require VPN access or an account on the DCSR systems. # Running Busco A Singularity container is available for version 4.0.6 of Busco. To run it, you need to proceed as follows: ``` $ module load singularityce $ export SINGULARITY_BINDPATH="/scratch,/users,/work" ``` Some configuration files included in the container must be copied in a writable location. So create a directory in your /scratch, e.g. called "busco\_config" ``` $ mkdir /path/to/busco_config ``` Then we copy the stuff out of the container to the newly created directory: ``` $ singularity exec /dcsrsoft/singularity/containers/busco-4.0.6 cp -rv /opt/miniconda/config/. /path/to/busco_config ``` Now we need to set the AUGUSTUS\_CONFIG\_PATH environment variable to the newly created and populated busco\_config directory: ``` $ export AUGUSTUS_CONFIG_PATH=/path/to/busco_config ``` Finally, you should now be able to run a test dataset from busco (see [https://gitlab.com/ezlab/busco/-/tree/master/test\_data/eukaryota](https://gitlab.com/ezlab/busco/-/tree/master/test_data/eukaryota)): ``` $ curl -O https://gitlab.com/ezlab/busco/-/raw/master/test_data/eukaryota/genome.fna ``` And launch the analysis. Note: in `$AUGUSTUS_CONFIG_PATH` you have a copy of the default `config.ini` used here, so you can copy, modify it and use it in the `--config` option in the following command: ``` $ singularity exec /dcsrsoft/singularity/containers/busco-4.0.6 busco --config /opt/miniconda/config/config.ini -i genome.fna -c 8 -m geno -f --out test_eukaryota ``` Then download the reference log: ``` curl -O https://gitlab.com/ezlab/busco/-/raw/master/test_data/eukaryota/expected_log.txt ``` And compare to the one you generated. # SWITCHfilesender from the cluster #### Switch Filesender Filesender is a service provided by SWITCH to transfer files over http. Normally files are uploaded via a web browser but this is not possible from the DCSR clusters. In order to avoid having to transfer the files to your local computer it is possible to use the Filesender command line tools as explained below #### Configuring the CLI tools Connect to [https://filesender.switch.ch](https://filesender.switch.ch) then go to the profile tab [![Screenshot 2022-01-13 at 15.14.02.png](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/scaled-1680-/screenshot-2022-01-13-at-15-14-02.png)](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/screenshot-2022-01-13-at-15-14-02.png) Then click on "Create API secret" to generate a code that will be used to allow you to authenticate [![Screenshot 2022-01-13 at 15.14.37.png](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/scaled-1680-/screenshot-2022-01-13-at-15-14-37.png)](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/screenshot-2022-01-13-at-15-14-37.png) This will generate a long string like `ab56bf28434d1fba1d5f6g3aaf8776e55fd722df205197` This code should never be shared Then connect to Curnagl and run the following commands to download the CLI tool and the configuration ``` cd mkdir ~/.filesender wget https://filesender.switch.ch/clidownload.php -O filesender.py wget https://filesender.switch.ch/clidownload.php?config=1 -O ~/.filesender/filesender.py.ini ``` You will then need to edit the` ~/.filesender/filesender.py.ini` file using your preferred tool You need to enter your username as show in the Filesender profile and the API key that you generated *Note that at present, unlike the other Switch services this is not your EduID account!* ``` [system] base_url = https://filesender.switch.ch/filesender2/rest.php default_transfer_days_valid = 20 [user] username = Ursula.Lambda@unil.ch apikey = ab56bf28434d1fba1d5f6g3aaf8776e55fd722df205197 ``` #### Transferring files Now that we have done this we can transfer files - note that the modules must be loaded in order to have a python with the required libraries. ``` [ulambda@login ~]$ module load gcc python [ulambda@login ~]$ python3 filesender.py -p -r ethz.collaborator@protonmail.ch results.zip Uploading: /users/ulambda/results.zip 0-5242880 0% Uploading: /users/ulambda/results.zip 5242880-10485760 6% Uploading: /users/ulambda/results.zip 10485760-15728640 11% Uploading: /users/ulambda/results.zip 15728640-20971520 17% Uploading: /users/ulambda/results.zip 20971520-26214400 23% Uploading: /users/ulambda/results.zip 26214400-31457280 29% Uploading: /users/ulambda/results.zip 31457280-36700160 34% Uploading: /users/ulambda/results.zip 36700160-41943040 40% Uploading: /users/ulambda/results.zip 41943040-47185920 46% Uploading: /users/ulambda/results.zip 47185920-52428800 52% Uploading: /users/ulambda/results.zip 52428800-57671680 57% Uploading: /users/ulambda/results.zip 57671680-62914560 63% Uploading: /users/ulambda/results.zip 62914560-68157440 69% Uploading: /users/ulambda/results.zip 68157440-73400320 74% Uploading: /users/ulambda/results.zip 73400320-78643200 80% Uploading: /users/ulambda/results.zip 78643200-83886080 86% Uploading: /users/ulambda/results.zip 83886080-89128960 92% Uploading: /users/ulambda/results.zip 89128960-91575794 97% Uploading: /users/ulambda/results.zip 91575794 100% ``` A mail will be sent to who can then download the file # Filetransfer from the cluster #### filetransfer.dcsr.unil.ch [https://filetransfer.dcsr.unil.ch](https://filetransfer.dcsr.unil.ch) is a service provided by the DCSR to allow you to transfer files to and from external collaborators. This is an alternative to SWITCHFileSender and the space available is 6TB with a maximum per user limit of 4TB - this space is shared between all users so it is unlikely that you will be able to transfer 4TB of data at once. The filetransfer service is based on LiquidFiles and the user guide is available at [https://man.liquidfiles.com/userguide.html](https://man.liquidfiles.com/userguide.html) In order to transfer files to and from the DCSR clusters without using the web browser it is also possible to the API REST as explained below #### Configuring the service First you need to connect to the web interface at [https://filetransfer.dcsr.unil.ch](https://filetransfer.dcsr.unil.ch) and connect using your UNIL username (e.g. ulambda for Ursula Lambda) and password. This is not your EduID password but rather the one you use to connect to the clusters. Once connected go to settings (the cog symbol in the top right corner) then the API tab [![Screenshot 2022-01-25 at 10.11.35.png](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/scaled-1680-/screenshot-2022-01-25-at-10-11-35.png)](https://wiki.unil.ch/ci/uploads/images/gallery/2022-01/screenshot-2022-01-25-at-10-11-35.png) The API key is how you authenticate from the clusters and this secret should never be shared. It can be reset via the yellow button. #### Transferring files from the cluster To upload a file and create a file link: ``` module load liquidfiles liquidfiles -k $APIKEY file_example_TIFF_.tiff ``` You can then connect to the web interface from you workstation to manage the files and send messages as required. As preparing and uploading files can take a while we recommend that this is performed in a tmux session which means that even if your connection to the cluster is lost the process continues and you can reconnect. #### Transferring large files You follow the same procedure: ``` module load liquidfiles liquidfiles -k $APIKEY myfile.ffdata ``` The liquidfiles tool will chuck the file and it will send it to the server. Once all the chunks are uploaded the file will be assembled/processed and after a short while it will be visible in the web interface. Here we see a previously uploaded file of 304 GB called `myfile.ffdata` [![Screenshot 2022-02-11 at 20.19.32.png](https://wiki.unil.ch/ci/uploads/images/gallery/2022-02/scaled-1680-/screenshot-2022-02-11-at-20-19-32.png)](https://wiki.unil.ch/ci/uploads/images/gallery/2022-02/screenshot-2022-02-11-at-20-19-32.png) # R on the clusters (old) R is provided via the [DCSR software stack](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/dcsr-software-stack) ### Interactive mode To load R: ```shell module load r R # Then you can use R interactively > ... ``` ### Batch mode While using R in batch mode, you have to use `Rscript` to launch your script. Here is an example of sbatch script, `run_r.sh`: ```shell #!/bin/bash #SBATCH --time 00-00:20:00 #SBATCH --cpus-per-task 1 #SBATCH --mem 4G module load r Rscript my_r_script.R ``` Then, just submit the job to Slurm: ```shell sbatch run_r.sh ``` ### Package installation A number of core packages are installed centrally - you can see what is available by using the `library()` function. Given the number of packages and multiple versions available other packages should be installed by the user. Installing R packages is pretty straightforward thanks to [install.packages()](https://stat.ethz.ch/R-manual/R-devel/library/utils/html/install.packages.html) function. However, be careful since it might fill your home directory very quickly. For big packages with large amount of dependencies, like `adegenet` for instance, you will probably reach the quota before the end of the installation. Here is a solution to mitigate that problem: - Remove your current R library (or set up an alternate one as explained in the section [Setting up an alternate personal library](#bkmrk-setting-up-an-altern) below): ```shell rm -rf $HOME/R ``` - Create a new library in your scratch directory (obviously modify the path according to your situation): ``` mkdir -p /work/FAC/FBM/DEE/my_py/default/jdoe/R ``` - Create a symlink to locate the R library on the scratch dir: ```shell cd $HOME ln -s /work/FAC/FBM/DEE/my_py/default/jdoe/R ``` - Install your R packages #### Handling dependencies Sometimes R packages depend on external libraries. For most of cases the library is already installed on the cluster you just need to load the module before trying to install the package from the R session. If the installation of package is still failing you need to define the following variables. For example, if our package depend on gsl and mpfr libraries, we need to do the following: ```bash module load gsl mpfr export CPATH=$GSL_ROOT/include:$MPFR_ROOT/include export LIBRARY_PATH=$GSL_ROOT/lib:$MPFR_ROOT/lib ``` ### Setting up an alternate personal library If you want to set up an alternate location where to install R packages, you can proceed as follows: ``` mkdir -p ~/R/my_personal_lib2 # If you already have a ~/.Renviron file, make a backup cp -iv ~/.Renviron ~/.Renviron_backup echo 'R_LIBS_USER=~/R/my_personal_lib2' > ~/.Renviron ``` Then relaunch R. Packages will then be installed under `~/R/my_personal_lib2`. # Sandbox containers #### Container basics For how to use Singularity/Apptainer containers please see our course at: [http://dcsrs-courses.ad.unil.ch/r\_python\_singularity/r\_python\_singularity.html](http://dcsrs-courses.ad.unil.ch/r_python_singularity/r_python_singularity.html) #### Sandboxes A container image (the .sif file) is read only and its contents cannot be changed which makes them perfect for distributing safe in the knowledge that they should run exactly as they were created. Sometimes, especially when developing things, it's very useful to be able to interactively modify a container and this is what sandboxes are for. Please be aware that anything done by hand is not reproducible so all steps should be transferred to the container definition file. #### Creating and modifying a sandbox Note that the steps here should be run on the cluster login node (curnagl.dcsr.unil.ch) as it is currently the only machine with the configuration in place to allow containers to be built. To start you need a basic definition file - this can be an empty OS or something more complicated that already has some configuration. In the following example we will use a definition that installs the latest version of R. We will then try and install extra packages before creating the immutable SIF image. Here's our file which we save as `newR.def` ``` Bootstrap: docker From: ubuntu:20.04 %post apt update apt install -y locales gnupg-agent sed -i '/^#.* en_.*.UTF-8 /s/^#//' /etc/locale.gen sed -i '/^#.* fr_.*.UTF-8 /s/^#//' /etc/locale.gen locale-gen # install two helper packages we need apt install -y --no-install-recommends software-properties-common dirmngr # add the signing key (by Michael Rutter) for these repos wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 51716619E084DAB9 # add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" apt install -y --no-install-recommends r-base ``` ##### ##### Create the sandbox Change to your scratch space /scratch/username and: ``` $ module load singularityce $ singularity build --fakeroot --sandbox newR newR.def WARNING: The underlying filesystem on which resides "/scratch/username/myR" won't allow to set ownership, as a consequence the sandbox could not preserve image's files/directories ownerships INFO: Starting build... Getting image source signatures Copying blob d7bfe07ed847 [--------------------------------------] 0.0b / 0.0b Copying config 2772dfba34 done .. .. .. Processing triggers for libc-bin (2.31-0ubuntu9.9) ... Processing triggers for systemd (245.4-4ubuntu3.17) ... Processing triggers for mime-support (3.64ubuntu1) ... INFO: Creating sandbox directory... INFO: Build complete: myR ``` This will create a directory called newR which is the writable container image. Have a look inside and see what there is! ##### Run and edit the image Before running the container we need to set up the filesystems that will be visible inside - here we want /users and /scratch to be visible ``` $ export SINGULARITY_BINDPATH="/users,/scratch" $ mkdir newR/users $ mkdir newR/scratch ``` Now we launch the image with an interactive shell ``` $ singularity shell --writable --fakeroot newR/ Singularity> ``` On the command line we can then work interactively with the image. As we are going to be installing R packages we know that we need some extra tools: ``` Singularity> apt-get install make gcc g++ gfortran ``` Now we can launch R and install some packages ``` Singularity> R R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid" Copyright (C) 2022 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) .. > install.packages('tibble') Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) also installing the dependencies ‘glue’, ‘cli’, ‘utf8’, ‘ellipsis’, ‘fansi’, ‘lifecycle’, ‘magrittr’, ‘pillar’, ‘rlang’, ‘vctrs’ trying URL 'https://cloud.r-project.org/src/contrib/glue_1.6.2.tar.gz' Content type 'application/x-gzip' length 106510 bytes (104 KB) ================================================== downloaded 104 KB .. .. ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (tibble) ``` Keep iterating until things are correct but don't forget to write down all the steps and transfer then to the definition file to allow for future reproducible builds. ##### Sandbox to SIF ``` $ singularity build --fakeroot R-4.2.1-production.sif newR/ ``` You will now have a SIF file that can be used in the normal way ``` $ singularity run R-4.2.1-production.sif R R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid" Copyright (C) 2022 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) .. > ``` Remember that files on /scratch will be automatically deleted if there isn't enough free space so save your definitions in a git repository and move the SIF images to your project space in /work # Course software for decision trees / random forests In the practicals, we will use only a small dataset and we will need only little computation power and memory ressources. You can therefore do the practicals on various computing platforms. However, since the participants may use various types of computers and softwares, we recommend to use the UNIL JupyterLab to do the practicals. - [JupyterLab](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-decision-trees-random-forests#bkmrk-jupyterlab): Working on the cloud is convenient because the installation of the Python and R packages is already done and you will be working with a Jupyter Notebook style even if you use R. Note, however, that the UNIL JupyterLab will only be active during the course and for one week following its completion, so in the long term you should use either your laptop or Curnagl. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - [Laptop](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-decision-trees-random-forests#bkmrk-laptop): This is good if you want to work directly on your laptop, but you will need to install the required libraries on your laptop. Warning: We will give general instructions on how to install the libraries on your laptop but it is sometimes tricky to find the right library versions and we will not be able to help you with the installation. The installation should take about 15 minutes. - [Curnagl](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-decision-trees-random-forests#bkmrk-curnagl): This is efficient if you are used to work on a cluster or if you intend to use one in the future to work on large projects. If you have an account you can work on your /scratch folder or ask us to be part of the course project but please contact us at least a week before the course. If you do not have an account to access the UNIL cluster Curnagl, please contact us at least a week before the course so that we can give you a temporary account. The installation should take about 15 minutes. Note that it is also possible to use JupyterLab on Curnagl: see [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster) If you choose to work on the UNIL JupyterLab, then you do not need to prepare anything since all the necessary libraries will already be installed on the UNIL JupyterLab. In all cases, you will receive a guest username during the course, so you will be able to work on the UNIL JupyterLab. Otherwise, if you prefer to work on your laptop or on Curnagl, please make sure you have a working installation before the day of the course as on the day we will be unable to provide any assistance with this. If you have difficulties with the installation on Curnagl we can help you so please contact us before the course at helpdesk@unil.ch with subject: DCSR ML course. On the other hand, if you are unable to install the libraries on your laptop, we will unfortunately not be able to help you (there are too many particular cases), so you will need to use the UNIL Jupyter Lab during the course. Before the course, we will send you all the files that are needed to do the practicals. ### **JupyterLab** Here are some instructions for using the UNIL JupyterLab to do the practicals. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. The webpage's link will be given during the course. Enter the login and password that you have received during the course. Due to a technical issue, you may receive a warning message "Your connection is not private". This is OK. So please proceed by clicking on the advanced button and then on "Proceed to dcsrs-jupyter.ad.unil.ch (unsafe)". #### **Python** Click on the "Cours ML" (or "ML") square button in the Notebook panel. Copy / paste the commands from the html practical file to the Jupyter Notebook. To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When you have finished the practicals, select File / Log out. #### **R** Click on the "Cours ML" (or "ML R") square button in the Notebook panel. Copy / paste the commands from the html practical file to the Jupyter Notebook. To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When you have finished the practicals, select File / Log out. ### **Laptop** You may need to install development tools including a C and Fortran compiler (e.g. Xcode on Mac, gcc and gfortran on Linux, Visual Studio on Windows). #### **Python installation** Here are some instructions for installing decision tree and random forest libraries on your laptop. You need Python >= 3.7. ##### **For Mac and Linux** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install scikit-learn pandas matplotlib graphviz seaborn ``` You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate pip3 install notebook jupyter notebook ``` ##### **For Windows** If you do not have Python installed, you can use either Conda: [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) or Python official installer: [https://www.python.org/downloads/windows/](https://www.python.org/downloads/windows/) Let us create a virtual environment. Open your terminal and type: ``` C:\Users\user>python -m venv mlcourse C:\Users\user>mlcourse\Scripts\activate.bat (mlcourse) C:\Users\user> (mlcourse) C:\Users\user>pip3 install scikit-learn pandas matplotlib graphviz seaborn ``` You can terminate the current session: ``` (mlcourse) C:\Users\user>deactivate C:\Users\user> ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` C:\Users\user>mlcourse\Scripts\activate.bat (mlcourse) C:\Users\user>pip3 install notebook (mlcourse) C:\Users\user>jupyter notebook ``` **Information:** Use Control-C to stop this server. #### **R installation** Here are some instructions for installing decision tree and random forest libraries on your laptop. You need R >= 4.0. Run R in your terminal or launch RStudio. For Windows users, you can download R here: [https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/ "https://cran.r-project.org/bin/windows/base/") REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions: Would you like to use a personal library instead? (yes/No/cancel) yes Would you like to create a personal library to install packages into? (yes/No/cancel) yes And select Switzerland for the CRAN mirror. ``` install.packages("rpart") install.packages("rpart.plot") install.packages("randomForest") install.packages("tidyverse") ``` The installation of "tidyverse" may lead to some conflicts, but do not worry you should be able to do the practicals fine. You can terminate the current R session: ``` q() ``` Save workspace image? \[y/n/c\]: n **TO DO THE PRACTICALS (today or another day):** Simply run R in your terminal or launch RStudio. ### **Curnagl** For the practicals, it will be convenient to be able to copy/paste text from a web page to the terminal on Curnagl. So please make sure you can do it before the course. You also need to make sure that your terminal has a X server. For Mac users, download and install XQuartz (X server): [https://www.xquartz.org/](https://www.xquartz.org/) For Windows users, download and install MobaXterm terminal (which includes a X server). Click on the "Installer edition" button on the following webpage: [https://mobaxterm.mobatek.net/download-home-edition.html](https://mobaxterm.mobatek.net/download-home-edition.html) For Linux users, you do not need to install anything. #### **Python installation** Here are some instructions for installing decision tree and random forest libraries on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question "do you want to save password ?" Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) ``` cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/ mkdir < my unil username > cd < my unil username > ``` For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1). ``` module load python/3.12.1 python -m venv mlcourse source mlcourse/bin/activate pip install scikit-learn pandas matplotlib graphviz seaborn ``` You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/< my unil username > ``` For convenience, you will work directly on the frontal node to do the practicals. Note however that it is normally not allowed to work directly on the frontal node, and you should use (Sinteractive -m 4G -c 1). ``` module load python/3.12.1 source mlcourse/bin/activate python ``` #### **R installation** Here are some instructions for installing decision tree and random forest libraries on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question “do you want to save password ?” Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) ``` cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/ mkdir < my unil username > cd < my unil username > ``` For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1). ``` module load r-light/4.4.1 R ``` REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions: Would you like to use a personal library instead? (yes/No/cancel) yes Would you like to create a personal library to install packages into? (yes/No/cancel) yes And select Switzerland for the CRAN mirror. ``` install.packages("rpart") install.packages("rpart.plot") install.packages("randomForest") install.packages("tidyverse") ``` The installation of "tidyverse" may lead to some conflicts, but do not worry you should be able to do the practicals fine. You can terminate the current R session: ``` q() ``` Save workspace image? \[y/n/c\]: n **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch cd /scratch/my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/< my unil username > ``` For convenience, you will work directly on the frontal node to do the practicals. Note however that it is normally not allowed to work directly on the frontal node, and you should use (Sinteractive -m 4G -c 1). ``` module load r-light/4.4.1 R ``` # Course software for introductory deep learning In the practicals, we will use only a small dataset and we will need only little computation power and memory ressources. You can therefore do the practicals on various computing platforms. However, since the participants may use various types of computers and softwares, we recommend to use the UNIL JupyterLab to do the practicals. - [JupyterLab](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-introductory-deep-learning#bkmrk-jupyterlab): Working on the cloud is convenient because the installation of the Python and R packages is already done and you will be working with a Jupyter Notebook style even if you use R. Note, however, that the UNIL JupyterLab will only be active during the course and for one week following its completion, so in the long term you should use either your laptop or Curnagl. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - [Laptop](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-introductory-deep-learning#bkmrk-laptop): This is good if you want to work directly on your laptop, but you will need to install the required libraries on your laptop. Warning: We will give general instructions on how to install the libraries on your laptop but it is sometimes tricky to find the right library versions and we will not be able to help you with the installation. The installation should take about 15 minutes. - [Curnagl](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-introductory-deep-learning#bkmrk-curnagl): This is efficient if you are used to work on a cluster or if you intend to use one in the future to work on large projects. If you have an account you can work on your /scratch folder or ask us to be part of the course project but please contact us at least a week before the course. If you do not have an account to access the UNIL cluster Curnagl, please contact us at least a week before the course so that we can give you a temporary account. The installation should take about 15 minutes. Note that it is also possible to use JupyterLab on Curnagl: see [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster) If you choose to work on the UNIL JupyterLab, then you do not need to prepare anything since all the necessary libraries will already be installed on the UNIL JupyterLab. In all cases, you will have access to the UNIL JupyterLab. Otherwise, if you prefer to work on your laptop or on Curnagl, please make sure you have a working installation before the day of the course as on the day we will be unable to provide any assistance with this. If you have difficulties with the installation on Curnagl we can help you so please contact us before the course at helpdesk@unil.ch with subject: DCSR ML course. On the other hand, if you are unable to install the libraries on your laptop, we will unfortunately not be able to help you (there are too many particular cases), so you will need to use the UNIL Jupyter Lab during the course. Before the course, we will send you all the files that are needed to do the practicals. ### **JupyterLab** Here are some instructions for using the UNIL JupyterLab to do the practicals. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. The webpage's link will be given during the course. Enter the login and password that you have received during the course. Due to a technical issue, you may receive a warning message "Your connection is not private". This is OK. So please proceed by clicking on the advanced button and then on "Proceed to dcsrs-jupyter.ad.unil.ch (unsafe)". #### **Python** You can work on Open On Demand or on a Server: - On Open On Demand: Fill in the form as shown in the lecture's slides. - On a Server: Click on the "ML" square button in the Notebook panel. Copy / paste the commands from the html practical file to the Jupyter Notebook. To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When using TensorFlow, you may receive a warning 2022-09-22 11:01:12.232756: W tensorflow/stream\_executor/platform/default/dso\_loader.cc:64\] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2022-09-22 11:01:12.232856: I tensorflow/stream\_executor/cuda/cudart\_stub.cc:29\] Ignore above cudart dlerror if you do not have a GPU set up on your machine. You should not worry. By default, TensorFlow is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course). When you have finished the practicals, select File / Log out. #### **R** You can work on Open On Demand or on a Server: On Open On Demand: Fill in the form as shown in the lecture's slides. On a Server: Click on the "ML R" square button in the Notebook panel. Copy / paste the commands from the html practical file to the Jupyter Notebook. To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When using TensorFlow, you may receive a warning 2022-09-22 11:01:12.232756: W tensorflow/stream\_executor/platform/default/dso\_loader.cc:64\] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2022-09-22 11:01:12.232856: I tensorflow/stream\_executor/cuda/cudart\_stub.cc:29\] Ignore above cudart dlerror if you do not have a GPU set up on your machine. You should not worry. By default, TensorFlow is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course). When you have finished the practicals, select File / Log out. ### **Laptop** You may need to install development tools including a C and Fortran compiler (e.g. Xcode on Mac, gcc and gfortran on Linux, Visual Studio on Windows). #### **Python installation** Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on your laptop. You need Python >= 3.8. ##### **For Linux** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow scikit-learn scikeras eli5 pandas matplotlib notebook keras-tuner ```

You may need to choose the right library versions, for example tensorflow==2.12.0

To check that Tensorflow was installed: ``` python3 -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Mac** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow-macos==2.12.0 scikit-learn==1.2.2 scikeras eli5 pandas matplotlib notebook keras-tuner ``` If you receive an error message such as: ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos Then, try the following command: ``` SYSTEM_VERSION_COMPAT=0 pip3 install tensorflow-macos==2.12.0 scikit-learn==1.2.2 scikeras eli5 pandas matplotlib notebook keras-tuner ``` If you have a Mac with M1 or more recent chip (if you are not sure have a look at "About this Mac"), you can also install the tensorflow-metal library to accelerate training on Mac GPUs (but this is not necessary for the course): ``` pip3 install tensorflow-metal ``` To check that Tensorflow was installed: ``` python3 -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Windows** If you do not have Python installed, you can use either Conda: [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) (see the instructions here: [https://conda.io/projects/conda/en/latest/user-guide/install/windows.html](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)) or Python official installer: [https://www.python.org/downloads/windows/](https://www.python.org/downloads/windows/) We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow scikit-learn scikeras eli5 pandas matplotlib notebook keras-tuner ```

You may need to choose the right library versions, for example tensorflow==2.12.0

To check that Tensorflow was installed: ``` python -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` mlcourse\Scripts\activate.bat jupyter notebook ``` #### **R installation** Here are some instructions for installing Keras with TensorFlow at the backend, and other libraries, on your laptop. The R keras is actually an interface to the Python Keras. In simple terms, this means that the keras R package allows you to enjoy the benefit of R programming while having access to the capabilities of the Python Keras package. You need R >= 4.0 and Python >= 3.8. REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions: Would you like to use a personal library instead? (yes/No/cancel) yes Would you like to create a personal library to install packages into? (yes/No/cancel) yes And select Switzerland for the CRAN mirror. ##### **For Mac, Windows and Linux** Run the following commands on your terminal: ``` cd ~ ls .virtualenvs # Create this directory only if you receive an error message # saying that this directory does not exist mkdir .virtualenvs ``` Then ``` cd ~/.virtualenvs python3 -m venv r-reticulate source r-reticulate/bin/activate # For Windows and Linux pip3 install tensorflow scikit-learn scikeras eli5 pandas matplotlib notebook keras-tuner # For Mac pip3 install tensorflow-macos==2.12.0 scikit-learn==1.2.2 scikeras eli5 pandas matplotlib notebook keras-tuner deactivate ```

You must name the environment 'r-reticulate' as otherwise it wont be able to find it.

You may need to choose the right library versions, for example tensorflow==2.12.0

Run R in your terminal and type ``` install.packages("keras") install.packages("reticulate") install.packages("ggplot2") install.packages("ggfortify") ``` To check that Keras was properly installed: ``` library(keras) library(tensorflow) is_keras_available(version = NULL) ``` There might be a warning message (see above) and the output should be something like "TRUE". You can terminate the current R session: ``` q() ``` Save workspace image? \[y/n/c\]: n **TO DO THE PRACTICALS (today or another day):** Then you can either run R in your terminal or launch RStudio. ### **Curnagl** For the practicals, it will be convenient to be able to copy/paste text from a web page to the terminal on Curnagl. So please make sure you can do it before the course. You also need to make sure that your terminal has a X server. For Mac users, download and install XQuartz (X server): [https://www.xquartz.org/](https://www.xquartz.org/) For Windows users, download and install MobaXterm terminal (which includes a X server). Click on the "Installer edition" button on the following webpage: [https://mobaxterm.mobatek.net/download-home-edition.html](https://mobaxterm.mobatek.net/download-home-edition.html) For Linux users, you do not need to install anything. When testing if TensorFlow was properly installed (see below) you may receive a warning 2022-03-16 12:15:00.564218: W tensorflow/stream\_executor/platform/default/dso\_loader.cc:64\] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD\_LIBRARY\_PATH: /dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/python-3.8.8-tb3aceqq5wzx4kr5m7s5m4kzh4kxi3ex/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tcl-8.6.11-aonlmtcje4sgqf6gc4d56cnp3mbbhvnj/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tk-8.6.11-2gb36lqwohtzopr52c62hajn4tq7sf6m/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib64:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib 2022-03-16 12:15:00.564262: I tensorflow/stream\_executor/cuda/cudart\_stub.cc:29\] Ignore above cudart dlerror if you do not have a GPU set up on your machine. You should not worry. By default, TensorFlow is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course). #### **Python installation** Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question "do you want to save password ?" Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) ``` cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc mkdir < my unil username > cd < my unil username > ``` For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1). ``` git clone https://git.dcsr.unil.ch/ML-Courses/DL_INTRO.git module load python/3.10.13 python -m venv mlcourse source mlcourse/bin/activate pip install -r DL_INTRO/requirements.txt ``` To check that TensorFlow was installed: ``` python -c 'import tensorflow; print(tensorflow.version.VERSION)' ``` There might be a warning message (see above) and the output should be something like "2.9.2". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/< my unil username > ``` For convenience, you will work directly on the frontal node to do the practicals. Note however that it is normally not allowed to work directly on the frontal node, and you should use (Sinteractive -m 4G -c 1). ``` module load python/3.10.13 source mlcourse/bin/activate python ``` #### **R installation** Here are some instructions for installing Keras with TensorFlow at the backend, and other libraries, on the UNIL cluster called Curnagl. The R keras is actually an interface to the Python Keras. In simple terms, this means that the keras R package allows you to enjoy the benefit of R programming while having access to the capabilities of the Python Keras package. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question “do you want to save password ?” Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) ``` cd ~ module load python/3.10.13 r-light/4.4.1 git clone https://git.dcsr.unil.ch/ML-Courses/DL_INTRO.git cd ~/.virtualenvs python -m venv r-reticulate source r-reticulate/bin/activate pip install -r ~/DL_INTRO/requirements.txt ``` For convenience, you will install the libraries from the frontal node to do the practicals. Note however that it is normally recommended to install libraries from the interactive partition by using (Sinteractive -m 4G -c 1). REMARK: The R libraries will be installed in your home directory. To allow it, you must answer yes to the questions: Would you like to use a personal library instead? (yes/No/cancel) yes Would you like to create a personal library to install packages into? (yes/No/cancel) yes And select Switzerland for the CRAN mirror. ``` R install.packages("keras") install.packages("ggplot2") install.packages("ggfortify") ``` To check that Keras was properly installed: ``` library(keras) library(tensorflow) is_keras_available(version = NULL) ``` There might be a warning message (see above) and the output should be something like "TRUE". You can terminate the current R session: ``` q() ``` Save workspace image? \[y/n/c\]: n **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` For convenience, you will work directly on the frontal node to do the practicals. Note however that it is normally not allowed to work directly on the frontal node, and you should use (Sinteractive -m 4G -c 1). ``` cd ~ module load python/3.10.13 r-light/4.4.1 R ``` # JupyterLab on the curnagl cluster JupyterLab can be run on the curnagl cluster for testing purposes, only as an intermediate step in the porting of applications from regular workstations to curnagl. The installation is made inside a python virtual environment, and this tutorial covers the installation of the following kernels: IPyKernel (**python**), IRKernel (**R**), IJulia (**julia**), MATLAB kernel (**matlab**), IOctave (**octave**), stata\_kernel (**stata**) and sas\_kernel (**sas**). If the workstation is outside of the campus, first [connect to the VPN](https://www.unil.ch/ci/reseau-unil-chez-soi#guides-dinstallation). ### Creating the virtual environment First create/choose a folder ${WORK} under the **/scratch** or the **/work** filesystems under your project (ex. WORK=*/work/FAC/.../my\_project*). The following needs to be run only once on the cluster (preferably on an interactive computing node): ```bash module load gcc python python -m venv ${WORK}/jlab_venv ${WORK}/jlab_venv/bin/pip install jupyterlab ipykernel numpy matplotlib ``` The IPyKernel is automatically available. The other kernels need to be installed according to your needs. ### Installing the kernels **Each time you start a new session on the cluster, remember to define the variable ${WORK} according to the path you chose when creating the virtual environment.** #### IRKernel ```bash module load gcc r export R_LIBS_USER=${WORK}/jlab_venv/lib/Rlibs mkdir -p ${R_LIBS_USER} echo "install.packages('IRkernel', repos='https://stat.ethz.ch/CRAN/', lib=Sys.getenv('R_LIBS_USER'))" | R --no-save source ${WORK}/jlab_venv/bin/activate echo "IRkernel::installspec()" | R --no-save deactivate ``` #### IJulia ```bash module load gcc julia export JULIA_DEPOT_PATH=${WORK}/jlab_venv/lib/Jlibs julia -e 'using Pkg; Pkg.add("IJulia")' ``` #### MATLAB kernel ```bash ${WORK}/jlab_venv/bin/pip install matlab_kernel matlabengine==9.11.19 ``` #### IOctave ```bash ${WORK}/jlab_venv/bin/pip install octave_kernel echo "c.OctaveKernel.plot_settings = dict(backend='gnuplot')" > ~/.jupyter/octave_kernel_config.py ``` #### stata\_kernel ```bash module load stata-se ${WORK}/jlab_venv/bin/pip install stata_kernel ${WORK}/jlab_venv/bin/python -m stata_kernel.install sed -i "s/^stata_path = None/stata_path = $(echo ${STATA_SE_ROOT} | sed 's/\//\\\//g')\/stata-se/" ~/.stata_kernel.conf sed -i 's/stata_path = \(.*\)stata-mp/stata_path = \1stata-se/' ~/.stata_kernel.conf ``` #### sas\_kernel ```bash module load sas ${WORK}/jlab_venv/bin/pip install sas_kernel sed -i "s/'\/opt\/sasinside\/SASHome/'$(echo ${SAS_ROOT} | sed 's/\//\\\//g')/g" ${WORK}/jlab_venv/lib64/python3.9/site-packages/saspy/sascfg.py ``` ### Running JupyterLab **Before running JupyterLab, you need to start an interactive session!** ```bash Sinteractive ``` Take note of the name of the running node, that you will later need. On curnagl, you can type: ```bash hostname ``` If you didn't install all of the kernels, the corresponding lines should be ignored in the commands below. **The execution order is important, in the sense that loading the gcc module should always be done before activating virtual environments.** ```bash # Load python module load gcc python # IOctave (optional) module load octave gnuplot # IRKernel (optional) export R_LIBS_USER=${WORK}/jlab_venv/lib/Rlibs # IJulia (optional) export JULIA_DEPOT_PATH=${WORK}/jlab_venv/lib/Jlibs # JupyterLab environment source ${WORK}/jlab_venv/bin/activate # Launch JupyterLab (on the shell a link that can be copied on the browser will appear) cd ${WORK} jupyter-lab deactivate ``` Before you can copy and paste the link into your favorite browser, you will need to establish an SSH tunnel to the interactive node. From a UNIX-like workstation, you can establish the SSH tunnel to the curnagl node with the following command (replace <username> with your user name, and <hostname> with the name of the node you obtained above, and the <port> number is obtained from the link, it is typically 8888): ``` ssh -n -N -J @curnagl.dcsr.unil.ch -L :localhost: @ ``` You will be prompted for your password. When you have finished, you can close the tunnel with Ctrl-C. ### Note on Python/R/Julia modules and packages The modules you install manually from JupyterLab in Python, R or Julia end up inside the JupyterLab virtual environment (${WORK}/jlab\_venv). They are hence isolated and independent from your Python/R/Julia instances outside of the virtual environment. # JupyterLab with C++ on the curnagl cluster JupyterLab can be run on the curnagl cluster for testing purposes, only as an intermediate step in the porting of applications from regular workstations to curnagl. This tutorial intends to setup JupyterLab on the cluster together with the support for the C++ programming language, through the [xeus-cling kernel](https://github.com/jupyter-xeus/xeus-cling). Besides the IPyKernel kernel for the python language, which is natively supported, we will also provide the option to install support for the following kernels: IRKernel (**R**), IJulia (**julia**), MATLAB kernel (**matlab**), IOctave (**octave**), stata\_kernel (**stata**) and sas\_kernel (**sas**). These instructions are hence related to the [JupyterLab on the curnagl cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster) tutorial, but the implementation is very different because a JIT compiler is necessary in order to interactively process C++ code. Instead of using a python virtual environment in order to isolate and install JupyterLab, the kernels and the corresponding dependencies, we use [micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html). ### Setup of the micromamba virtual environment First create/choose a folder ${WORK} under the **/scratch** or the **/work** filesystems under your project (ex. WORK=*/work/FAC/.../my\_project*). The following needs to be run only once on the cluster (preferably on an interactive computing node): ```bash module load gcc python export MAMBA_ROOT=/dcsrsoft/spack/external/micromamba export MAMBA_ROOT_PREFIX="${WORK}/micromamba" eval "$(${MAMBA_ROOT}/micromamba shell hook --shell=bash)" micromamba create -y --prefix ${WORK}/jlab_menv python==3.9.13 jupyterlab ipykernel numpy matplotlib xeus-cling -c conda-forge ``` The IPyKernel and the xeus-cling kernel for handling C++ are now available. The other kernels need to be installed according to your needs. ### Installing the optional kernels **Each time you start a new session on the cluster, remember to define the variable ${WORK} according to the path you chose when creating the virtual environment.** #### IRKernel ```bash module load gcc r export R_LIBS_USER=${WORK}/jlab_menv/lib/Rlibs mkdir ${R_LIBS_USER} echo "install.packages('IRkernel', repos='https://stat.ethz.ch/CRAN/', lib=Sys.getenv('R_LIBS_USER'))" | R --no-save export MAMBA_ROOT=/dcsrsoft/spack/external/micromamba export MAMBA_ROOT_PREFIX="${WORK}/micromamba" eval "$(${MAMBA_ROOT}/micromamba shell hook --shell=bash)" echo "IRkernel::installspec()" | micromamba run --prefix ${WORK}/jlab_menv R --no-save ``` #### IJulia ```bash module load gcc julia export JULIA_DEPOT_PATH=${WORK}/jlab_menv/lib/Jlibs julia -e 'using Pkg; Pkg.add("IJulia")' ``` #### MATLAB kernel ```bash ${WORK}/jlab_menv/bin/pip install matlab_kernel matlabengine==9.11.19 ``` #### IOctave ```bash ${WORK}/jlab_menv/bin/pip install octave_kernel echo "c.OctaveKernel.plot_settings = dict(backend='gnuplot')" > ~/.jupyter/octave_kernel_config.py ``` #### stata\_kernel ```bash module load stata-se ${WORK}/jlab_menv/bin/pip install stata_kernel ${WORK}/jlab_menv/bin/python -m stata_kernel.install sed -i "s/^stata_path = None/stata_path = $(echo ${STATA_SE_ROOT} | sed 's/\//\\\//g')\/stata-se/" ~/.stata_kernel.conf sed -i 's/stata_path = \(.*\)stata-mp/stata_path = \1stata-se/' ~/.stata_kernel.conf ``` #### sas\_kernel ```bash module load sas ${WORK}/jlab_menv/bin/pip install sas_kernel sed -i "s/'\/opt\/sasinside\/SASHome/'$(echo ${SAS_ROOT} | sed 's/\//\\\//g')/g" ${WORK}/jlab_venv/lib64/python3.9/site-packages/saspy/sascfg.py ``` ### Running JupyterLab **Before running JupyterLab, you need to start an interactive session!** ```bash Sinteractive ``` Take note of the name of the running node, that you will later need. On curnagl, you can type: ```bash hostname ``` If you didn't install all of the kernels, the corresponding lines should be ignored in the commands below. **The execution order is important, in the sense that loading the gcc module should always be done before activating virtual environments.** ```bash # Load python and setup the environment for micromamba to work module load gcc python export MAMBA_ROOT=/dcsrsoft/spack/external/micromamba export MAMBA_ROOT_PREFIX="${WORK}/micromamba" eval "$(${MAMBA_ROOT}/micromamba shell hook --shell=bash)" # IOctave (optional) module load octave gnuplot # IRKernel (optional) export R_LIBS_USER=${WORK}/jlab_menv/lib/Rlibs # IJulia (optional) export JULIA_DEPOT_PATH=${WORK}/jlab_menv/lib/Jlibs # Launch JupyterLab (on the shell a link that can be copied on the browser will appear) cd ${WORK} micromamba run --prefix ${WORK}/jlab_menv jupyter-lab ``` Before you can copy and paste the link into your favorite browser, you will need to establish an SSH tunnel to the interactive node. From a UNIX-like workstation, you can establish the SSH tunnel to the curnagl node with the following command (replace <username> with your user name, and <hostname> with the name of the node you obtained above, and the <port> number is obtained from the link, it is typically 8888): ``` ssh -n -N -J @curnagl.dcsr.unil.ch -L :localhost: ``` You will be prompted for your password. When you have finished, you can close the tunnel with Ctrl-C. ### Note on Python/R/Julia modules and packages The modules you install manually from JupyterLab in Python, R or Julia end up inside the JupyterLab virtual environment (${WORK}/jlab\_menv). They are hence isolated and independent from your Python/R/Julia instances outside of the virtual environment. # Dask on curnagl In order to use Dask in Curnagl you have to use the following packages: - dask - dask-jobqueue

Note: please make sure to use version 2022.11.0 or later. Previous versions have some bugs on worker-nodes that make them very slow when using several threads.

Dask makes easy to parallelize computations, you can run computational intensive methods on parallel by assigning those computations to different CPU resources. For example: ```python def cpu_intensive_method(x, y , z): # CPU computations return x + 1 futures = [] for x,y,z in zip(list_x, list_y, list_z): future = client.submit(cpu_intensive_method, x, y, z) futures.append(future) result = client.gather(futures) ``` This documentation proposes two types of use: - LocalCluster: this mode is very simple and can be used to easily parallelize computations by submitting just one job in the cluster. This is a good starting point - SlurmCluster: this mode handle more parallelisim by distributing work on several machines. It can handle load and submit automatically new jobs for increasing paralellisim ### Local cluster Python script looks like: ```python import dask from dask.distributed import Client, LocalCluster def compute(x): ""CPU demanding code" if __name__ == "__main__": cluster = LocalCluster() client = Client(address=cluster) parameters = [1, 2, 3, 4] for x in parameters: future = client.submit(inc, x) futures.append(future) result = client.gather(futures) ```

Call to LocalCluster and Client should be put inside the block if \_\_name\_\_ == "\_\_main\_\_". For more information, you can check the following link: [https://docs.dask.org/en/stable/scheduling.html](https://docs.dask.org/en/stable/scheduling.html)

The method LocalCluster() will deploy N workers, each worker using T threads such that NxT is equal to the number of cores reserved by SLURM. Dask will balance the number of workers and the number of threads per worker, the goal is to take advantage of GIL free workloads such as Numpy and Pandas. SLURM script: ```bash #SBATCH --job-name dask_job #SBATCH --ntasks 16 #SBATCH -N 1 #SBATCH --partition cpu #SBATCH --cpus-per-task 1 #SBATCH --time 01:00:00 #SBATCH --output=dask_job-%j.out #SBATCH --error=dask_job%j.error python script.py ``` Make sure to include the parameter `-N 1` otherwise SLURM will allocate tasks on different nodes and it will make Dask local cluster fail. You should adapt the parameter` --ntasks`, as we are using just one machine we can choose between 1 and 48. Just have in mind that the smallest the number the faster your job will start. You can choose to run with less processes but for a longer time. ### Slurm cluster The python script can be launched directly from the frontend but you need to keep you session open with tools such as `tmux `or `screen `otherwise your jobs will be cancelled. In your Python script you should put something like: ```python import dask from dask.distributed import Client from dask_jobqueue import SLURMCluster def compute(x): ""CPU demanding code" if __name__ == "__main__": cluster = SLURMCluster(cores=8, memory="40GB") client = Client(cluster) cluster.adapt(maximum_jobs=5, interval="10000 ms") for x in parameters: future = client.submit(inc, x) futures.append(future) result = client.gather(futures) ``` In this case DASK will launch jobs with 8 cores and 40GB of memory. The parameters `memory `and `cores` are mandatory. There are two methods to launch jobs: adapt and scale. `adapt` will launch/kill jobs by taking into account the load of your computation and how many computations in parallel you can run. You can put a limit on the number of jobs that will be launched. The parameter `interval` is necessary and needs to be set to `10000 ms` to avoid killing jobs too early. `scale` will create a static infrastructure composed of a fix number of jobs, specified with the parameters jobs. Example `scale(jobs=10)` This will launch 10 jobs independent from the load and the amount of computation you generate. #### Some facts about Slurm jobs and DASK You need to have in mind that the computation will depend on the availability of resources, if jobs are not running your computation will not start. So if you think that your computation is stuck, please verify first that jobs have been submitted and that they are running using the command: `squeue -u $USER`. By default the walltime is set to 30 min, you can use the parameter: `walltime` if you think that each individual computation will last more than the default time. Slurm files will be generated under the same directory where you launch your python command. Jobs will killed by Dask when there is no more computation to be done. If you see the message: `slurmstepd: error: *** JOB 25260254 ON dna051 CANCELLED AT 2023-03-01T11:00:19 ***` It is completely normal and it does not mean that there was an error in your computation. ### Optimal number of workers Both LocalCluster or SLURMCluster, will automatically balance the number of workers and the number of threads per worker. You can choose the number of workers using the parameter `n_workers`. If most of the computation relies on Numpy or Pandas, it is preferable to have only one worker `n_workers=1`. If most of the computation is pure Python code you should use as much workers as possible. Example: Local cluster: `LocalCluster(n_workers=int(os.environ['SLURM_NTASKS']))` Slurm cluster: `SLURMCluster(cores=8, memory="40GB", n_workers=8)` ### Example Here, it is an example code which illustrates the use of Dask. The code runs 40 multiplications of random matrices of size NXN, each computation returns the sum of all the elements of the result matrix: ```python import os import time import numpy as np from dask.distributed import Client, LocalCluster from dask_jobqueue import SLURMCluster SIZE = 9192 def compute(tag): np.random.seed(tag) A = np.random.random((SIZE,SIZE)) B = np.random.random((SIZE,SIZE)) start = time.time() C = np.dot(A,B) end = time.time() elapsed = end-start return elapsed, np.sum(C) if __name__ == "__main__": # cluster = LocalCluster(n_workers=int(os.environ['SLURM_NTASKS'])) cluster = SLURMCluster(memory="40GB", n_workers=8) client = Client(cluster) cluster.adapt(maximum_jobs=5, interval="10000 ms") N_ITER = 40 futures = [] for i in range(N_ITER): future = client.submit(compute, i) futures.append(future) results = client.gather(futures) print(results) ``` # Running the Isca framework on the cluster

Isca is a framework for the idealized modelling of the global circulation of planetary atmospheres at varying levels of complexity and realism. The framework is an outgrowth of models from GFDL designed for Earth's atmosphere, but it may readily be extended into other planetary regimes.

### Installation First of all define a folder ${WORK} on the /work or the /scratch filesystem (somewhere where you have write permissions): ```bash export WORK=/work/FAC/... mkdir -p ${WORK} ``` Load the following relevant modules and create a python virtual environment: ```bash dcsrsoft use arolle module load gcc/10.4.0 module load mvapich2/2.3.7 module load netcdf-c/4.8.1-mpi module load netcdf-fortran/4.5.4 module load python/3.9.13 python -m venv ${WORK}/isca_venv ``` Install the required python modules: ```bash ${WORK}/isca_venv/bin/pip install dask f90nml ipykernel Jinja2 numpy pandas pytest sh==1.14.3 tqdm xarray ``` Download and install the Isca framework: ```bash cd ${WORK} git clone https://github.com/ExeClim/Isca cd Isca/src/extra/python ${WORK}/isca_venv/bin/pip install -e . ``` Patch the Isca makefile: ```bash sed -i 's/-fdefault-double-8$/-fdefault-double-8 \\\n -fallow-invalid-boz -fallow-argument-mismatch/' ${WORK}/Isca/src/extra/python/isca/templates/mkmf.template.gfort ``` Create the environment file for curnagl: ```bash cat << EOF > ${WORK}/Isca/src/extra/env/curnagl-gfortran echo Loading basic gfortran environment # this defaults to ia64, but we will use gfortran, not ifort export GFDL_MKMF_TEMPLATE=gfort export F90=mpifort export CC=mpicc EOF ``` ### Compiling and running the Held-Suarez dynamical core test case Compilation takes place automatically at runtime. After logging in to the cluster, create a SLURM script file start.sbatch with the following contents: ```bash #!/bin/bash -l #SBATCH --account ACCOUNT_NAME #SBATCH --mail-type ALL #SBATCH --mail-user @unil.ch #SBATCH --chdir ${WORK} #SBATCH --job-name isca_held-suarez #SBATCH --output=isca_held-suarez.job.%j #SBATCH --partition cpu #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --cpus-per-task 16 #SBATCH --mem 8G #SBATCH --time 00:29:59 #SBATCH --export ALL dcsrsoft use arolle module load gcc/10.4.0 module load mvapich2/2.3.7 module load netcdf-c/4.8.1-mpi module load netcdf-fortran/4.5.4 WORK=$(pwd) export GFDL_BASE=${WORK}/Isca export GFDL_ENV=curnagl-gfortran export GFDL_WORK=${WORK}/isca_work export GFDL_DATA=${WORK}/isca_gfdl_data export C_INCLUDE_PATH=${NETCDF_C_ROOT}/include export LIBRARY_PATH=${NETCDF_C_ROOT}/lib sed -i "s/^NCORES =.*$/NCORES = $(echo ${SLURM_CPUS_PER_TASK:-1})/" ${GFDL_BASE}/exp/test_cases/held_suarez/held_suarez_test_case.py ${WORK}/isca_venv/bin/python $GFDL_BASE/exp/test_cases/held_suarez/held_suarez_test_case.py ``` You need to carefully replace, at the beginning of the file, the following elements: - On line 3: ***ACCOUNT\_NAME*** with the project id that was attributed to your PI for the given project - On line 5: ***<first.lastname>@unil.ch*** with your e-mail address (or double-comment that line with an additional '#' if you don't wish to receive e-mail notifications about the status of the job) - On line 7: ***${WORK}*** must be replaced with the **absolute path** (ex. */work/FAC/.../isca*) to the chosen folder you created on the installation steps - On line 15-17: you can adjust the number of CPUs, the memory and the time for the job (the present values are appropriate for the default Held-Suarez example) Then you can simply start the job: ```bash sbatch start.sbatch ``` # Running the MPAS framework on the cluster

The Model for Prediction Across Scales (MPAS) is a collaborative project for developing atmosphere, ocean and other earth-system simulation components for use in climate, regional climate and weather studies.

### Compilation First of all define a folder ${WORK} on the /work or the /scratch filesystem (somewhere where you have write permissions): ```bash export WORK=/work/FAC/... mkdir -p ${WORK} ``` Load the following relevant modules: ```bash module load gcc/11.4.0 module load mvapich2/2.3.7-1 module load parallel-netcdf/1.12.3 module load parallelio/2.6.2 export PIO=$PARALLELIO_ROOT export PNETCDF=$PARALLEL_NETCDF_ROOT ``` Download the MPAS framework: ```bash cd ${WORK} git clone https://github.com/MPAS-Dev/MPAS-Model --depth 1 --branch $(curl -sL https://api.github.com/repos/MPAS-Dev/MPAS-Model/releases/latest | grep -i "tag_name" | awk -F '"' '{print $4}') ```

This is going to download the source code of the latest release of MPAS. The last version that was successfully tested on the `curnagl` cluster with the present instructions is `v8.1.0` and future versions might need some adjustments to compile and run.

Patch the MPAS Makefile: ```bash sed -i 's/-ffree-form/-ffree-form -fallow-argument-mismatch/' ${WORK}/MPAS-Model/Makefile sed -i 's/ mpi_f08_test//' ${WORK}/MPAS-Model/Makefile ```

This is going to force MPAS to use the old MPI wrapper for Fortran 90. When compiling with GCC older than version 12.0, a bug in the C binding interoperability feature ([https://gcc.gnu.org/bugzilla/show\_bug.cgi?id=104100](https://gcc.gnu.org/bugzilla/show_bug.cgi?id=104100)) used by the MPI wrapper for Fortran 2008 breaks the code. If you are compiling with GCC 12.0 or newer, you do not need to patch and the new wrapper will be successfully used.

Compile: ```bash cd ${WORK}/MPAS-Model make gfortran CORE=init_atmosphere AUTOCLEAN=true PRECISION=single OPENMP=true USE_PIO2=true make gfortran CORE=atmosphere AUTOCLEAN=true PRECISION=single OPENMP=true USE_PIO2=true ``` ### Running a basic global simulation Here we aim at running a basic global simulation, just to test that the framework runs. we need to proceed in three steps: 1. Process time-invariant fields, which will be interpolated into a given mesh, this step produces a "static" file 2. Interpolating time-varying meteorological and land-surface fields from intermediate files (produced by the ungrib component of the WRF Pre-processing System), this step produces an "init" file 3. Run the basic simulation ##### Create the run folder and link to the binary files ```bash cd ${WORK} mkdir -p run cd run ln -s ${WORK}/MPAS-Model/init_atmosphere_model ln -s ${WORK}/MPAS-Model/atmosphere_model ``` ##### Get the mesh files ```bash cd ${WORK} wget https://www2.mmm.ucar.edu/projects/mpas/atmosphere_meshes/x1.40962.tar.gz wget https://www2.mmm.ucar.edu/projects/mpas/atmosphere_meshes/x1.40962_static.tar.gz cd run tar xvzf ../x1.40962.tar.gz tar xvzf ../x1.40962_static.tar.gz ``` ##### Create the configuration files for the "static" run The `namelist.init_atmosphere` file: ```bash cat << EOF > ${WORK}/run/namelist.init_atmosphere &nhyd_model config_init_case = 7 / &data_sources config_geog_data_path = '${WORK}/WPS_GEOG/' config_landuse_data = 'MODIFIED_IGBP_MODIS_NOAH' config_topo_data = 'GMTED2010' config_vegfrac_data = 'MODIS' config_albedo_data = 'MODIS' config_maxsnowalbedo_data = 'MODIS' / &preproc_stages config_static_interp = true config_native_gwd_static = true config_vertical_grid = false config_met_interp = false config_input_sst = false config_frac_seaice = false / EOF ``` The `streams.init_atmosphere` file: ```bash cat << EOF > ${WORK}/run/streams.init_atmosphere EOF ``` ##### Proceed to the "static" run You will need to make sure that the folder `${WORK}/WPS_GEOG` exists and contains all the appropriate data. First create a `start_mpas_init.sbatch` file (carefully replace on line #4 `ACCOUNT_NAME` by your actual project name and on line #6 appropriately type your e-mail address, or double-comment with an additional `#` if you don't wish to receive job notifications): ```bash cat << EOF > ${WORK}/run/start_mpas_init.sbatch #!/bin/bash -l #SBATCH --account ACCOUNT_NAME #SBATCH --mail-type ALL #SBATCH --mail-user @unil.ch #SBATCH --chdir ${WORK}/run #SBATCH --job-name mpas_init #SBATCH --output=mpas_init.job.%j #SBATCH --partition cpu #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --cpus-per-task 1 #SBATCH --mem 8G #SBATCH --time 00:59:59 #SBATCH --export ALL module load gcc/11.4.0 module load mvapich2/2.3.7-1 module load parallel-netcdf/1.12.3 module load parallelio/2.6.2 export PIO=\$PARALLELIO_ROOT export PNETCDF=\$PARALLEL_NETCDF_ROOT export LD_LIBRARY_PATH=\$PARALLELIO_ROOT/lib:\$PARALLEL_NETCDF_ROOT/lib:\$LD_LIBRARY_PATH srun ./init_atmosphere_model EOF ``` Now start the job with `sbatch start_mpas_init.sbatch` and at the end of the run, make sure that the log file `${WORK}/run/log.init_atmosphere.0000.out` displays no error. ##### Create the configuration files for the "init" run The `namelist.init_atmosphere` file: ```bash cat << EOF > ${WORK}/run/namelist.init_atmosphere &nhyd_model config_init_case = 7 config_start_time = '2014-09-10_00:00:00' / &dimensions config_nvertlevels = 55 config_nsoillevels = 4 config_nfglevels = 38 config_nfgsoillevels = 4 / &data_sources config_met_prefix = 'GFS' config_use_spechumd = false / &vertical_grid config_ztop = 30000.0 config_nsmterrain = 1 config_smooth_surfaces = true config_dzmin = 0.3 config_nsm = 30 config_tc_vertical_grid = true config_blend_bdy_terrain = false / &preproc_stages config_static_interp = false config_native_gwd_static = false config_vertical_grid = true config_met_interp = true config_input_sst = false config_frac_seaice = true / EOF ``` The `streams.init_atmosphere` file: ```bash cat << EOF > ${WORK}/run/streams.init_atmosphere EOF ``` ##### Proceed to the "init" run Just start again the job with `sbatch start_mpas_init.sbatch` and at the end of the run, make sure that the log file `${WORK}/run/log.init_atmosphere.0000.out` displays no error. ##### Create the configuration file for the global simulation The `namelist.atmosphere` file: ```bash cat << EOF > ${WORK}/run/namelist.atmosphere &nhyd_model config_time_integration_order = 2 config_dt = 720.0 config_start_time = '2014-09-10_00:00:00' config_run_duration = '0_03:00:00' config_split_dynamics_transport = true config_number_of_sub_steps = 2 config_dynamics_split_steps = 3 config_h_mom_eddy_visc2 = 0.0 config_h_mom_eddy_visc4 = 0.0 config_v_mom_eddy_visc2 = 0.0 config_h_theta_eddy_visc2 = 0.0 config_h_theta_eddy_visc4 = 0.0 config_v_theta_eddy_visc2 = 0.0 config_horiz_mixing = '2d_smagorinsky' config_len_disp = 120000.0 config_visc4_2dsmag = 0.05 config_w_adv_order = 3 config_theta_adv_order = 3 config_scalar_adv_order = 3 config_u_vadv_order = 3 config_w_vadv_order = 3 config_theta_vadv_order = 3 config_scalar_vadv_order = 3 config_scalar_advection = true config_positive_definite = false config_monotonic = true config_coef_3rd_order = 0.25 config_epssm = 0.1 config_smdiv = 0.1 / &damping config_zd = 22000.0 config_xnutr = 0.2 / &limited_area config_apply_lbcs = false / &io config_pio_num_iotasks = 0 config_pio_stride = 1 / &decomposition config_block_decomp_file_prefix = 'x1.40962.graph.info.part.' / &restart config_do_restart = false / &printout config_print_global_minmax_vel = true config_print_detailed_minmax_vel = false / &IAU config_IAU_option = 'off' config_IAU_window_length_s = 21600. / &physics config_sst_update = false config_sstdiurn_update = false config_deepsoiltemp_update = false config_radtlw_interval = '00:30:00' config_radtsw_interval = '00:30:00' config_bucket_update = 'none' config_physics_suite = 'mesoscale_reference' / &soundings config_sounding_interval = 'none' / EOF ``` The `streams.atmosphere` file: ```bash cat << 'EOF' > ${WORK}/run/streams.atmosphere EOF ``` #### Run the whole simulation You will need to copy relevant data to the run folder: ```bash cp ${WORK}/MPAS-Model/{GENPARM.TBL,LANDUSE.TBL,OZONE_DAT.TBL,OZONE_LAT.TBL,OZONE_PLEV.TBL,RRTMG_LW_DATA,RRTMG_SW_DATA,SOILPARM.TBL,VEGPARM.TBL} ${WORK}/run/. ``` Then create a `start_mpas.sbatch` file (carefully replace on line #4 `ACCOUNT_NAME` by your actual project name and on line #6 appropriately type your e-mail address, or double-comment with an additional `#` if you don't wish to receive job notifications): ```bash cat << EOF > ${WORK}/run/start_mpas.sbatch #!/bin/bash -l #SBATCH --account ACCOUNT_NAME #SBATCH --mail-type ALL #SBATCH --mail-user @unil.ch #SBATCH --chdir ${WORK}/run #SBATCH --job-name mpas_init #SBATCH --output=mpas_init.job.%j #SBATCH --partition cpu #SBATCH --nodes 1 #SBATCH --ntasks 1 #SBATCH --cpus-per-task 16 #SBATCH --mem 8G #SBATCH --time 00:59:59 #SBATCH --export ALL module load mvapich2/2.3.7-1 module load parallel-netcdf/1.12.3 module load parallelio/2.6.2 export PIO=\$PARALLELIO_ROOT export PNETCDF=\$PARALLEL_NETCDF_ROOT export LD_LIBRARY_PATH=\$PARALLELIO_ROOT/lib:\$PARALLEL_NETCDF_ROOT/lib:\$LD_LIBRARY_PATH srun ./atmosphere_model EOF ``` Now start the job with `sbatch start_mpas.sbatch` and at the end of the run, make sure that the log file `${WORK}/run/log.atmosphere.0000.out` displays no error. # Run OpenFOAM codes on Curnagl ### Script to run OpenFOAM code ##### **You are using OpenFOAM on your computer and you need more ressources. Let’s go on Curnagl! ** OpenFOAM is usually using MPI. Here is a bash script to run your parallelized OpenFOAM code. NTASKS should be replaced by the number of processors you want to use in your OpenFOAM code. It is good practice to put your OpenFOAM code in a bash file instead of calling OpenFOAM commands right into the sbatch file. For instance, create `openfoam.sh` in which you call your OpenFOAM code (replace commands with yours): ```bash !/bin/bash # First command decomposepar ... # Second command, if you are using a parallel command, CALL IT WITH SRUN COMMAND srun snappyHexMesh -parallel ... ``` Then, create a sbatch file to run your OpenFOAM bash file on Curnagl: ```bash #!/bin/bash -l  #SBATCH --job-name openfoam   #SBATCH --output openfoam.out  #SBATCH --partition cpu  #SBATCH --nodes 1   #SBATCH --ntasks NTASKS  #SBATCH --cpus-per-task 1  #SBATCH --mem 8G   #SBATCH --time 00:30:00 #SBATCH --export NONE module purge module load gcc/10.4.0 mvapich2/2.3.7 openfoam/2206  export SLURM_EXPORT_ENV=ALL # RUN YOUR BASH OPENFOAM CODE HERE bash ./openfoam.sh ```

Please note that running your parallelized OpenFOAM code should not be performed via `mpirun` but `srun`. For a complete MPI overview on Curnagl, please refer to [compiling and running MPI codes](https://wiki.unil.ch/ci/books/service-de-calcul-haute-performance-%28hpc%29/page/compiling-and-running-mpi-codes "compiling and running MPI codes") wiki.

### ### How do I transfer my OpenFOAM code to Curnagl ? You can upload your OpenFOAM code thanks to FileZilla or copy and paste data to the cluster thanks to the `scp` command. Example: I want to copy test.py to Curnagl. I run the following command: `scp test.py @curnagl.dcsr.unil.ch:/YOUR_PATH_ON_CURNAGL` Where `YOUR_PATH_ON_CURNAGL` is something like `/users/username/work/my_folder`. In these commands, do not forget to change `` with yours.

**This transfer can be done for any file type: .py, .csv, .h, images...**

**To copy a folder, use the command `scp -r`.**

**For more details, refer to [transfer files to/from Curnagl](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/transfer-files-tofrom-curnagl "transfer files to/from Curnagl") wiki.**

# Compiling software using cluster libraries If you see the following error when compiling a code on the cluster: ```bash fatal error: XXXX.h: No such file or directory ``` That means that the software you are trying to compile needs a specific header file provided by a third party library. In order to use a third party library, the compiler mainly needs two things: - a header file XXXX.h - the binary of the library: XXXXX.so By default in Linux systems, those files are located in default paths as: /usr, /lib, etc.. There are two ways to tell the compiler where to look for those files: Makefile or using compiler variables. ### Makefile Makefiles provide the following [Variables](https://www.gnu.org/software/make/manual/make.html#Implicit-Variables) : - CFLAGS - CXXFLAGS - FFLAGS - LDFLAGS The three first variables are used to pass extra options to a specific compiler and language, c, c++ and fortran respectively. The last variable is meant to be used to pass the option `-L -l` which are used by the linker. **Example** ```bash CFLAGS+= -I/usr/local/cuda/include LDFLAGS+= -L/usr/local/cuda/lib -lcudnn ``` Here we will tell the compiler where to find the include files and the location of libraries. Those variables should already be present on the makefile and used on the compilation process. #### GCC Variables if you are using GCC, you can use the following [Variables](https://gcc.gnu.org/onlinedocs/gcc/Environment-Variables.html) : - CPATH - LIBRARY\_PATH ```bash CPATH=/usr/local/cuda/include LIBRARY_PATH=/usr/local/cuda/lib ``` This would have the same result as modifying the variable on the Makefile. This procedure is very useful in case you do not have access to the Makefile or Makefile variables are not used during compilation. ### Using cluster libraries On the cluster, libraries are provided by modules which means that you need to tell the compiler to look for headers files and binary files in special locations. The procedure is the following: - load the library: module load XXX - find the name of the ROOT variable by executing: module show XXX - Use that variable on the CFLAFGS and LDFLAGS definition **Example** ```bash $ module load cuda $ module show cuda ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- /dcsrsoft/spack/arolle/v1.0/spack/share/spack/lmod/Zen2-IB/Core/cuda/11.6.2.lua: ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- whatis("Name : cuda") whatis("Version : 11.6.2") whatis("Target : zen") whatis("Short description : CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).") help([[CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). Note: This package does not currently install the drivers necessary to run CUDA. These will need to be installed manually. See: https://docs.nvidia.com/cuda/ for details.]]) depends_on("libxml2/2.9.13") prepend_path("LD_LIBRARY_PATH","/dcsrsoft/spack/arolle/v1.0/spack/opt/spack/linux-rhel8-zen/gcc-8.4.1/cuda-11.6.2-rswplbcorqlt6ywhcnbdisk6puje4ejf/lib64") prepend_path("PATH","/dcsrsoft/spack/arolle/v1.0/spack/opt/spack/linux-rhel8-zen/gcc-8.4.1/cuda-11.6.2-rswplbcorqlt6ywhcnbdisk6puje4ejf/bin") prepend_path("CMAKE_PREFIX_PATH","/dcsrsoft/spack/arolle/v1.0/spack/opt/spack/linux-rhel8-zen/gcc-8.4.1/cuda-11.6.2-rswplbcorqlt6ywhcnbdisk6puje4ejf/") setenv("CUDA_HOME","/dcsrsoft/spack/arolle/v1.0/spack/opt/spack/linux-rhel8-zen/gcc-8.4.1/cuda-11.6.2-rswplbcorqlt6ywhcnbdisk6puje4ejf") setenv("CUDA_ROOT","/dcsrsoft/spack/arolle/v1.0/spack/opt/spack/linux-rhel8-zen/gcc-8.4.1/cuda-11.6.2-rswplbcorqlt6ywhcnbdisk6puje4ejf") ``` You can observe that there is the variable `CUDA_ROOT` which is the one that should be used. ``` export CFLAGS="-I$CUDA_ROOT/include" LDFLAGS+= -L$(CUDA_ROOT)/lib64/stubs -L$(CUDA_ROOT)/lib64/ -lcuda -lcudart -lcublas -lcurand ``` This is quite a complex example, sometimes you only need `-L$(XXX_ROOT)/lib`. # Course software for Image Analysis with CNNs You can do the practicals on various computing platforms. However, since the participants may use various types of computers and softwares, we recommend to use the UNIL JupyterLab to do the practicals. - [JupyterLab](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-image-analysis-with-cnns#bkmrk-jupyterlab): Working on the cloud is convenient because the installation of the Python packages is already done and you will be working with a Jupyter Notebook style. Note, however, that the UNIL JupyterLab will only be active during the course and for one week following its completion, so in the long term you should use either your laptop or Curnagl. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - [Laptop](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-image-analysis-with-cnns#bkmrk-laptop): This is good if you want to work directly on your laptop, but you will need to install the required libraries on your laptop. Warning: We will give general instructions on how to install the libraries on your laptop but it is sometimes tricky to find the right library versions and we will not be able to help you with the installation. The installation should take about 15 minutes. - [Curnagl](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-image-analysis-with-cnns#bkmrk-curnagl): This is efficient if you are used to work on a cluster or if you intend to use one in the future to work on large projects. If you have an account you can work on your /scratch folder or ask us to be part of the course project but please contact us at least a week before the course. If you do not have an account to access the UNIL cluster Curnagl, please contact us at least a week before the course so that we can give you a temporary account. The installation should take about 15 minutes. Note that it is also possible to use JupyterLab on Curnagl: see [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster) If you choose to work on the UNIL JupyterLab, then you do not need to prepare anything since all the necessary libraries will already be installed on the UNIL JupyterLab. In all cases, you will receive a guest username during the course, so you will be able to work on the UNIL JupyterLab. Otherwise, if you prefer to work on your laptop or on Curnagl, please make sure you have a working installation before the day of the course as on the day we will be unable to provide any assistance with this. If you have difficulties with the installation on Curnagl we can help you, so please contact us before the course at helpdesk@unil.ch with subject: DCSR ML course. On the other hand, if you are unable to install the libraries on your laptop, we will unfortunately not be able to help you (there are too many particular cases), so you will need to use the UNIL Jupyter Lab during the course. Before the course, we will send you all the files that are needed to do the practicals. ### **JupyterLab** Here are some instructions for using the UNIL JupyterLab to do the practicals. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. The webpage's link will be given during the course. Enter the login and password that you have received during the course. #### **Image Classification** We have already prepared your workspace, including the data and notebook. However, in case there is a problem, you can follow the following instructions. Click on the button "New Folder" (the small logo of of folder with a "+" sign) and name it "models". Click again on the same button "New Folder" and name it "images". Double click on the "images" folder that you have just created. Click on the button "Upload Files" (the vertical arrow logo) and upload the three images (car.jpeg, frog.jpeg and ship.jpeg) that are included in "images" directory you have received for this course. Click on the folder logo (just on top of "Name") to come out of the "images" folder. Double click on the "models" folder and then click on the button "Upload Files" to upload all the "models.keras" and "models.npy" files that are included in the "models" directory you have received for this course. Click on the folder logo (just on top of "Name") to come out of the "models" folder. To work with the html file "Convolutional\_Neural\_Networks.html": - Click on the "CNN" square button in the Notebook panel - Copy / paste the commands from the html practical file to the Jupyter Notebook To work with the notebook "Convolutional\_Neural\_Networks.ipynb": - Upload the notebook "Convolutional\_Neural\_Networks.ipynb" - Double click on "Convolutional\_Neural\_Networks.ipynb" - Change the "ipykernel" (top right button "Python 3 ipykernel") to CNN In the practical code (i.e. the Python code in the html or ipynb file), the following paths were set: platform = "jupyter" PATH\_IMAGES = "./images" PATH\_MODELS = "./models" To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When using TensorFlow, you may receive a warning 2022-09-22 11:01:12.232756: W tensorflow/stream\_executor/platform/default/dso\_loader.cc:64\] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2022-09-22 11:01:12.232856: I tensorflow/stream\_executor/cuda/cudart\_stub.cc:29\] Ignore above cudart dlerror if you do not have a GPU set up on your machine. You should not worry. By default, TensorFlow is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course). When you have finished the practicals, select File / Log out. #### **Image Segmentation** Now click on the "ImageProcessing" square button in the Notebook panel. Copy / paste the commands from the html practical file to the Jupyter Notebook. To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. ### **Laptop** You may need to install development tools including a C and Fortran compiler (e.g. Xcode on Mac, gcc and gfortran on Linux, Visual Studio on Windows). #### **Image Classification** Please decide in which folder (or path) you want to do the practicals and go there: ``` cd THE_PATH_WHERE_I_DO_THE_PRACTICALS ``` Then you need to create two folders: ``` mkdir images mkdir models ``` Please copy/paste the three images (car.jpeg, frog.jpeg and ship.jpeg) that are included in the folder "images" you have received for this course in the "images" folder. And also copy/paste all the "models.keras" and "models.npy" files that are included in "models" directory you have received for this course. In the practical code (i.e. the Python code in the html file), you will need to set the paths as follows: platform = "laptop" PATH\_IMAGES = "./images" PATH\_MODELS = "./models" Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on your laptop. You need Python >= 3.8. ##### **For Linux** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow tf-keras-vis scikit-learn matplotlib numpy h5py notebook ```

You may need to choose the right library versions, for example tensorflow==2.12.0

To check that Tensorflow was installed: ``` python3 -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Mac** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow-macos==2.12.0 tf-keras-vis scikit-learn matplotlib numpy h5py notebook ``` If you receive an error message such as: ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos Then, try the following command: ``` SYSTEM_VERSION_COMPAT=0 pip3 install tensorflow-macos==2.12.0 scikit-learn==1.2.2 scikeras eli5 pandas matplotlib notebook keras-tuner ``` If you have a Mac with M1 or more recent chip (if you are not sure have a look at "About this Mac"), you can also install the tensorflow-metal library to accelerate training on Mac GPUs (but this is not necessary for the course): ``` pip3 install tensorflow-metal ``` To check that Tensorflow was installed: ``` python3 -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Windows** If you do not have Python installed, you can use either Conda: [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) (see the instructions here: [https://conda.io/projects/conda/en/latest/user-guide/install/windows.html](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)) or Python official installer: [https://www.python.org/downloads/windows/](https://www.python.org/downloads/windows/) We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install tensorflow tf-keras-vis scikit-learn matplotlib numpy h5py notebook ```

You may need to choose the right library versions, for example tensorflow==2.12.0

To check that Tensorflow was installed: ``` python -c "import tensorflow; print(tensorflow.version.VERSION)" ``` There might be a warning message (see above) and the output should be something like "2.12.0". You can terminate the current session: ``` deactivate ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` mlcourse\Scripts\activate.bat jupyter notebook ``` #### **Image Segmentation** This part of the course must be done on the UNIL Jupyter Lab but some instructions on how to install the libraries on your laptop will be given at the end of the course. ### **Curnagl** For the practicals, it will be convenient to be able to copy/paste text from a web page to the terminal on Curnagl. So please make sure you can do it before the course. You also need to make sure that your terminal has a X server. For Mac users, download and install XQuartz (X server): [https://www.xquartz.org/](https://www.xquartz.org/) For Windows users, download and install MobaXterm terminal (which includes a X server). Click on the "Installer edition" button on the following webpage: [https://mobaxterm.mobatek.net/download-home-edition.html](https://mobaxterm.mobatek.net/download-home-edition.html) For Linux users, you do not need to install anything. When testing if TensorFlow was properly installed (see below) you may receive a warning 2022-03-16 12:15:00.564218: W tensorflow/stream\_executor/platform/default/dso\_loader.cc:64\] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD\_LIBRARY\_PATH: /dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/python-3.8.8-tb3aceqq5wzx4kr5m7s5m4kzh4kxi3ex/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tcl-8.6.11-aonlmtcje4sgqf6gc4d56cnp3mbbhvnj/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen2/gcc-9.3.0/tk-8.6.11-2gb36lqwohtzopr52c62hajn4tq7sf6m/lib:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib64:/dcsrsoft/spack/hetre/v1.2/spack/opt/spack/linux-rhel8-zen/gcc-8.3.1/gcc-9.3.0-nwqdwvso3jf3fgygezygmtty6hvydale/lib 2022-03-16 12:15:00.564262: I tensorflow/stream\_executor/cuda/cudart\_stub.cc:29\] Ignore above cudart dlerror if you do not have a GPU set up on your machine. You should not worry. By default, TensorFlow is trying to use GPUs and since there are no GPUs, it writes a warning and decides to use CPUs (which is enough for our course). #### **Image Classification** Here are some instructions for installing Keras with TensorFlow at the backend (for Python3), and other libraries, on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question "do you want to save password ?" Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) You can do the practicals in your /scratch directory or on the course group "cours\_hpc" if you have asked us in advanced: ``` cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc mkdir < my unil username > cd < my unil username > ``` You need to make two directories: ``` mkdir images mkdir models ``` Clone the following git repos: ``` git clone https://c4science.ch/source/CNN_Classification.git ``` Copy the images from CNN\_Classification to images: ``` cp CNN_Classification/*jpeg images ``` You also need to upload all the "models.keras" and "models.npy" files that are included in the "models" directory you have received for this course, and move them to the "models" folder on Curnagl. Let us install libraries from the interactive partition: ``` Sinteractive -m 10G -G 1 module load python/3.10.13 cuda/11.8.0 cudnn/8.7.0.84-11.8 python -m venv mlcourse source mlcourse/bin/activate pip install -r CNN_Classification/requirements.txt ``` To check that TensorFlow was installed: ``` python -c 'import tensorflow; print(tensorflow.version.VERSION)' ``` There might be a warning message (see above) and the output should be something like "2.9.1". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/< my unil username > ``` You can do the practicals on the interactive partition: ``` Sinteractive -m 10G -G 1 module load python/3.10.13 cuda/11.8.0 cudnn/8.7.0.84-11.8 source mlcourse/bin/activate python ``` In the practical code (i.e. the Python code in the html file), you will need to set the paths as follows: platform = "curnagl" PATH\_IMAGES = "./images" PATH\_MODELS = "./models" #### **Image Segmentation** On demand. If you work in a project in which you need to use Curnagl to do segmentations, please contact us. # Course software for Text Analysis with LLMs You can do the practicals on various computing platforms. However, since the participants may use various types of computers and softwares, we recommend to use the UNIL JupyterLab to do the practicals. - [JupyterLab](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-text-analysis-with-llms#bkmrk-jupyterlab): Working on the cloud is convenient because the installation of the Python packages is already done and you will be working with a Jupyter Notebook style. Note, however, that the UNIL JupyterLab will only be active during the course and for one week following its completion, so in the long term you should use either your laptop or Curnagl. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - [Laptop](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-text-analysis-with-llms#bkmrk-laptop): This is good if you want to work directly on your laptop, but you will need to install the required libraries on your laptop. Warning: We will give general instructions on how to install the libraries on your laptop but it is sometimes tricky to find the right library versions and we will not be able to help you with the installation. The installation should take about 15 minutes. - [Curnagl](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/course-software-for-text-analysis-with-llms#bkmrk-curnagl): This is efficient if you are used to work on a cluster or if you intend to use one in the future to work on large projects. If you have an account you can work on your /scratch folder or ask us to be part of the course project but please contact us at least a week before the course. If you do not have an account to access the UNIL cluster Curnagl, please contact us at least a week before the course so that we can give you a temporary account. The installation should take about 15 minutes. Note that it is also possible to use JupyterLab on Curnagl: see [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/jupyterlab-on-the-curnagl-cluster) If you choose to work on the UNIL JupyterLab, then you do not need to prepare anything since all the necessary libraries will already be installed on the UNIL JupyterLab. In all cases, you will have access to the UNIL JupyterLab. Otherwise, if you prefer to work on your laptop or on Curnagl, please make sure you have a working installation before the day of the course as on the day we will be unable to provide any assistance with this. If you have difficulties with the installation on Curnagl we can help you, so please contact us before the course at helpdesk@unil.ch with subject: DCSR ML course. On the other hand, if you are unable to install the libraries on your laptop, we will unfortunately not be able to help you (there are too many particular cases), so you will need to use the UNIL Jupyter Lab during the course. Before the course, we will send you all the files that are needed to do the practicals. ### **JupyterLab** Here are some instructions for using the UNIL JupyterLab to do the practicals. Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. The webpage's link will be given during the course. Enter the login and password corresponding to your UNIL credentials. Fill in the form as shown in the lecture's slides. We have already prepared your workspace, including the data and notebook. Double click on "Transformers\_with\_Hugging\_Face.ipynb" Change the "ipykernel" (top right button "Python 3 ipykernel") to LLM In the notebook, check that platform = "jupyter" To execute a command, click on "Run the selected cells and advance" (the right arrow), or SHIFT + RETURN. When you have finished the practicals, select File / Log out. ### **Laptop** You may need to install development tools including a C and Fortran compiler (e.g. Xcode on Mac, gcc and gfortran on Linux, Visual Studio on Windows). Please decide in which folder (or path) you want to do the practicals, go there and copie the notebook there: ``` cd THE_PATH_WHERE_I_DO_THE_PRACTICALS ``` In the notebook, set platform = "laptop" Here are some instructions for installing PyTorch and other libraries on your laptop. You need Python >= 3.8. ##### **For Linux** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install torch torchvision torchinfo transformers accelerate datasets sentencepiece pandas scikit-learn matplotlib sacremoses notebook ipywidgets gdown wget ```

You may need to choose the right library versions

To check that PyTorch was installed: ``` python3 -c "import torch; print(torch.__version__)" ``` There might be a warning message (see above) and the output should be something like "2.3.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Mac** We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install torch torchvision torchinfo transformers accelerate datasets sentencepiece pandas scikit-learn matplotlib sacremoses notebook ipywidgets gdown wget ```

You may need to choose the right library versions

To check that PyTorch was installed: ``` python3 -c "import torch; print(torch.__version__)" ``` There might be a warning message (see above) and the output should be something like "2.3.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` source mlcourse/bin/activate jupyter notebook ``` ##### **For Windows** If you do not have Python installed, you can use either Conda: [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) (see the instructions here: [https://conda.io/projects/conda/en/latest/user-guide/install/windows.html](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)) or Python official installer: [https://www.python.org/downloads/windows/](https://www.python.org/downloads/windows/) We will use a terminal to install the libraries. Let us create a virtual environment. Open your terminal and type: ``` python3 -m venv mlcourse source mlcourse/bin/activate pip3 install torch torchvision torchinfo transformers accelerate datasets sentencepiece pandas scikit-learn matplotlib sacremoses notebook ipywidgets gdown wget ```

You may need to choose the right library versions

To check that PyTorch was installed: ``` python3 -c "import torch; print(torch.__version__)" ``` There might be a warning message (see above) and the output should be something like "2.3.0". You can terminate the current session: ``` deactivate ``` **TO DO THE PRACTICALS (today or another day):** You can use any Python IDE (e.g. Jupyter Notebook or PyCharm), but you need to launch it after activating the virtual environment. For example, for Jupyter Notebook: ``` mlcourse\Scripts\activate.bat jupyter notebook ``` ### **Curnagl** For the practicals, it will be convenient to be able to copy/paste text from a web page to the terminal on Curnagl. So please make sure you can do it before the course. You also need to make sure that your terminal has a X server. For Mac users, download and install XQuartz (X server): [https://www.xquartz.org/](https://www.xquartz.org/) For Windows users, download and install MobaXterm terminal (which includes a X server). Click on the "Installer edition" button on the following webpage: [https://mobaxterm.mobatek.net/download-home-edition.html](https://mobaxterm.mobatek.net/download-home-edition.html) For Linux users, you do not need to install anything. Here are some instructions for installing PyTorch and other libraries on the UNIL cluster called Curnagl. Open a terminal on your laptop and type (if you are located outside the UNIL you will need to activate the UNIL VPN): ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch ``` Here and in what follows we added the brackets < > to emphasize the username, but you should not write them in the command. Enter your UNIL password. For Windows users with the MobaXterm terminal: Launch MobaXterm, click on Start local terminal and type the command ssh -Y < my unil username >@curnagl.dcsr.unil.ch. Enter your UNIL password. Then you should be on Curnagl. Alternatively, launch MobaXterm, click on the session icon and then click on the SSH icon. Fill in: remote host = curnagl.dcsr.unil.ch, specify username = < my unil username >. Finally, click ok, enter your password. If you have the question "do you want to save password ?" Say No if your are not sure. Then you should be on Curnagl. See also the documentation: [https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster](https://wiki.unil.ch/ci/books/high-performance-computing-hpc/page/ssh-connection-to-dcsr-cluster) You can do the practicals in your /scratch directory or on the course group "cours\_hpc" if you have asked us in advanced: ``` cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc mkdir < my unil username > cd < my unil username > ``` Clone the following git repos: ``` git clone https://git.dcsr.unil.ch/ML-Courses/llm_course.git ``` Let us install libraries from the interactive partition: ``` Sinteractive -m 10G -G 1 module load python/3.11.7 python -m venv mlcourse source mlcourse/bin/activate pip install -r llm_course/requirements_gpu.txt --extra-index-url https://download.pytorch.org/whl/cu128 ``` To check that PyTorch was installed: ``` python3 -c "import torch; print(torch.__version__)" ``` There might be a warning message (see above) and the output should be something like "2.10.0". You can terminate the current session: ``` deactivate exit ``` **TO DO THE PRACTICALS (today or another day):** ``` ssh -Y < my unil username >@curnagl.dcsr.unil.ch cd /scratch/< my unil username > or cd /work/TRAINING/UNIL/CTR/rfabbret/cours_hpc/< my unil username > ``` You can do the practicals on the interactive partition: ``` Sinteractive -m 10G -G 1 module load python/3.11.7 source mlcourse/bin/activate python ``` In the practical code (i.e. the Python code in the html file), you will need to set the paths as follows: platform = "curnagl" During the practicals, if you receive an error message "Disk quota exceeded", you will need to make some space in your home directory. For example, by deleting .cache. # Run MPI with containers ## Simple test Simple container with ucx and openmpi. ```bash Bootstrap: docker From: debian:trixie %environment export LD_LIBRARY_PATH=/usr/local/lib %post apt-get update && apt-get install -y build-essential wget rdma-core libibverbs-dev wget https://github.com/openucx/ucx/releases/download/v1.18.1/ucx-1.18.1.tar.gz tar xzf ucx-1.18.1.tar.gz cd ucx-1.18.1 mkdir build cd build ../configure --prefix=/opt/ make -j4 make install cd .. export OPENMPI_VERSION="4.1.6" export OPENMPI_MAJOR_VERSION="v4.1" export OPENMPI_MAKE_OPTIONS="-j4" mkdir -p /openmpi-src cd /openmpi-src wget https://download.open-mpi.org/release/open-mpi/${OPENMPI_MAJOR_VERSION}/openmpi-${OPENMPI_VERSION}.tar.gz \ && tar xfz openmpi-${OPENMPI_VERSION}.tar.gz cd openmpi-${OPENMPI_VERSION} && ./configure --with-ucx=/opt --without-verbs make all ${OPENMPI_MAKE_OPTIONS} make install cd / rm -rf /openmpi-src ``` To build it: ```bash singularity build -f openmpitest.sif openmpi.def ``` Then we compile an MPI application inside the container. For example [osu-benchmarks](https://mvapich.cse.ohio-state.edu/benchmarks/). ```bash wget https://mvapich.cse.ohio-state.edu/download/mvapich/osu-micro-benchmarks-7.5-1.tar.gz tar -xvf osu-micro-benchmarks-7.5-1.tar.gz ``` ```bash singularity shell openmpitest.sif ``` ```bash cd osu-micro-benchmarks-7.5-1 ./configure CC=/usr/local/bin/mpicc CXX=/usr/local/bin/mpicxx --prefix=/scratch/$USER/osu_install make install ``` Then you can use the following job: ```bash #!/bin/bash #SBATCH -N 2 #SBATCH -n 2 #SBATCH -o mpi-%j.out #SBATCH -e mpi-%j.err module purge module load singularityce module load openmpi export PMIX_MCA_psec=native export PMIX_MCA_gds=^ds12 export SINGULARITY_BINDPATH=/scratch srun --mpi=pmix singularity run openmpitest.sif /scratch/$user/osu_install/libexec/osu-micro-benchmarks/mpi/collective/osu_alltoall ``` ## Some possible errors if the option `--mpi=mpix` is not used, you will have the following error: ```bash [dna067:2560172] OPAL ERROR: Unreachable in file pmix3x_client.c at line 111 -------------------------------------------------------------------------- The application appears to have been direct launched using "srun", but OMPI was not built with SLURM's PMI support and therefore cannot execute. There are several options for building PMI support under SLURM, depending upon the SLURM version you are using: version 16.05 or later: you can use SLURM's PMIx support. This requires that you configure and build SLURM --with-pmix. Versions earlier than 16.05: you must use either SLURM's PMI-1 or PMI-2 support. SLURM builds PMI-1 by default, or you can manually install PMI-2. You must then build Open MPI using --with-pmi pointing to the SLURM PMI library location. Please configure as appropriate and try again. ``` By default OpenMPI 4.x will try to use PMIx client v3. The intialisation does not success bacuase there is no PMIx server initialized. mpirun takes care of initializing an embedded PMIx server. ### Psec error You can also have this error: ```bash A requested component was not found, or was unable to be opened. This means that this component is either not installed or is unable to be used on your system (e.g., sometimes this means that shared libraries that the component requires are unable to be found/loaded). Note that PMIX stopped checking at the first component that it did not find. Host: dna075 Framework: psec Component: munge ``` Here, the application will run. This is related to the PMIX_SECURITY_MODE. When `srun` is executed, it will setup previous variable to: `munge,native`. To verify it: ```bash srun --mpi=pmix env | grep PMIX_SECURITY PMIX_SECURITY_MODE=munge,native ``` Which means that munge protocol will be used for authentication. As the PMIx library on the container (client side) does not have that component, it will failed but it will then use the `native` component. You can read [here](https://pmix.org/standard/RFC/refactor-security-support.html) for more explanations. You have to use `export PMIX_MCA_psec=native` to avoid this message. ### gds error You can also see this error: ```bash [dna075:373342] PMIX ERROR: ERROR in file gds_ds12_lock_pthread.c at line 168 ``` This is an OpenPMIx bug related to the 'Generalized DataStore for storing job-level and other data' component. You can blacklist it by setting: `export PMIX_MCA_gds=^ds12`. > This is fixed in OpenMPI 5 ## Running OpenMPI 5.0 This works, there is no any compatibilty problem with the host version. If you want to test, set the version of OpenMPI to `5.0.7`. Other versions have problems to compile. ## Running a container from dockerhub This section explains how to run a thirdparty container that you cannot edit and it is based on another MPI distribution. Let's take for example the [openfoam container](https://hub.docker.com/r/pawsey/mpich-base), which is based on MPICH. To build the container: ```bash singularity build openfoam.sif docker://quay.io/pawsey/openfoamlibrary:v2312-rocm5.4-gcc ``` This container provides: `mpich 3.4.3` and the `osu benchmarks`. The osu benchmarks will help us to measure the performance of the MPI library and compare it with the native MPI library installed on the cluster. If we try to execute the `osu benchmarks` from the container, we get: ```bash srun -n 2 singularity exec openfoam.sif osu_alltoall This test requires at least two processes This test requires at least two processes srun: error: dna066: tasks 0-1: Exited with exit code 1 ``` This means that the binary is not able to initialize the MPI layer and just one instance is launched. Let's try to launch it with `PMIx`: ```bash srun --mpi=pmix -n 2 singularity exec openfoam.sif osu_alltoall This test requires at least two processes This test requires at least two processes srun: error: dna066: tasks 0-1: Exited with exit code 1 ``` We get the same error, probably because MPICH does not support PMIx protocol. Let's try with pmi2: ``` srun --mpi=pmi2 -n 2 singularity exec openfoam.sif osu_alltoall # OSU MPI All-to-All Personalized Exchange Latency Test v7.3 # Datatype: MPI_CHAR. # Size Avg Latency(us) 1 2.52 2 2.44 4 2.56 8 2.45 16 2.45 32 2.52 ``` This works but it has some overhead compare to the MPI installed natively. The other problem is that in a multinode configuration the overhead is high: ```bash #!/bin/bash #SBATCH -N 2 #SBATCH -n 2 module load singularityce srun --mpi=pmi2 -n 2 singularity exec openfoam.sif osu_alltoall ``` ```bash # OSU MPI All-to-All Personalized Exchange Latency Test v7.3 # Datatype: MPI_CHAR. # Size Avg Latency(us) 1 48.89 2 44.16 4 44.73 8 49.08 16 50.07 32 48.33 ``` ### Using wi4mpi Wi4mpi is a tool that allows us to translate calls between different MPI implementations. The idea here is to be able to use the OpenMPI installed natively on the cluster. The following job slurm is used: ```bash #!/bin/bash #SBATCH -N 1 #SBATCH -n 2 module load singularityce module load openmpi wi4mpi export SINGULARITY_BINDPATH=/dcsrsoft export WI4MPI_FROM=MPICH export WI4MPI_TO=OMPI export WI4MPI_RUN_MPI_C_LIB=${OPENMPI_ROOT}/lib/libmpi.so export WI4MPI_RUN_MPI_F_LIB=${OPENMPI_ROOT}/lib/libmpi_mpifh.so export WI4MPI_RUN_MPIIO_C_LIB=${WI4MPI_RUN_MPI_C_LIB} export WI4MPI_RUN_MPIIO_F_LIB=${WI4MPI_RUN_MPI_F_LIB} export SINGULARITYENV_LD_PRELOAD=${WI4MPI_ROOT}/libexec/wi4mpi/libwi4mpi_${WI4MPI_FROM}_${WI4MPI_TO}.so:${WI4MPI_RUN_MPI_C_LIB} srun --mpi=pmix -n 2 singularity exec openfoam.sif osu_alltoall ``` Result: ```bash You are using Wi4MPI-3.6.4 with the mode preload From MPICH To OMPI # OSU MPI All-to-All Personalized Exchange Latency Test v7.3 # Datatype: MPI_CHAR. # Size Avg Latency(us) 1 0.77 2 1.00 4 0.81 8 0.93 16 0.98 ``` First, you can notice that now it works with `PMIx` and that the peformance is much better than before. We have still some errors/warnings: ```bash A requested component was not found, or was unable to be opened. This means that this component is either not installed or is unable to be used on your system (e.g., sometimes this means that shared libraries that the component requires are unable to be found/loaded). Note that PMIx stopped checking at the first component that it did not find. Host: dna065 Framework: psec Component: munge -------------------------------------------------------------------------- [1752250146.039806] [dna065:2110525:0] ucp_context.c:1177 UCX WARN network device 'mlx5_2:1' is not available, please use one or more of: 'ens1f1'(tcp), 'ib0'(tcp), ' lo'(tcp) [1752250146.049910] [dna065:2110525:0] ucp_context.c:1177 UCX WARN network device 'mlx5_2:1' is not available, please use one or more of: 'ens1f1'(tcp), 'ib0'(tcp), ' lo'(tcp) [1752250146.039795] [dna065:2110526:0] ucp_context.c:1177 UCX WARN network device 'mlx5_2:1' is not available, please use one or more of: 'ens1f1'(tcp), 'ib0'(tcp), ' : ``` As we have seen before, the first error can be fixed using the following variable: ```bash export PMIX_MCA_psec=native ``` The second error is related with the infiniband dectection. The UCX libray is linked to some libraries that are not available in the container. We can try to mount those libraries on the container: ```bash export SINGULARITY_BINDPATH=/dcsrsoft,/lib64/libibverbs.so.1:/lib/x86_64-linux-gnu/libibverbs.so.1,/lib64/libmlx5.so.1:/lib/x86_6 4-linux-gnu/libmlx5.so.1,/lib64/librdmacm.so.1:/lib/x86_64-linux-gnu/librdmacm.so.1,/lib64/libnl-route-3.so.200:/lib/x86_64-linux-gnu/libnl-route-3.so.200 ``` If we try again, we should see this: ```bash [dna066:1443708] mca_base_component_repository_open: unable to open mca_pmix_s1: libpmi.so.0: cannot open shared object file: No such file or directory (ignored) [dna066:1443711] mca_base_component_repository_open: unable to open mca_pmix_s1: libpmi.so.0: cannot open shared object file: No such file or directory (ignored) [dna066:1443708] mca_base_component_repository_open: unable to open mca_pmix_s2: libpmi2.so.0: cannot open shared object file: No such file or directory (ignored) [dna066:1443711] mca_base_component_repository_open: unable to open mca_pmix_s2: libpmi2.so.0: cannot open shared object file: No such file or directory (ignored) You are using Wi4MPI-3.6.4 with the mode preload From MPICH To OMPI # OSU MPI All-to-All Personalized Exchange Latency Test v7.3 # Datatype: MPI_CHAR. # Size Avg Latency(us) 1 0.67 2 0.67 4 0.66 8 0.68 16 0.66 32 0.74 64 0.76 128 1.05 256 1.03 ``` The peformance was improved. If we try multinode now: ```bash # OSU MPI All-to-All Personalized Exchange Latency Test v7.3 # Datatype: MPI_CHAR. # Size Avg Latency(us) 1 1.64 2 2.15 4 1.63 8 1.61 16 1.71 32 1.70 64 2.25 128 2.14 ``` We have around 40x of improvement. # Measuring job's CO2 footprint There are three main ways in which the use of the HPC clusters can be more taxing for the environment than it needs to be: 1) by using more of the cluster RAM (Random Allocated Memory) than needed for your calculations (i.e., the "job" you submit to the cluster), 2) by having your submitted jobs crash 3) by requesting more cores (i.e., computing units) for a job than needed. These all imply waste of energy. To help minimize them, the GreenAlgorithms4HPC package was installed on the clusters. It can estimate the carbon output and energy consumption of the user, either for a particular job run on the clusters, or over a time period that you specify. In addition, it can also measure how much memory is being used for the jobs, compared to how much is actually required to run the job. ## Green Algorithms The methodolgy is based on [Green Algorithms](https://www.green-algorithms.org/) developed by Loïc Lannelongue. He developed the package [GreenAlgorithms4HPC](https://github.com/GreenAlgorithms/GreenAlgorithms4HPC) which is a plugin to process the accounting information of a cluster HPC in order to provide an estimation of CO2 footprint. ## How to use it You need to load the following module: ```bash module load ga4hpc ``` And then you can check your CO2 footprint for a period of time: ```bash green_hpc -S 2025-11-24 -E 2025-11-25 ``` The following output is generated: ```bash ################################# # # # Carbon footprint on curnagl # # - user: cruiz1 - # # (2025-11-24 / 2025-11-25) # ################################# -------------- | 51 gCO2e | -------------- ...This is equivalent to: - 0.055 tree-months - driving 0.29 km - 0.0 flights between Paris and London ``` You can also get an estimation for a particular job: ```bash green_hpc -S 2025-11-24 -E 2025-11-25 --filterJobIDs 41694290 ``` > In oder to have information about jobs running in the same day, you should put the day after in the `-E` parameter. There are several options to filter jobs that you can check with: ```bash green_hpc -h ``` ## How precise is the estimation? The power usage is based on the TDP (Termal Desing Power) information provided by the manufacturer. This value is a limit of the power comsomption a CPU, GPU could have. The power consumption is estimated as follows: Power consumption = time * (resources 1 * TDP + resources_2* TPD + ...) ## Assumpions and limitations * Resources are assumed to be used at a 100%. This may lead to slightly overestimated carbon footprints, although the order of magnitude is probably correct. * Conversely, the wasted energy due to memory overallocation may be largely underestimated, as the information needed is not always logged. * Only the carbon imprint of cluster use is measured, not the impact of cooling the computers down, or of building the facilities. The estimation does not take into account neither the CO2 produced during manufacturing. ### Results of some tests: |config| appli| GA mesured | real | |--------|---- |--------|------| | cpu 48 cores| cpu benchmark NAS |0.343| 0.3017| | 2 gpu A100 | julia heat equation | 0.355 |0.350| |2 gpu A100 | LLM inference | 0.376 | 0.234|