From 88b4b752915e0ceebc95a70f1978bb3b26819796 Mon Sep 17 00:00:00 2001 From: Carlos Alarcon Date: Mon, 29 Apr 2024 10:11:32 -0400 Subject: [PATCH 1/2] jupyter+conda+pytorch, no singularity --- doc/web/index.html | 693 ++++++++++++++++++++++++++------------------- 1 file changed, 396 insertions(+), 297 deletions(-) diff --git a/doc/web/index.html b/doc/web/index.html index 99fe53e..d3c469e 100644 --- a/doc/web/index.html +++ b/doc/web/index.html @@ -67,54 +67,55 @@

Contents


 2.8 Interactive Jobs
  2.8.1 Command Line
  2.8.2 Graphical Applications -
  2.8.3 Jupyter Notebooks -
  2.8.4 VScode -
 2.9 Scheduler Environment Variables -
 2.10 SSH Keys For MPI -
 2.11 Creating Virtual Environments -
  2.11.1 Anaconda -
  2.11.2 Python -
 2.12 Example Job Script: Fluent -
 2.13 Example Job: efficientdet -
 2.14 Java Jobs -
 2.15 Scheduling On The GPU Nodes -
  2.15.1 P6 on Multi-GPU, Multi-Node -
  2.15.2 CUDA -
  2.15.3 Special Notes for sending CUDA jobs to the GPU Queue -
  2.15.4 OpenISS Examples -
 2.16 Singularity Containers -
3 Conclusion -
 3.1 Important Limitations -
 3.2 Tips/Tricks -
 3.3 Use Cases -
A History -
 A.1 Acknowledgments -
 A.2 Migration from UGE to SLURM -
 A.3 Phases -
  A.3.1 Phase 4 -
  A.3.2 Phase 3 -
  A.3.3 Phase 2 -
  A.3.4 Phase 1 -
B Frequently Asked Questions -
 B.1 Where do I learn about Linux? -
 B.2 How to use the “bash shell” on Speed? -
  B.2.1 How do I set bash as my login shell? -
  B.2.2 How do I move into a bash shell on Speed? -
  B.2.3 How do I use the bash shell in an interactive session on Speed? -
  B.2.4 How do I run scripts written in bash on Speed? -
 B.3 How to resolve “Disk quota exceeded” errors? -
  B.3.1 Probable Cause -
  B.3.2 Possible Solutions -
  B.3.3 Example of setting working directories for COMSOL -
  B.3.4 Example of setting working directories for Python Modules -
 B.4 How do I check my job’s status? -
 B.5 Why is my job pending when nodes are empty? -
  B.5.1 Disabled nodes - - - -
  B.5.2 Error in job submit request. -
C Sister Facilities +
  2.8.3 Jupyter Notebooks in Singularity +
  2.8.4 Jupyter Labs in Conda and Pytorch +
  2.8.5 VScode +
 2.9 Scheduler Environment Variables +
 2.10 SSH Keys For MPI +
 2.11 Creating Virtual Environments +
  2.11.1 Anaconda +
  2.11.2 Python +
 2.12 Example Job Script: Fluent +
 2.13 Example Job: efficientdet +
 2.14 Java Jobs +
 2.15 Scheduling On The GPU Nodes +
  2.15.1 P6 on Multi-GPU, Multi-Node +
  2.15.2 CUDA +
  2.15.3 Special Notes for sending CUDA jobs to the GPU Queue +
  2.15.4 OpenISS Examples +
 2.16 Singularity Containers +
3 Conclusion +
 3.1 Important Limitations +
 3.2 Tips/Tricks +
 3.3 Use Cases +
A History +
 A.1 Acknowledgments +
 A.2 Migration from UGE to SLURM +
 A.3 Phases +
  A.3.1 Phase 4 +
  A.3.2 Phase 3 +
  A.3.3 Phase 2 +
  A.3.4 Phase 1 +
B Frequently Asked Questions +
 B.1 Where do I learn about Linux? +
 B.2 How to use the “bash shell” on Speed? +
  B.2.1 How do I set bash as my login shell? +
  B.2.2 How do I move into a bash shell on Speed? +
  B.2.3 How do I use the bash shell in an interactive session on Speed? +
  B.2.4 How do I run scripts written in bash on Speed? +
 B.3 How to resolve “Disk quota exceeded” errors? +
  B.3.1 Probable Cause +
  B.3.2 Possible Solutions +
  B.3.3 Example of setting working directories for COMSOL +
  B.3.4 Example of setting working directories for Python Modules +
 B.4 How do I check my job’s status? +
 B.5 Why is my job pending when nodes are empty? + + + +
  B.5.1 Disabled nodes +
  B.5.2 Error in job submit request. +
C Sister Facilities
Annotated Bibliography @@ -313,7 +314,7 @@

1.6

1.7 Requesting Access

After reviewing the “What Speed is” (Section 1.4) and “What Speed is Not” (Section 1.5), request -access to the “Speed” cluster by emailing: rt-ex-hpc AT encs.concordia.ca. CGS ENCS +access to the “Speed” cluster by emailing: rt-ex-hpc AT encs.concordia.ca. GCS ENCS faculty and staff may request access directly. Students must include the following in their message:

@@ -327,7 +328,7 @@

1.7

Non-GCS faculty / students need to get a “sponsor” within GCS, such that your guest GCS ENCS account is created first. A sponsor can be any GCS Faculty member you collaborate with. Failing that, request the approval from our Dean’s Office; via our Associate Deans Drs. Eddie Hoi Ng or -Emad Shihab. External entities to Concordia who collaborate with CGS Concordia researchers, should +Emad Shihab. External entities to Concordia who collaborate with GCS Concordia researchers, should also go through the Dean’s office for approvals. Non-GCS students taking a GCS course do have their GCS ENCS account created automatically, but still need the course instructor’s approval to use the service. @@ -780,12 +781,48 @@

See man sacct or sacct -e for details of the available formatting options. You can define your preferred default format in the SACCT_FORMAT environment variable in your .cshrc or .bashrc files. +

+
  • +

    seff [job-ID]: reports on the efficiency of a job’s cpu and memory utilization. Don’t execute it + on RUNNING jobs (only on completed/finished jobs), efficiency statistics may be + misleading. +

    If you define the following directives in your batch script, you will receive seff output in your + email when your job is finished. + + + +

    + 
    +     #SBATCH --mail-type=ALL
+     #SBATCH --mail-user=USER_NAME@encs.concordia.ca
+     ## Replace USER_NAME with your encs username.
+
    +

    +

    Output example: + + + +

    + 
    +     Job ID: XXXXX
+     Cluster: speed
+     User/Group: user1/user1
+     State: COMPLETED (exit code 0)
+     Nodes: 1
+     Cores per node: 4
+     CPU Utilized: 00:04:29
+     CPU Efficiency: 0.35% of 21:32:20 core-walltime
+     Job Wall-clock time: 05:23:05
+     Memory Utilized: 2.90 GB
+     Memory Efficiency: 2.90% of 100.00 GB
+
    +

    • -

    +

    2.5 Advanced sbatch Options

    -

    In addition to the basic sbatch options presented earlier, there are a few additional options that are +

    In addition to the basic sbatch options presented earlier, there are a few additional options that are generally useful:

      @@ -797,12 +834,12 @@

    • --mail-user email@domain.com: requests that the scheduler use this e-mail notification address, rather than the default (see, --mail-type). - - -
    • --export=[ALL | NONE | variables]: exports environment variable(s) that can be used by the script. + + +
    • -t [min] or DAYS-HH:MM:SS: sets a job runtime of min or HH:MM:SS. Note that if you give a single number, that represents minutes, not hours. @@ -810,35 +847,35 @@

    • --depend=[state:job-ID]: run this job only when job [job-ID] finishes. Held jobs appear in the queue.

    -

    The many sbatch options available are read with, man sbatch. Also note that sbatch options can +

    The many sbatch options available are read with, man sbatch. Also note that sbatch options can be specified during the job-submission command, and these override existing script options (if present). The syntax is, sbatch [options] PATHTOSCRIPT, but unlike in the script, the options are specified without the leading #SBATCH (e.g., sbatch -J sub-test --chdir=./ --mem=1G ./tcsh.sh). -

    +

    2.6 Array Jobs

    -

    Array jobs are those that start a batch job or a parallel job multiple times. Each iteration of the job +

    Array jobs are those that start a batch job or a parallel job multiple times. Each iteration of the job array is called a task and receives a unique job ID. Only supported for batch jobs; submit time \(< 1\) second, compared to repeatedly submitting the same regular job over and over even from a script. -

    To submit an array job, use the --array option of the sbatch command as follows: +

    To submit an array job, use the --array option of the sbatch command as follows:

    - 
    +   
     sbatch --array=n-m[:s]] <batch_script>
 
    
-
    
-
       -t Option Syntax: 
    
+
    
+
       -t Option Syntax: 
    
      
      
      
    • n: indicates the start-id.
      
      • 
      
    • m: indicates the max-id.
      
      • 
      
    • s: indicates the step size.
    
-
       Examples: 
    
+
       Examples: 
    
      
      
      
    • sbatch --array=1-50000 -N1 -i my_in_%a -o my_out_%a array.sh:  submits  a  job
      with 50000 elements, %a maps to the task-id between 1 and 50K.
@@ -850,56 +887,56 @@ 
      2.6   
      
      • 
      
    • sbatch --array=3-15:3 array.sh: submits a jobs with 5 tasks numbered consecutively
      with step size 3 (task-ids 3,6,9,12,15).
    
-
       Output files for Array Jobs:
-
       The default and output and error-files are slurm-job_id_task_id.out. This means that Speed
+
       Output files for Array Jobs:
+
       The default and output and error-files are slurm-job_id_task_id.out. This means that Speed
 creates an output and an error-file for each task generated by the array-job as well as
 one for the super-ordinate array-job. To alter this behavior use the -o and -e option of
 sbatch.
-
       For more details about Array Job options, please review the manual pages for sbatch by executing
+
       For more details about Array Job options, please review the manual pages for sbatch by executing
 the following at the command line on speed-submit man sbatch.
                                                                                
 
                                                                                
-
    
+
    
 
    
    
    2.7    Requesting Multiple Cores (i.e., Multithreading Jobs)
    
-
    For jobs that can take advantage of multiple machine cores, up to 32 cores (per job) can be requested
+
    For jobs that can take advantage of multiple machine cores, up to 32 cores (per job) can be requested
 in your script with:
                                                                                
 
                                                                                
 
    
-   
    +   
     #SBATCH -n [#cores for processes]
 
    
-
    
-
       or
+
    
+
       or
                                                                                
 
                                                                                
 
    
-   
    +   
     #SBATCH -n 1
 #SBATCH -c [#cores for threads of a single process]
 
    
-
    
-
       Both sbatch and salloc support -n on the command line, and it should always be used either in
+
    
+
       Both sbatch and salloc support -n on the command line, and it should always be used either in
 the script or on the command line as the default \(n=1\). Do not request more cores than you think
 will be useful, as larger-core jobs are more difficult to schedule. On the flip side, though, if you are
 going to be running a program that scales out to the maximum single-machine core count available,
 please (please) request 32 cores, to avoid node oversubscription (i.e., to avoid overloading the
 CPUs).
-
       Important note about --ntasks or --ntasks-per-node (-n) talks about processes (usually the
+
       Important note about --ntasks or --ntasks-per-node (-n) talks about processes (usually the
 ones ran with srun). --cpus-per-task (-c) corresponds to threads per process. Some programs
 consider them equivalent, some don’t. Fluent for example uses --ntasks-per-node=8 and
 --cpus-per-task=1, some just set --cpus-per-task=8 and --ntasks-per-node=1. If one of them is
 not \(1\) then some applications need to be told to use \(n*c\) total cores.
-
       Core count associated with a job appears under, “AllocCPUS”, in the, qacct -j, output.
+
       Core count associated with a job appears under, “AllocCPUS”, in the, qacct -j, output.
                                                                                
 
                                                                                
 
    
-   
    +   
     [serguei@speed-submit src] % squeue -l
 Thu Oct 19 20:32:32 2023
 JOBID PARTITION     NAME     USER    STATE       TIME TIME_LIMI  NODES NODELIST(REASON)
@@ -919,58 +956,58 @@ 
    
-
    
-
    
+
    
+
    
 
    
    
    2.8    Interactive Jobs
    
-
    Job sessions can be interactive, instead of batch (script) based. Such sessions can be useful for testing,
+
    Job sessions can be interactive, instead of batch (script) based. Such sessions can be useful for testing,
 debugging, and optimising code and resource requirements, conda or python virtual environments
 setup, or any likewise preparatory work prior to batch submission.
-
    
+
    
 
    
    
    2.8.1    Command Line
    
-
    To request an interactive job session, use, salloc [options], similarly to a sbatch command-line
+
    To request an interactive job session, use, salloc [options], similarly to a sbatch command-line
 job, e.g.,
                                                                                
 
                                                                                
 
    
-   
    +   
     salloc -J interactive-test --mem=1G -p ps -n 8
 
    
-
     Inside the allocated salloc session you can run shell commands as usual; it is recommended to use
+
     Inside the allocated salloc session you can run shell commands as usual; it is recommended to use
 srun for the heavy compute steps inside salloc. If it is a quick a short job just to compile something,
 e.g., on a GPU node you can use an interactive srun directly (note no srun can run within srun),
 e.g., a 1 hour allocation:
-
       For tcsh:
+
       For tcsh:
                                                                                
 
                                                                                
 
    
-   
    +   
     srun --pty -n 8 -p pg --gpus=1 --mem=1Gb -t 60 /encs/bin/tcsh
 
    
-
    
-
       For bash:
+
    
+
       For bash:
                                                                                
 
                                                                                
 
    
-   
    +   
     srun --pty -n 8 -p pg --gpus=1 --mem=1Gb -t 60 /encs/bin/bash
 
    
-
    
-
    
+
    
+
    
 
    
    
    2.8.2    Graphical Applications
    
-
    If you need to run an on-Speed graphical-based UI application (e.g., MALTLAB, Abaqus CME, etc.),
+
    If you need to run an on-Speed graphical-based UI application (e.g., MALTLAB, Abaqus CME, etc.),
 or an IDE (PyCharm, VSCode, Eclipse) to develop and test your job’s code interactively you need to
 enable X11-forwarding from your client machine to speed then to the compute node. To do
 so:
-
    
+
    
      
      
 
    1. 
-     
      you need to run an X server on your client machine, such as, 
      
+     
      you need to run an X server on your client machine, such as, 
      
          
        
          
      • on  Windows:  MobaXterm  with  X  turned  on,  or  Xming  +  PuTTY  with  X11
          forwarding, or XOrg under Cygwin
@@ -978,17 +1015,17 @@ 
        on macOS: XQuarz – use its xterm and ssh -X
          
        • 
          
      • on Linux just use ssh -X speed.encs.concordia.ca
      
-     
      See https://www.concordia.ca/ginacody/aits/support/faq/xserver.html for
+     
      See https://www.concordia.ca/ginacody/aits/support/faq/xserver.html for
      details.
      
      2. 
 
    2. 
-     
      verify your X connection was properly forwarded by printing the DISPLAY variable:
-     
      echo $DISPLAY If it has no output, then your X forwarding is not on and you may need to
+     
      verify your X connection was properly forwarded by printing the DISPLAY variable:
+     
      echo $DISPLAY If it has no output, then your X forwarding is not on and you may need to
      re-login to Speed.
      
      3. 
 
    3. 
-     
      Use the --x11 with salloc or srun:
-     
      salloc ... --x11=first ...
+     
      Use the --x11 with salloc or srun:
+     
      salloc ... --x11=first ...
                                                                                
 
                                                                                
@@ -996,30 +1033,30 @@ 
      Once landed on a compute node, verify DISPLAY again.
      
      4. 
 
    4. 
-     
      While running under scheduler, create a run-user directory and set the variable
+     
      While running under scheduler, create a run-user directory and set the variable
      XDG_RUNTIME_DIR.
                                                                                
 
                                                                                
 
      
-     
      +     
            mkdir -p /speed-scratch/$USER/run-dir
      setenv XDG_RUNTIME_DIR /speed-scratch/$USER/run-dir
 
      
-     
      
+     
      
      
      5. 
 
    5. 
-     
      Launch your graphical application:
-     
      module load the required version, then matlab, or abaqus cme, etc.
    
-
       Here’s an example of starting PyCharm (see Figure 4), of which we made a sample local
+     
    Launch your graphical application:
+     
    module load the required version, then matlab, or abaqus cme, etc.
    
+
       Here’s an example of starting PyCharm (see Figure 4), of which we made a sample local
 installation. You can make a similar install under your own directory. If using VSCode, it’s currently
 only supported with the --no-sandbox option. 
    
-
       BASH version:
+
       BASH version:
                                                                                
 
                                                                                
 
    
-   
    +   
     bash-3.2$ ssh -X speed (XQuartz xterm, PuTTY or MobaXterm have X11 forwarding too)
 serguei@speed’s password:
 [serguei@speed-submit ~] % echo $DISPLAY
@@ -1032,13 +1069,13 @@ 
    
-
       TCSH version:
+
    
+
       TCSH version:
                                                                                
 
                                                                                
 
    
-   
    +   
     ssh -X speed (XQuartz xterm, PuTTY or MobaXterm have X11 forwarding too)
 [speed-submit] [/home/c/carlos] > echo $DISPLAY
 localhost:14.0
@@ -1053,7 +1090,7 @@ 
    
+
    
 
    
    
     
 
@@ -1064,62 +1101,62 @@ 
    PIC
+
    PIC
 
    
 
    Figure 4: PyCharm Starting up on a Speed Node
    
                                                                                
 
                                                                                
    
    
-   
    2.8.3    Jupyter Notebooks
    
-
    This is an example of running Jupyter notebooks together with Singularity (more on Singularity see
+   
    2.8.3    Jupyter Notebooks in Singularity
    
+
    This is an example of running Jupyter notebooks together with Singularity (more on Singularity see
 Section 2.16). Here we are using one of the OpenISS-derived containers (see Section 2.15.4 as
 well).
-
    
+
    
      
      
 
    1. Use the --x11 with salloc or srun as described in the above example
      
      2. 
 
    2. Load Singularity module module load singularity/3.10.4/default
      
      3. 
 
    3. 
-     
      Execute this Singularity command on a single line. It’s best to save it in a shell script that you
+     
      Execute this Singularity command on a single line. It’s best to save it in a shell script that you
      could call, since it’s long.
                                                                                
 
                                                                                
 
      
-     
      +     
            srun singularity exec -B $PWD\:/speed-pwd,/speed-scratch/$USER\:/my-speed-scratch,/nettemp \
       --env SHELL=/bin/bash --nv /speed-scratch/nag-public/openiss-cuda-conda-jupyter.sif \
       /bin/bash -c ’/opt/conda/bin/jupyter notebook --no-browser --notebook-dir=/speed-pwd \
       --ip="*" --port=8888 --allow-root’
 
      
-     
      
+     
      
      
      4. 
 
    4. 
-     
      Create an ssh tunnel between your computer and the node (speed-XX) where Jupyter is
+     
      Create an ssh tunnel between your computer and the node (speed-XX) where Jupyter is
      running (Using speed-submit as a “jump server”) (Preferably: PuTTY, see Figure 5 and
      Figure 6)
                                                                                
 
                                                                                
 
      
-     
      +     
            ssh -L 8888:speed-XX:8888 YOUR_USER@speed-submit.encs.concordia.ca
 
      
-     
       Don’t close the tunnel.
+     
       Don’t close the tunnel.
      
      5. 
 
    5. 
-     
      Open a browser, and copy your Jupyter’s token, in the screenshot example in Figure 7; each
+     
      Open a browser, and copy your Jupyter’s token, in the screenshot example in Figure 7; each
      time the token will be different, as it printed to you in the terminal.
                                                                                
 
                                                                                
 
      
-     
      +     
            http://localhost:8888/?token=5a52e6c0c7dfc111008a803e5303371ed0462d3d547ac3fb
 
      
-     
      
+     
      
      
      6. 
 
    6. Work with your notebook.
 
    
@@ -1168,64 +1205,126 @@ 
    2.8
 
                                                                                
    
-   
    2.8.4    VScode
    
-
    This is an example of running VScode, it’s similar to Jupyter notebooks, but it doesn’t use containers.
-This a Web version, it exists the local(workstation)-remote(speed-node) version too, but it is for
-Advanced users (no support, execute it at your own risk).
+   
    2.8.4    Jupyter Labs in Conda and Pytorch
    
+
    This is an example of Jupyter Labs running in a Conda environment, with Pytorch
 
    
      
      
      
    • 
-     
      Environment preparation: for the FIRST time:
+     
      Environment preparation: for the FIRST time:
          
        
 
      1. Go to your speed-scratch directory: cd /speed-scratch/$USER
          
        2. 
-
      2. Create a vscode directory: mkdir vscode
+
      3. Create a Jupyter (name of your choice) directory: mkdir -p Jupyter
          
        4. 
-
      4. Go to vscode: cd vscode
+
      5. Go to Jupyter: cd Jupyter
          
        6. 
-
      6. Create home and projects: mkdir {home,projects}
+
      7. Open an Interactive session: salloc --mem=50G --gpus=1 -ppg (or -ppt)
          
        8. 
-
      8. Create this directory: mkdir -p /speed-scratch/$USER/run-user
      
+
    • 
+         
      Set env. variables, conda environment, jupyter+pytorch installation
+                                                                               
+
+                                                                               
+
      
+         
      +         module load anaconda3/2023.03/default
+         setenv TMPDIR /speed-scratch/$USER/tmp
+         setenv TMP /speed-scratch/$USER/tmp
+         setenv CONDA_PKGS_DIRS /speed-scratch/$USER/Jupyter/pkgs
+         conda create -p /speed-scratch/$USER/Jupyter/jupyter-env
+         conda activate /speed-scratch/$USER/Jupyter/jupyter-env
+         conda install -c conda-forge jupyterlab
+         pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+         exit
+
      
+         
      • 
      
      
    • 
-     
      Running VScode
+     
      Running Jupyter Labs, from speed-submit:
+         
        
+
      1. 
+         
        Open an Interactive session: salloc --mem=50G --gpus=1 -ppg (or -ppt)
+                                                                               
+
+                                                                               
+
        
+         
        +         cd /speed-scratch/$USER/Jupyter
+         module load anaconda3/2023.03/default
+         setenv TMPDIR /speed-scratch/$USER/tmp
+         setenv TMP /speed-scratch/$USER/tmp
+         setenv CONDA_PKGS_DIRS /speed-scratch/$USER/Jupyter/pkgs
+         conda activate /speed-scratch/$USER/Jupyter/jupyter-env
+         jupyter lab --no-browser --notebook-dir=$PWD --ip="*" --port=8888 --port-retries=50
+
        
+         
        
+         
        2. 
+
      2. Verify which port the system has assigned to Jupyter: http://localhost:XXXX/lab?token=
+         
        3. 
+
      3. SSH Tunnel creation: similar to Jupyter in Singularity, see Section 2.8.3
+         
        4. 
+
      4. Open a browser and type: localhost:XXXX (port assigned)
      
+     
    
+
    
+
    
+   
    2.8.5    VScode
    
+
    This is an example of running VScode, it’s similar to Jupyter notebooks, but it doesn’t use containers.
+This a Web version, it exists the local(workstation)-remote(speed-node) version too, but it is for
+Advanced users (no support, execute it at your own risk).
+
    
+     
      
+     
    • 
+     
      Environment preparation: for the FIRST time:
          
        
-
      1. Go to your vscode directory: cd /speed-scratch/$USER/vscode
+
      2. Go to your speed-scratch directory: cd /speed-scratch/$USER
          
        3. 
-
      3. Open interactive session: salloc --mem=10Gb --constraint=el9
+
      4. Create a vscode directory: mkdir vscode
                                                                                
 
                                                                                
          
        5. 
-
      5. Set              environment              variable:               setenv XDG_RUNTIME_DIR
+
      6. Go to vscode: cd vscode
+         
        7. 
+
      7. Create home and projects: mkdir {home,projects}
+         
        8. 
+
      8. Create this directory: mkdir -p /speed-scratch/$USER/run-user
      
+     
      • 
+     
    • 
+     
      Running VScode
+         
        
+
      1. Go to your vscode directory: cd /speed-scratch/$USER/vscode
+         
        2. 
+
      2. Open interactive session: salloc --mem=10Gb --constraint=el9
+         
        3. 
+
      3. Set              environment              variable:               setenv XDG_RUNTIME_DIR
          /speed-scratch/$USER/run-user
          
        4. 
-
      4. 
-         
        Run VScode, change the port if needed.
+
      5. 
+         
        Run VScode, change the port if needed.
                                                                                
 
                                                                                
 
        
-         
        +         
                  /speed-scratch/nag-public/code-server-4.22.1/bin/code-server --user-data-dir=$PWD\/projects \
          --config=$PWD\/home/.config/code-server/config.yaml --bind-addr="0.0.0.0:8080" $PWD\/projects
 
        
-         
        
+         
        
          
        6. 
-
      6. Tunnel ssh creation: similar to Jupyter, see Section 2.8.3
+
      7. SSH Tunnel creation: similar to Jupyter, see Section 2.8.3
          
        8. 
-
      8. Open a browser and type: localhost:8080
+
      9. Open a browser and type: localhost:8080
          
        10. 
-
      10. 
-         
        If the browser asks for password:
+
      11. 
+         
        If the browser asks for password:
                                                                                
 
                                                                                
 
        
-         
        +         
                  cat /speed-scratch/$USER/vscode/home/.config/code-server/config.yaml
 
        
-         
        
+         
        
 
        
          
      
      
    
@@ -1234,18 +1333,18 @@ 
    2.8.4   
+
                                                                                
 
                                                                                
 
    PIC
    
-
    Figure 8: VScode running on a Speed node
    
+
    Figure 8: VScode running on a Speed node
    
                                                                                
 
                                                                                
    
-   
    2.9    Scheduler Environment Variables
    
-
    The scheduler presents a number of environment variables that can be used in your jobs. You can
+   
    2.9    Scheduler Environment Variables
    
+
    The scheduler presents a number of environment variables that can be used in your jobs. You can
 invoke env or printenv in your job to know what hose are (most begin with the prefix SLURM). Some
 of the more useful ones are:
 
    
@@ -1265,48 +1364,48 @@ 
    $SLURM_ARRAY_TASK_ID=for array jobs (see Section 2.6).
      
      
  • 
-     
    See a more complete list here:
+     
    See a more complete list here:
 
    
          
      
    • 
-
    In Figure 9 is a sample script, using some of these.
+
    In Figure 9 is a sample script, using some of these.
 
    
    
     
 
                                                                                
 
                                                                                
-
+
                                                                                
 
                                                                                
-
    #!/encs/bin/tcsh 
- 
-#SBATCH --job-name=tmpdir      ## Give the job a name 
-#SBATCH --mail-type=ALL        ## Receive all email type notifications 
-#SBATCH --mail-user=YOUR_USER_NAME@encs.concordia.ca 
-#SBATCH --chdir=./             ## Use currect directory as working directory 
-#SBATCH --nodes=1 
-#SBATCH --ntasks=1 
-#SBATCH --cpus-per-task=8      ## Request 8 cores 
-#SBATCH --mem=32G              ## Assign 32G memory per node 
- 
-cd $TMPDIR 
-mkdir input 
-rsync -av $SLURM_SUBMIT_DIR/references/ input/ 
-mkdir results 
-srun STAR --inFiles $TMPDIR/input --parallel $SRUN_CPUS_PER_TASK --outFiles $TMPDIR/results 
-rsync -av $TMPDIR/results/ $SLURM_SUBMIT_DIR/processed/
+
    #!/encs/bin/tcsh 
+ 
+#SBATCH --job-name=tmpdir      ## Give the job a name 
+#SBATCH --mail-type=ALL        ## Receive all email type notifications 
+#SBATCH --mail-user=YOUR_USER_NAME@encs.concordia.ca 
+#SBATCH --chdir=./             ## Use currect directory as working directory 
+#SBATCH --nodes=1 
+#SBATCH --ntasks=1 
+#SBATCH --cpus-per-task=8      ## Request 8 cores 
+#SBATCH --mem=32G              ## Assign 32G memory per node 
+ 
+cd $TMPDIR 
+mkdir input 
+rsync -av $SLURM_SUBMIT_DIR/references/ input/ 
+mkdir results 
+srun STAR --inFiles $TMPDIR/input --parallel $SRUN_CPUS_PER_TASK --outFiles $TMPDIR/results 
+rsync -av $TMPDIR/results/ $SLURM_SUBMIT_DIR/processed/
 
    
-
    Figure 9: Source code for tmpdir.sh
    
+
    Figure 9: Source code for tmpdir.sh
    
                                                                                
 
                                                                                
    
    
-   
    2.10    SSH Keys For MPI
    
+   
    2.10    SSH Keys For MPI
    
 
    Some programs effect their parallel processing via MPI (which is a communication protocol). An
 example of such software is Fluent. MPI needs to have ‘passwordless login’ set up, which means SSH
 keys. In your NFS-mounted home directory:
@@ -1324,7 +1423,7 @@ 
    2.10
      permissions by default).
 
    
 
    
-   
    2.11    Creating Virtual Environments
    
+   
    2.11    Creating Virtual Environments
    
 
    The following documentation is specific to the Speed HPC Facility at the Gina Cody School of
 Engineering and Computer Science. Virtual environments typically instantiated via Conda or Python.
 Another option is Singularity detailed in Section 2.16. Usually, virtual environments are created once
@@ -1333,7 +1432,7 @@ 
    
 
    
-   
    2.11.1    Anaconda
    
+   
    2.11.1    Anaconda
    
 
    Request an interactive session in the queue you wish to submit your jobs to (e.g., salloc -p pg
 –gpus=1 for GPU jobs). Once your interactive has started, create an anaconda environment in your
 speed-scratch directory by using the prefix option when executing conda create. For example,
@@ -1346,7 +1445,7 @@ 
    2.11.1   
-   
    +   
     module load anaconda3/2023.03/default
 conda create --prefix /speed-scratch/a_user/myconda
 
    
@@ -1354,13 +1453,13 @@ 
    2.11.1   
    Note: Without the prefix option, the conda create command creates the environment in a_user’s
 home directory by default.
 
    
-
    List Environments.
+
    List Environments.
    To view your conda environments, type: conda info --envs
                                                                                
 
                                                                                
 
    
-   
    +   
     # conda environments:
 #
 base                  *  /encs/pkg/anaconda3-2023.03/root
@@ -1368,13 +1467,13 @@ 
    2.11.1   
 
    
 
    
-
    Activate an Environment.
+
    Activate an Environment.
    Activate the environment speedscratcha_usermyconda as follows
                                                                                
 
                                                                                
 
    
-   
    +   
     conda activate /speed-scratch/a_user/myconda
 
    
 
     After activating your environment, add pip to your environment by using
@@ -1382,7 +1481,7 @@ 
    2.11.1   
-   
    +   
     conda install pip
 
    
 
     This will install pip and pip’s dependencies, including python, into the environment.
@@ -1394,7 +1493,7 @@ 
    2.11.1   
-     
    +     
          salloc -p pg --gpus=1 --mem=10GB -A <slurm account name>
      cd /speed-scratch/$USER
      module load python/3.11.0/default
@@ -1414,7 +1513,7 @@ 
    2.11.1   conda install installs modules from anaconda’s repository.
 
    
 
    
-   
    2.11.2    Python
    
+   
    2.11.2    Python
    
 
    Setting up a Python virtual environment is fairly straightforward. The first step is to request an
 interactive session in the queue you wish to submit your jobs to.
 
       We have a simple example that use a Python virtual environment:
@@ -1426,7 +1525,7 @@ 
    2.11.2   
-     
    +     
          salloc -p pg --gpus=1 --mem=10GB -A <slurm account name>
      cd /speed-scratch/$USER
      module load python/3.9.1/default
@@ -1446,51 +1545,51 @@ 
    2.11.2   --gpus= when preparing environments for CPU jobs.
 
    
 
    
-   
    2.12    Example Job Script: Fluent
    
+   
    2.12    Example Job Script: Fluent
    
    
     
 
                                                                                
 
                                                                                
-
+
                                                                                
 
                                                                                
-
    #!/encs/bin/tcsh 
- 
-#SBATCH --job-name=flu10000    ## Give the job a name 
-#SBATCH --mail-type=ALL        ## Receive all email type notifications 
-#SBATCH --mail-user=YOUR_USER_NAME@encs.concordia.ca 
-#SBATCH --chdir=./             ## Use currect directory as working directory 
-#SBATCH --nodes=1              ## Number of nodes to run on 
-#SBATCH --ntasks-per-node=32   ## Number of cores 
-#SBATCH --cpus-per-task=1      ## Number of MPI threads 
-#SBATCH --mem=160G             ## Assign 160G memory per node 
- 
-date 
- 
-module avail ansys 
- 
-module load ansys/19.2/default 
-cd $TMPDIR 
- 
-set FLUENTNODES = "‘scontrol␣show␣hostnames‘" 
-set FLUENTNODES = ‘echo $FLUENTNODES | tr ’ ’ ’,’‘ 
- 
-date 
- 
-srun fluent 3ddp \ 
-        -g -t$SLURM_NTASKS \ 
-        -g-cnf=$FLUENTNODES \ 
-        -i $SLURM_SUBMIT_DIR/fluentdata/info.jou > call.txt 
- 
-date 
- 
-srun rsync -av $TMPDIR/ $SLURM_SUBMIT_DIR/fluentparallel/ 
- 
-date
+
    #!/encs/bin/tcsh 
+ 
+#SBATCH --job-name=flu10000    ## Give the job a name 
+#SBATCH --mail-type=ALL        ## Receive all email type notifications 
+#SBATCH --mail-user=YOUR_USER_NAME@encs.concordia.ca 
+#SBATCH --chdir=./             ## Use currect directory as working directory 
+#SBATCH --nodes=1              ## Number of nodes to run on 
+#SBATCH --ntasks-per-node=32   ## Number of cores 
+#SBATCH --cpus-per-task=1      ## Number of MPI threads 
+#SBATCH --mem=160G             ## Assign 160G memory per node 
+ 
+date 
+ 
+module avail ansys 
+ 
+module load ansys/19.2/default 
+cd $TMPDIR 
+ 
+set FLUENTNODES = "‘scontrol␣show␣hostnames‘" 
+set FLUENTNODES = ‘echo $FLUENTNODES | tr ’ ’ ’,’‘ 
+ 
+date 
+ 
+srun fluent 3ddp \ 
+        -g -t$SLURM_NTASKS \ 
+        -g-cnf=$FLUENTNODES \ 
+        -i $SLURM_SUBMIT_DIR/fluentdata/info.jou > call.txt 
+ 
+date 
+ 
+srun rsync -av $TMPDIR/ $SLURM_SUBMIT_DIR/fluentparallel/ 
+ 
+date
 
    
-
    Figure 10: Source code for fluent.sh
    
+
    Figure 10: Source code for fluent.sh
    
                                                                                
 
                                                                                
@@ -1505,7 +1604,7 @@ 
    Caveat: take care with journal-file file
 paths.
 
    
-   
    2.13    Example Job: efficientdet
    
+   
    2.13    Example Job: efficientdet
    
 
    The following steps describing how to create an efficientdet environment on Speed, were submitted by
 a member of Dr. Amer’s research group.
 
    
@@ -1526,7 +1625,7 @@ 
    
+
     pip install tensorflow==2.7.0
 pip install lxml>=4.6.1
 pip install absl-py>=0.10.0
@@ -1545,7 +1644,7 @@ 
    
 
    
 
    
-   
    2.14    Java Jobs
    
+   
    2.14    Java Jobs
    
 
    Jobs that call java have a memory overhead, which needs to be taken into account when assigning a
 value to --mem. Even the most basic java call, java -Xmx1G -version, will need to have,
 --mem=5G, with the 4-GB difference representing the memory overhead. Note that this memory
@@ -1554,7 +1653,7 @@ 
    2.14   
 314G.
 
    
 
    
-   
    2.15    Scheduling On The GPU Nodes
    
+   
    2.15    Scheduling On The GPU Nodes
    
 
    The primary cluster has two GPU nodes, each with six Tesla (CUDA-compatible) P6 cards: each card
 has 2048 cores and 16GB of RAM. Though note that the P6 is mainly a single-precision card, so
 unless you need the GPU double precision, double-precision calculations will be faster on a CPU
@@ -1565,7 +1664,7 @@ 
    
+   
     #SBATCH --gpus=[1|2]
 
    
 
    
@@ -1575,7 +1674,7 @@ 
    
+   
     sbatch -p pg ./<myscript>.sh
 
    
 
    
@@ -1584,7 +1683,7 @@ 
    
+   
     ssh <username>@speed[-05|-17|37-43] nvidia-smi
 
    
 
    
@@ -1593,7 +1692,7 @@ 
    
+   
     sinfo -p pg --long --Node
 
    
 
    
@@ -1609,7 +1708,7 @@ 
    
+   
     [serguei@speed-submit src] % sinfo -p pg --long --Node
 Thu Oct 19 22:31:04 2023
 NODELIST   NODES PARTITION       STATE CPUS    S:C:T MEMORY TMP_DISK WEIGHT AVAIL_FE REASON
@@ -1637,7 +1736,7 @@ 
    
+   
     [serguei@speed-submit src] % squeue -p pg -o "%15N %.6D %7P %.11T %.4c %.8z %.6m %.8d %.6w %.8f %20G %20E"
 NODELIST         NODES PARTITI       STATE MIN_    S:C:T MIN_ME MIN_TMP_  WCKEY FEATURES GROUP DEPENDENCY
 speed-05             1 pg          RUNNING    1    *:*:*     1G        0 (null)   (null) 11929     (null)
@@ -1650,7 +1749,7 @@ 
    
 
    
 
    
-   
    2.15.1    P6 on Multi-GPU, Multi-Node
    
+   
    2.15.1    P6 on Multi-GPU, Multi-Node
    
 
    As described lines above, P6 cards are not compatible with Distribute and DataParallel functions
 (Pytorch, Tensorflow) when running on Multi-GPUs. One workaround is to run the job in
 Multi-node, single GPU per node; per example:
@@ -1658,7 +1757,7 @@ 
    
+   
     #SBATCH --nodes=2
 #SBATCH --gpus-per-node=1
 
    
@@ -1668,7 +1767,7 @@ 
    
 
    
-   
    2.15.2    CUDA
    
+   
    2.15.2    CUDA
    
 
    When calling CUDA within job scripts, it is important to create a link to the desired CUDA libraries and
 set the runtime link path to the same libraries. For example, to use the cuda-11.5 libraries, specify
 the following in your Makefile.
@@ -1676,7 +1775,7 @@ 
    2.15.2   
 
                                                                                
 
    
-   
    +   
     -L/encs/pkg/cuda-11.5/root/lib64 -Wl,-rpath,/encs/pkg/cuda-11.5/root/lib64
 
    
 
    
@@ -1684,14 +1783,14 @@ 
    2.15.2   
 load gcc/8.4 or module load gcc/9.3
 
    
 
    
-   
    2.15.3    Special Notes for sending CUDA jobs to the GPU Queue
    
+   
    2.15.3    Special Notes for sending CUDA jobs to the GPU Queue
    
 
    Interactive jobs (Section 2.8) must be submitted to the GPU partition in order to compile and link.
 We have several versions of CUDA installed in:
                                                                                
 
                                                                                
 
    
-   
    +   
     /encs/pkg/cuda-11.5/root/
 /encs/pkg/cuda-10.2/root/
 /encs/pkg/cuda-9.2/root
@@ -1701,15 +1800,15 @@ 
    usrlocalcuda with one of the above.
 
    
 
    
-   
    2.15.4    OpenISS Examples
    
+   
    2.15.4    OpenISS Examples
    
 
    These represent more comprehensive research-like examples of jobs for computer vision and other
 tasks with a lot longer runtime (a subject to the number of epochs and other parameters) derive from
 the actual research works of students and their theses. These jobs require the use of CUDA
 and GPUs. These examples are available as “native” jobs on Speed and as Singularity
 containers.
 
    
-
    OpenISS and REID
-
+
    OpenISS and REID
+
    The example openiss-reid-speed.sh illustrates a job for a computer-vision based person
 re-identification (e.g., motion capture-based tracking for stage performance) part of the OpenISS
 project by Haotao Lai [10] using TensorFlow and Keras. The fork of the original repo [12] adjusted to
@@ -1724,8 +1823,8 @@ 
    2.15
                                                                                
 
                                                                                
-
    OpenISS and YOLOv3
-
+
    OpenISS and YOLOv3
+
    The related code using YOLOv3 framework is in the the fork of the original repo [11] adjusted to
 to run on Speed is here:
 
    
@@ -1746,7 +1845,7 @@ 
    2.15
      
  • https://github.com/NAG-DevOps/speed-hpc/tree/master/src#openiss-yolov3
    • 
 
    
 
    
-   
    2.16    Singularity Containers
    
+   
    2.16    Singularity Containers
    
 
    If the /encs software tree does not have a required software instantaneously available, another option
 is to run Singularity containers. We run EL7 flavor of Linux, and if some projects require Ubuntu or
 other distributions, there is a possibility to run that software as a container, including the ones
@@ -1778,7 +1877,7 @@ 
    2
 
                                                                                
 
    
-   
    +   
     /speed-scratch/nag-public:
 
 openiss-cuda-conda-jupyter.sif
@@ -1820,7 +1919,7 @@ 
    2
 
                                                                                
 
    
-   
    +   
     salloc --gpus=1 -n8 --mem=4Gb -t60
 cd /speed-scratch/$USER/
 singularity pull openiss-cuda-devicequery.sif docker://openiss/openiss-cuda-devicequery
@@ -1833,14 +1932,14 @@ 
    2
 example.
 
    
 
    
-   
    3    Conclusion
    
+   
    3    Conclusion
    
 
    The cluster is, “first come, first served”, until it fills, and then job position in the queue is
 based upon past usage. The scheduler does attempt to fill gaps, though, so sometimes a
 single-core job of lower priority will schedule before a multi-core job of higher priority, for
 example.
 
    
 
    
-   
    3.1    Important Limitations
    
+   
    3.1    Important Limitations
    
      
      
      
    • New users are restricted to a total of 32 cores: write to rt-ex-hpc@encs.concordia.ca
      if you need more temporarily (192 is the maximum, or, 6 jobs of 32 cores each).
@@ -1871,7 +1970,7 @@ 
      3.
 
    
 
    
 
    
-   
    3.2    Tips/Tricks
    
+   
    3.2    Tips/Tricks
    
      
      
      
    • Files/scripts must have Linux line breaks in them (not Windows ones). Use file command
      to verify; and dos2unix command to convert.
@@ -1894,7 +1993,7 @@ 
      3.2   
      
    • E-mail, rt-ex-hpc AT encs.concordia.ca, with any concerns/questions.
    
 
    
 
    
-   
    3.3    Use Cases
    
+   
    3.3    Use Cases
    
      
      
      
    • 
      
      HPC Committee’s initial batch about 6 students (end of 2019): 
      
@@ -1973,10 +2072,10 @@ 
      3.3   
      
    
 
    
 
    
-   
    A    History
    
+   
    A    History
    
 
    
 
    
-   
    A.1    Acknowledgments
    
+   
    A.1    Acknowledgments
    
      
      
      
    • The first 6 (to 6.5) versions of this manual and early UGE job script samples, Singularity
      testing and user support were produced/done by Dr. Scott Bunnell during his time at
@@ -1986,14 +2085,14 @@ 
      A.1   
      
    • Dr. Tariq  Daradkeh,  was  our  IT  Instructional  Specialist  August  2022  to  September
      2023; working on the scheduler, scheduling research, end user support, and integration
-     of  examples,  such  as  YOLOv3  in  Section 2.15.4.0  other  tasks.  We  have  a  continued
+     of  examples,  such  as  YOLOv3  in  Section 2.15.4.0  other  tasks.  We  have  a  continued
      collaboration on HPC/scheduling research.
    
                                                                                
 
                                                                                
 
    
 
    
-   
    A.2    Migration from UGE to SLURM
    
+   
    A.2    Migration from UGE to SLURM
    
 
    For long term users who started off with Grid Engine here are some resources to make a transition
 and mapping to the job submission process.
 
    
@@ -2005,7 +2104,7 @@ 
    
+     
          GE  => SLURM
      s.q    ps
      g.q    pg
@@ -2019,8 +2118,8 @@ 
    https://docs.alliancecan.ca/wiki/Running_jobs
    https://www.depts.ttu.edu/hpcc/userguides/general_guides/Conversion_Table_1.pdf
    https://docs.mpcdf.mpg.de/doc/computing/clusters/aux/migration-from-sge-to-slurm
 
    
      
     
- PIC
-
    Figure 11: Rosetta Mappings of Scheduler Commands from SchedMD
    
+ PIC
+
    Figure 11: Rosetta Mappings of Scheduler Commands from SchedMD
    
      
    
      
      
  • 
@@ -2032,7 +2131,7 @@ 
    
+     
          # Speed environment set up
      if ($HOSTNAME == speed-submit.encs.concordia.ca) then
         source /local/pkg/uge-8.6.3/root/default/common/settings.csh
@@ -2044,7 +2143,7 @@ 
    
+     
          # Speed environment set up
      if [ $HOSTNAME = "speed-submit.encs.concordia.ca" ]; then
          . /local/pkg/uge-8.6.3/root/default/common/settings.sh
@@ -2058,21 +2157,21 @@ 
    
 
    
-   
    A.3    Phases
    
+   
    A.3    Phases
    
 
    Brief summary of Speed evolution phases.
 
    
 
    
-   
    A.3.1    Phase 4
    
+   
    A.3.1    Phase 4
    
 
    Phase 4 had 7 SuperMicro servers with 4x A100 80GB GPUs each added, dubbed as “SPEED2”. We
 also moved from Grid Engine to SLURM.
 
    
 
    
-   
    A.3.2    Phase 3
    
+   
    A.3.2    Phase 3
    
 
    Phase 3 had 4 vidpro nodes added from Dr. Amer totalling 6x P6 and 6x V100 GPUs
 added.
 
    
 
    
-   
    A.3.3    Phase 2
    
+   
    A.3.3    Phase 2
    
 
    Phase 2 saw 6x NVIDIA Tesla P6 added and 8x more compute nodes. The P6s replaced 4x of FirePro
 S7150.
                                                                                
@@ -2080,7 +2179,7 @@ 
    A.3.3   
    
 
    
-   
    A.3.4    Phase 1
    
+   
    A.3.4    Phase 1
    
 
    Phase 1 of Speed was of the following configuration:
 
    
      
      
@@ -2091,20 +2190,20 @@ 
      A.3.4   
    
 
    
 
    
-   
    B    Frequently Asked Questions
    
+   
    B    Frequently Asked Questions
    
 
    
 
    
-   
    B.1    Where do I learn about Linux?
    
+   
    B.1    Where do I learn about Linux?
    
 
    All Speed users are expected to have a basic understanding of Linux and its commonly used
 commands.
 
    
 
    
-   
    Software Carpentry
    
+   
    Software Carpentry
    
 
    Software Carpentry provides free resources to learn software, including a workshop on the Unix shell.
 https://software-carpentry.org/lessons/
 
    
 
    
-   
    Udemy
    
+   
    Udemy
    
 
    There are a number of Udemy courses, including free ones, that will assist you in learning Linux.
 Active Concordia faculty, staff and students have access to Udemy courses. The course Linux
 Mastery: Master the Linux Command Line in 11.5 Hours is a good starting point for
@@ -2115,25 +2214,25 @@ 
    Udemy
    
                                                                                
 
    
 
    
-   
    B.2    How to use the “bash shell” on Speed?
    
+   
    B.2    How to use the “bash shell” on Speed?
    
 
    This section describes how to use the “bash shell” on Speed. Review Section 2.1.2 to ensure that your
 bash environment is set up.
 
    
 
    
-   
    B.2.1    How do I set bash as my login shell?
    
+   
    B.2.1    How do I set bash as my login shell?
    
 
    In order to set your default login shell to bash on Speed, your login shell on all GCS servers must be
 changed to bash. To make this change, create a ticket with the Service Desk (or email help at
 concordia.ca) to request that bash become your default login shell for your ENCS user account on
 all GCS servers.
 
    
 
    
-   
    B.2.2    How do I move into a bash shell on Speed?
    
+   
    B.2.2    How do I move into a bash shell on Speed?
    
 
    To move to the bash shell, type bash at the command prompt. For example:
                                                                                
 
                                                                                
 
    
-   
    +   
     [speed-submit] [/home/a/a_user] > bash
 bash-4.4$ echo $0
 bash
@@ -2143,7 +2242,7 @@ 
    bash-4.4$ after entering the bash shell.
 
    
 
    
-   
    B.2.3    How do I use the bash shell in an interactive session on Speed?
    
+   
    B.2.3    How do I use the bash shell in an interactive session on Speed?
    
 
    Below are examples of how to use bash as a shell in your interactive job sessions with both the salloc
 and srun commands.
 
    
@@ -2153,41 +2252,41 @@ 
    srun --mem=50G -n 5 --pty /encs/bin/bash
    • 
 
       Note: Make sure the interactive job requests memory, cores, etc.
 
    
-   
    B.2.4    How do I run scripts written in bash on Speed?
    
+   
    B.2.4    How do I run scripts written in bash on Speed?
    
 
    To execute bash scripts on Speed:
      
      
-
    1. Ensure that the shebang of your bash job script is #!/encs/bin/bash
+
    2. Ensure that the shebang of your bash job script is #!/encs/bin/bash
      
      3. 
-
    3. Use the sbatch command to submit your job script to the scheduler.
    
+
  • Use the sbatch command to submit your job script to the scheduler.
    • 
 
       The Speed GitHub contains a sample bash job script.
                                                                                
 
                                                                                
 
    
 
    
-   
    B.3    How to resolve “Disk quota exceeded” errors?
    
+   
    B.3    How to resolve “Disk quota exceeded” errors?
    
 
    
 
    
-   
    B.3.1    Probable Cause
    
+   
    B.3.1    Probable Cause
    
 
    The “Disk quota exceeded” Error occurs when your application has run out of disk space to write
 to. On Speed this error can be returned when:
 
    
      
      
-
    1. Your NFS-provided home is full and cannot be written to. You can verify this using quota
+
    2. Your NFS-provided home is full and cannot be written to. You can verify this using quota
      and bigfiles commands.
      
      3. 
-
    3. The /tmp directory on the speed node your application is running on is full and cannot
+
    4. The /tmp directory on the speed node your application is running on is full and cannot
      be written to.
    
 
    
 
    
-   
    B.3.2    Possible Solutions
    
+   
    B.3.2    Possible Solutions
    
 
    
      
      
-
    1. Use the --chdir job script option to set the directory that the job script is submitted
+
    2. Use the --chdir job script option to set the directory that the job script is submitted
      from the job working directory. The job working directory is the directory that
      the job will write output files in.
      
      3. 
-
    3. 
+
    4. 
      
      The use local disk space is generally recommended for IO intensive operations. However, as the
      size of /tmp on speed nodes is 1TB it can be necessary for scripts to store temporary data
      elsewhere. Review the documentation for each module called within your script to determine
@@ -2208,7 +2307,7 @@ 
      B.
 
                                                                                
 
      
-         
      +         
                mkdir -m 750 /speed-scratch/$USER/output
           
 
      
@@ -2220,7 +2319,7 @@ 
      B.
 
                                                                                
 
      
-         
      +         
                mkdir -m 750 /speed-scratch/$USER/recovery
 
      
          
      
@@ -2231,7 +2330,7 @@ 
      B.
 
      In the above example, $USER is an environment variable containing your ENCS username.
 
      
 
      
-   
      B.3.3    Example of setting working directories for COMSOL
      
+   
      B.3.3    Example of setting working directories for COMSOL