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-

Contents

+

Contents

- 1 Introduction -
 1.1 Resources -
 1.2 Team -
 1.3 What Speed Consists of -
 1.4 What Speed Is Ideal For -
 1.5 What Speed Is Not -
 1.6 Available Software -
 1.7 Requesting Access -
2 Job Management -
 2.1 Getting Started -
  2.1.1 SSH Connections -
  2.1.2 Environment Set Up -
 2.2 Job Submission Basics -
  2.2.1 Directives -
  2.2.2 Module Loads -
  2.2.3 User Scripting -
 2.3 Sample Job Script -
 2.4 Common Job Management Commands Summary -
 2.5 Advanced sbatch Options - - - -
 2.6 Array Jobs -
 2.7 Requesting Multiple Cores (i.e., Multithreading Jobs) -
 2.8 Interactive Jobs -
  2.8.1 Command Line -
  2.8.2 Graphical Applications -
  2.8.3 Jupyter Notebooks -
 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 CUDA -
  2.15.2 Special Notes for sending CUDA jobs to the GPU Queue -
  2.15.3 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 + 1 Introduction +
1.1 Resources +
1.2 Team +
1.3 What Speed Consists of +
1.4 What Speed Is Ideal For +
1.5 What Speed Is Not +
1.6 Available Software +
1.7 Requesting Access +
2 Job Management +
2.1 Getting Started +
2.1.1 SSH Connections +
2.1.2 Environment Set Up +
2.2 Job Submission Basics +
2.2.1 Directives +
2.2.2 Module Loads +
2.2.3 User Scripting +
2.3 Sample Job Script +
2.4 Common Job Management Commands Summary +
2.5 Advanced sbatch Options + + + +
2.6 Array Jobs +
2.7 Requesting Multiple Cores (i.e., Multithreading Jobs) +
2.8 Interactive Jobs +
2.8.1 Command Line +
2.8.2 Graphical Applications +
2.8.3 Jupyter Notebooks +
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
-

1 Introduction

+

1 Introduction

This document contains basic information required to use “Speed” as well as tips and tricks, examples, and references to projects and papers that have used Speed. User contributions of sample jobs and/ or references are welcome. Details are sent to the hpc-ml mailing list.

Note: On October 20, 2023 with workshops prior, we have completed migration to SLURM (see -Figure 2) from Grid Engine (UGE/AGE) as our job scheduler, so this manual has been ported to use -SLURM’s syntax and commands. If you are a long-time GE user, see Appendix A.2 key highlights of +Figure 2) from Grid Engine (UGE/AGE) as our job scheduler, so this manual has been ported to use +SLURM’s syntax and commands. If you are a long-time GE user, see Appendix A.2 key highlights of the move needed to translate your GE jobs to SLURM as well as environment changes. These are also elaborated throughout this document and our examples as well in case you desire to re-read it. @@ -134,7 +135,7 @@

1

-

1.1 Resources

+

1.1 Resources

  • Our public GitHub page where the manual and sample job scripts are maintained (pull-requests (PRs), subject to review, are welcome):
    https://github.com/NAG-DevOps/speed-hpc
    https://github.com/NAG-DevOps/speed-hpc/pulls @@ -150,7 +151,7 @@

    1.1

    -

    1.2 Team

    +

    1.2 Team

    Speed is supported by:

      @@ -162,10 +163,10 @@

      1.2

      We receive support from the rest of AITS teams, such as NAG, SAG, FIS, and DOG.
      https://www.concordia.ca/ginacody/aits.html

      -

      1.3 What Speed Consists of

      +

      1.3 What Speed Consists of

      • Twenty four (24) 32-core compute nodes, each with 512 GB of memory and approximately - 1 TB of local volatile-scratch disk space (pictured in Figure 1). + 1 TB of local volatile-scratch disk space (pictured in Figure 1).
      • Twelve (12) NVIDIA Tesla P6 GPUs, with 16 GB of memory (compatible with the CUDA, OpenGL, OpenCL, and Vulkan APIs). @@ -180,7 +181,7 @@

        1

      • One AMD FirePro S7150 GPU, with 8 GB of memory (compatible with the Direct X, OpenGL, OpenCL, and Vulkan APIs).
      -
      +
      @@ -196,7 +197,7 @@

      1

      -
      +
      @@ -212,7 +213,7 @@

      1

      -

      1.4 What Speed Is Ideal For

      +

      1.4 What Speed Is Ideal For

      • To design and develop, test and run parallel, batch, and other algorithms, scripts with partial data sets. “Speed” has been optimised for compute jobs that are multi-core aware, @@ -245,7 +246,7 @@

      • Non-CUDA GPU jobs using OpenCL (speed-19 and -01|03|05|17|25|27|37-43).

      -

      1.5 What Speed Is Not

      +

      1.5 What Speed Is Not

      • Speed is not a web host and does not host websites.
        • @@ -255,13 +256,13 @@

        1.5
      • Does not run Kubernetes or other container orchestration software.
      • Does not run Docker. (Note: Speed does run Singularity and many Docker containers - can be converted to Singularity containers with a single command. See Section 2.16.) + can be converted to Singularity containers with a single command. See Section 2.16.)
      • Speed is not for jobs executed outside of the scheduler. (Jobs running outside of the scheduler will be killed and all data lost.)

      -

      1.6 Available Software

      +

      1.6 Available Software

      We have a great number of open-source software available and installed on “Speed” – various Python, CUDA versions, C++/Java compilers, OpenGL, OpenFOAM, OpenCV, TensorFlow, OpenMPI, OpenISS, MARF [24], etc. There are also a number of commercial packages, subject to @@ -282,7 +283,7 @@

      1.6
    • Fluent (19.2, ...)
      • -
    • Singularity containers (see Section 2.16) can run other operating systems and Linux +
    • Singularity containers (see Section 2.16) can run other operating systems and Linux distributions, like Ubuntu’s, as well as converted Docker containers.

  • We do our best to accommodate custom software requests. Python environments can use @@ -309,8 +310,8 @@

    1.6

    -

    1.7 Requesting Access

    -

    After reviewing the “What Speed is” (Section 1.4) and “What Speed is Not” (Section 1.5), request +

    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 faculty and staff may request access directly. Students must include the following in their message: @@ -331,7 +332,7 @@

    1.7 service.

    -

    2 Job Management

    +

    2 Job Management

    In these instructions, anything bracketed like so, <>, indicates a label/value to be replaced (the entire bracketed term needs replacement). We use SLURM as the Workload Manager. It supports primarily two types of jobs: batch and interactive. Batch jobs are used to run unattended @@ -347,23 +348,23 @@

    2

    We use srun for every complex compute step inside the script. Use interactive jobs to set up virtual environments, compilation, and debugging. salloc is preferred; allows multiple steps. srun can start -interactive jobs as well (see Section 2.8). Required and common job parameters: job-name (J), +interactive jobs as well (see Section 2.8). Required and common job parameters: job-name (J), mail-type, mem, ntasks (n), cpus-per-task, account, -p (partition).

    -

    2.1 Getting Started

    -

    Before getting started, please review the “What Speed is” (Section 1.4) and “What Speed is Not” -(Section 1.5). Once your GCS ENCS account has been granted access to “Speed”, use +

    2.1 Getting Started

    +

    Before getting started, please review the “What Speed is” (Section 1.4) and “What Speed is Not” +(Section 1.5). Once your GCS ENCS account has been granted access to “Speed”, use your GCS ENCS account credentials to create an SSH connection to speed (an alias for speed-submit.encs.concordia.ca). All users are expected to have a basic understanding of Linux -and its commonly used commands (see Appendix B.1 for resources). +and its commonly used commands (see Appendix B.1 for resources).

    -
    2.1.1 SSH Connections
    +
    2.1.1 SSH Connections

    Requirements to create connections to Speed:

    1. An active GCS ENCS user account, which has permission to connect to Speed (see - Section 1.7). + Section 1.7).
    2. If you are off campus, an active connection to Concordia’s VPN. Accessing Concordia’s VPN requires a Concordia netname. @@ -387,7 +388,7 @@
      2.1.1

      Read the AITS FAQ: How do I securely connect to a GCS server?

      -
      2.1.2 Environment Set Up
      +
      2.1.2 Environment Set Up

      After creating an SSH connection to Speed, you will need to make sure the srun, sbatch, and salloc commands are available to you. Type the command name at the command prompt and press enter. If the command is not available, e.g., (“command not found”) is returned, you need to @@ -407,11 +408,11 @@

      2.

      Tip: the default shell for GCS ENCS users is tcsh. If you would like to use bash, please contact rt-ex-hpc AT encs.concordia.ca.

      Note: If a “command not found” error appears after you log in to speed, your user account many -have probably have defunct Grid Engine environment commands. See Appendix A.2 to learn how to +have probably have defunct Grid Engine environment commands. See Appendix A.2 to learn how to prevent this error on login.

      -

      2.2 Job Submission Basics

      +

      2.2 Job Submission Basics

      Preparing your job for submission is fairly straightforward. Start by basing your job script on one of the examples available in the src/ directory of our GitHub’s (https://github.com/NAG-DevOps/speed-hpc). Job scripts are broken into four main sections: @@ -447,7 +448,7 @@

      2.

      -
      2.2.1 Directives
      +
      2.2.1 Directives

      Directives are comments included at the beginning of a job script that set the shell and the options for the job scheduler. The shebang directive is always the first line of a script. In your job script, this directive sets which shell your script’s commands will run in. On “Speed”, we recommend that your @@ -548,7 +549,7 @@

      2.2.1

-
2.2.2 Module Loads
+
2.2.2 Module Loads

As your job will run on a compute or GPU “Speed” node, and not the submit node, any software that is needed must be loaded by the job script. Software is loaded within the script just as it would be from the command line. @@ -611,7 +612,7 @@

2.2.2

Typically, only the module load command will be used in your script.

-
2.2.3 User Scripting
+
2.2.3 User Scripting

The last part the job script is the scripting that will be executed by the job. This part of the job script includes all commands required to set up and execute the task your script has been written to do. Any Linux command can be used at this step. This section can @@ -638,11 +639,11 @@

2.2.3 job’s end.

-

2.3 Sample Job Script

-

Now, let’s look at a basic job script, tcsh.sh in Figure 3 (you can copy it from our GitHub page or +

2.3 Sample Job Script

+

Now, let’s look at a basic job script, tcsh.sh in Figure 3 (you can copy it from our GitHub page or from /home/n/nul-uge).

-
+
@@ -721,7 +722,7 @@

2.3 output of the, module list command. Important information is often written to this file.

-

2.4 Common Job Management Commands Summary

+

2.4 Common Job Management Commands Summary

Here are useful job-management commands:

    @@ -782,7 +783,7 @@

-

2.5 Advanced sbatch Options

+

2.5 Advanced sbatch Options

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

@@ -815,7 +816,7 @@

./tcsh.sh).

-

2.6 Array Jobs

+

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 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 @@ -860,7 +861,7 @@

2.6

-

2.7 Requesting Multiple Cores (i.e., Multithreading Jobs)

+

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 in your script with: @@ -920,13 +921,13 @@

-

2.8 Interactive Jobs

+

2.8 Interactive Jobs

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
+
2.8.1 Command Line

To request an interactive job session, use, salloc [options], similarly to a sbatch command-line job, e.g., @@ -960,7 +961,7 @@

2.8.1

-
2.8.2 Graphical Applications
+
2.8.2 Graphical Applications

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 @@ -998,7 +999,7 @@

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 +

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. @@ -1020,7 +1021,7 @@

-
+
@@ -1036,9 +1037,9 @@
2.8.3 Jupyter Notebooks
+
2.8.3 Jupyter Notebooks

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 2.15.3 as +Section 2.16). Here we are using one of the OpenISS-derived containers (see Section 2.15.4 as well).

    @@ -1063,19 +1064,19 @@
    2.8

  1. 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) + running (Using speed-submit as a “jump server”) (Preferably: PuTTY, see Figure 5 and + Figure 6) 

    -     ssh -L 8888:localhost:8888 speed-XX
+     ssh -L 8888:speed-XX:8888 YOUR_USER@speed-submit.encs.concordia.ca

    Don’t close the tunnel.

  2. -

    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. @@ -1088,7 +1089,7 @@

    2.8

  3. Work with your notebook.
-
+
@@ -1103,7 +1104,7 @@
2.8
-
+
@@ -1118,7 +1119,7 @@
2.8
-
+
@@ -1133,7 +1134,7 @@
2.8
-

2.9 Scheduler Environment Variables

+

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: @@ -1151,7 +1152,7 @@

$SLURM_JOB_NODELIST=nodes participating in your job. -
  • $SLURM_ARRAY_TASK_ID=for array jobs (see Section 2.6). +
  • $SLURM_ARRAY_TASK_ID=for array jobs (see Section 2.6).

  • See a more complete list here: @@ -1161,9 +1162,9 @@

    https://slurm.schedmd.com/srun.html#SECTION_OUTPUT-ENVIRONMENT-VARIABLES

    • -

    In Figure 8 is a sample script, using some of these. +

    In Figure 8 is a sample script, using some of these.

    -
    +
    @@ -1195,7 +1196,7 @@

    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: @@ -1213,16 +1214,16 @@

    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 +Another option is Singularity detailed in Section 2.16. Usually, virtual environments are created once and before sending any job to the scheduler, so when sending the job to the scheduler we (1) activate the virtual environment, (2) use it, and (3) close it at the end of the job.

    -
    2.11.1 Anaconda
    +
    2.11.1 Anaconda

    To create an anaconda environment in your speed-scratch directory, use the prefix option when executing conda create. For example, to create an anaconda environment for a_user, execute the following at the command line: @@ -1231,13 +1232,14 @@

    2.11.1 
    +module load anaconda3/2023.03/default
 conda create --prefix /speed-scratch/a_user/myconda
    -

    -

    Note: Without the prefix option, the conda create command creates the environment in a_user’s +

    +

    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 @@ -1246,12 +1248,12 @@

    2.11.1 # conda environments: # -base                  *  /encs/pkg/anaconda3-2019.07/root +base                  *  /encs/pkg/anaconda3-2023.03/root                          /speed-scratch/a_user/myconda -

    +

    -

    Activate an Environment. +

    Activate an Environment. Activate the environment speedscratcha_usermyconda as follows @@ -1260,7 +1262,7 @@

    2.11.1 conda activate /speed-scratch/a_user/myconda -

    After activating your environment, add pip to your environment by using +

    After activating your environment, add pip to your environment by using @@ -1268,66 +1270,68 @@

    2.11.1 conda install pip -

    This will install pip and pip’s dependencies, including python, into the environment. +

    This will install pip and pip’s dependencies, including python, into the environment.

    • -

      A consolidated example using Conda: +

      A consolidated example using Conda: 

      -     cd /speed-scratch/$USER
-     srun --partition=p(s/g) -A Your_account --mem=10Gb --gpus=1 --pty /encs/bin/tcsh
-     module load python/3.11.0/default
-     conda create -p /speed-scratch/$USER/pytorch-env
-     conda activate /speed-scratch/$USER/pytorch-env
-     conda install python=3.11.0
-     pip3 install torch torchvision torchaudio --index-url \
-       https://download.pytorch.org/whl/cu117
-     ....
-     conda deactivate
-     exit
+        cd /speed-scratch/$USER
+        srun --partition=p(s/g) -A Your_account --mem=10Gb --gpus=1 --pty /encs/bin/tcsh
+        module load python/3.11.0/default
+        conda create -p /speed-scratch/$USER/pytorch-env
+        conda activate /speed-scratch/$USER/pytorch-env
+        conda install python=3.11.0
+        pip3 install torch torchvision torchaudio --index-url \
+          https://download.pytorch.org/whl/cu117
+        ....
+        conda deactivate
+        exit
+       
      -

    -

    Important Note: pip (and pip3) are used to install modules from the python distribution while +

    +

    Important Note: pip (and pip3) are used to install modules from the python distribution while conda install installs modules from anaconda’s repository. -

    +

    -
    2.11.2 Python
    -

    Setting up a Python virtual environment is fairly straightforward. We have a simple example that use +

    2.11.2 Python
    +

    Setting up a Python virtual environment is fairly straightforward. We have a simple example that use a Python virtual environment:

    • -

      Using Python Venv +

      Using Python Venv 

      -     cd /speed-scratch/$USER
-     srun --partition=p(s/g) -A Your_account --mem=10Gb --gpus=1 --pty /encs/bin/tcsh
-     module load python/3.9.1/default
-     mkdir -p /speed-scratch/$USER/tmp
-     setenv TMPDIR /speed-scratch/$USER/tmp
-     setenv TMP /speed-scratch/$USER/tmp
-     python -m venv $TMPDIR/testenv (testenv=name of the virtualEnv)
-     source /speed-scratch/$USER/tmp/testenv/bin/activate.csh
-     pip install modules…
-     deactivate
-     exit
+        cd /speed-scratch/$USER
+        srun --partition=p(s/g) -A Your_account --mem=10Gb --gpus=1 --pty /encs/bin/tcsh
+        module load python/3.9.1/default
+        mkdir -p /speed-scratch/$USER/tmp
+        setenv TMPDIR /speed-scratch/$USER/tmp
+        setenv TMP /speed-scratch/$USER/tmp
+        python -m venv $TMPDIR/testenv (testenv=name of the virtualEnv)
+        source /speed-scratch/$USER/tmp/testenv/bin/activate.csh
+        pip install modules…
+        deactivate
+        exit
+       
      -

      +

    • See, e.g., gurobi-with-python.sh
    -

    Important Note: partition ps is used for CPU jobs, partitions pg, pt are used for GPU jobs, no +

    Important Note: partition ps is used for CPU jobs, partitions pg, pt are used for GPU jobs, no need to use --gpus= when preparing environments for CPU jobs.

    -

    2.12 Example Job Script: Fluent

    -
    +

    2.12 Example Job Script: Fluent

    +
    @@ -1375,7 +1379,7 @@

    The job script in Figure 9 runs Fluent in parallel over 32 cores. Of note, we have requested +

    The job script in Figure 9 runs Fluent in parallel over 32 cores. Of note, we have requested e-mail notifications (--mail-type), are defining the parallel environment for, fluent, with, -t$SLURM_NTASKS and -g-cnf=$FLUENTNODES (very important), and are setting $TMPDIR as the in-job location for the “moment” rfile.out file (in-job, because the last line of the @@ -1385,7 +1389,7 @@

    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.

    @@ -1425,7 +1429,7 @@

    -

    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 @@ -1434,7 +1438,7 @@

    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 @@ -1530,48 +1534,66 @@

    -
    2.15.1 CUDA
    -

    When calling CUDA within job scripts, it is important to create a link to the desired CUDA libraries and +

    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: + + + +

    + 
    +#SBATCH --nodes=2
+#SBATCH --gpus-per-node=1
+
    +

    +

    On P6 nodes: speed-05, speed-17, speed-01 +

    The example: pytorch-multinode-multigpu.sh illustrates a job for training on Multi-nodes, +Multi-GPUs +

    +

    +
    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.

    - 
    +   
     -L/encs/pkg/cuda-11.5/root/lib64 -Wl,-rpath,/encs/pkg/cuda-11.5/root/lib64
 
    
-
    
-
       In your job script, specify the version of gcc to use prior to calling cuda. For example: module
+
    
+
       In your job script, specify the version of gcc to use prior to calling cuda. For example: module
 load gcc/8.4 or module load gcc/9.3
-
    
+
    
 
    
-   
    2.15.2    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.
+   
    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
 
    
-
    
-
       For CUDA to compile properly for the GPU partition, edit your Makefile replacing
+
    
+
       For CUDA to compile properly for the GPU partition, edit your Makefile replacing
 usrlocalcuda with one of the above.
-
    
+
    
 
    
-   
    2.15.3    OpenISS Examples
    
-
    These represent more comprehensive research-like examples of jobs for computer vision and other
+   
    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
@@ -1579,21 +1601,21 @@ 
    2.15
 
    
      
-
       and its detailed description on how to run it on Speed is in the README:
+
       and its detailed description on how to run it on Speed is in the README:
 
    
      
                                                                                
 
                                                                                
-
    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:
 
    
      
-
       Its example job scripts can run on both CPUs and GPUs, as well as interactively using
+
       Its example job scripts can run on both CPUs and GPUs, as well as interactively using
 TensorFlow:
 
    
      
-
       The detailed description on how to run these on Speed is in the README at:
+
       The detailed description on how to run these on Speed is in the README at:
 
    
      
-
    
+
    
 
    
-   
    2.16    Singularity Containers
    
-
    If the /encs software tree does not have a required software instantaneously available, another option
+   
    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
 translated from Docker.
-
       The example lambdal-singularity.sh showcases an immediate use of a container built for the
+
       The example lambdal-singularity.sh showcases an immediate use of a container built for the
 Ubuntu-based LambdaLabs software stack, originally built as a Docker image then pulled
 in as a Singularity container that is immediately available for use as that job example
 illustrates. The source material used for the docker image was our fork of their official repo:
@@ -1621,12 +1643,12 @@ 
    2
                                                                                
 
                                                                                
-
       NOTE: It is important if you make your own containers or pull from DockerHub, use your
+
       NOTE: It is important if you make your own containers or pull from DockerHub, use your
 /speed-scratch/$USER directory as these images may easily consume gigs of space in your home
 directory and you’d run out of quota there very fast.
-
       TIP: To check for your quota, and the corresponding commands to find big files, see:
+
       TIP: To check for your quota, and the corresponding commands to find big files, see:
 https://www.concordia.ca/ginacody/aits/encs-data-storage.html
-
       We likewise built equivalent OpenISS (Section 2.15.3) containers from their Docker
+
       We likewise built equivalent OpenISS (Section 2.15.4) containers from their Docker
 counter parts as they were used for teaching and research [14]. The images from
 https://github.com/NAG-DevOps/openiss-dockerfiles and their DockerHub equivalents
 https://hub.docker.com/u/openiss are found in the same public directory on
@@ -1640,7 +1662,7 @@ 
    2
 
                                                                                
 
    
-   
    +   
     /speed-scratch/nag-public:
 
 openiss-cuda-conda-jupyter.sif
@@ -1651,14 +1673,14 @@ 
    2
 openiss-reid.sif
 openiss-xeyes.sif
 
    
-
    
-
       The currently recommended version of Singularity is singularity/3.10.4/default.
-
       This section comprises an introduction to working with Singularity, its containers, and what can
+
    
+
       The currently recommended version of Singularity is singularity/3.10.4/default.
+
       This section comprises an introduction to working with Singularity, its containers, and what can
 and cannot be done with Singularity on the ENCS infrastructure. It is not intended to be an
 exhaustive presentation of Singularity: the program’s authors do a good job of that here:
 https://www.sylabs.io/docs/. It also assumes that you have successfully installed Singularity on a
 user-managed/personal system (see next paragraph as to why).
-
       Singularity containers are essentially either built from an existing container, or are built from
+
       Singularity containers are essentially either built from an existing container, or are built from
 scratch. Building from scratch requires a recipe file (think of like a Dockerfile), and the operation must
 be effected as root. You will not have root on the ENCS infrastructure, so any built-from-scratch
 containers must be created on a user-managed/personal system. Root-level permissions are also
@@ -1669,51 +1691,51 @@ 
    2
 containers are essentially a directory in an existing read-write space, and squashfs containers are
 a read-only compressed “file”. Note that file-system containers cannot be resized once
 built.
-
       Note that the default build is a squashfs one. Also note what Singularity’s authors have to say
+
       Note that the default build is a squashfs one. Also note what Singularity’s authors have to say
 about the builds, “A common workflow is to use the “sandbox” mode for development of
 the container, and then build it as a default (squashfs) Singularity image when done.”
 File-system containers are considered to be, “legacy”, at this point in time. When built, a
 very small overhead is allotted to a file-system container (think, MB), and that cannot be
 changed.
-
       Probably for the most of your workflows you might find there is a Docker container exists for your
+
       Probably for the most of your workflows you might find there is a Docker container exists for your
 tasks, in this case you can use the docker pull function of Singularity as a part of you virtual
 environment setup as an interactive job allocation:
                                                                                
 
                                                                                
 
    
-   
    +   
     salloc --gpus=1 -n8 --mem=4Gb -t60
 cd /speed-scratch/$USER/
 singularity pull openiss-cuda-devicequery.sif docker://openiss/openiss-cuda-devicequery
 INFO:    Converting OCI blobs to SIF format
 INFO:    Starting build...
 
    
-
    
-
    This method can be used for converting Docker containers directly on Speed. On GPU nodes make
+
    
+
    This method can be used for converting Docker containers directly on Speed. On GPU nodes make
 sure to pass on the --nv flag to Singularity, so its containers could access the GPUs. See the linked
 example.
-
    
+
    
 
    
-   
    3    Conclusion
    
-
    The cluster is, “first come, first served”, until it fills, and then job position in the queue is
+   
    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).
      
      • 
      
    • Batch job sessions are a maximum of one week in length (only 24 hours, though, for
-     interactive jobs, see Section 2.8).
+     interactive jobs, see Section 2.8).
      
      • 
      
    • 
-     
      Scripts can live in your NFS-provided home, but any substantial data need to be in your
+     
      Scripts can live in your NFS-provided home, but any substantial data need to be in your
      cluster-specific directory (located at /speed-scratch/<ENCSusername>/).
-     
      NFS is great for acute activity, but is not ideal for chronic activity. Any data that a job will
+     
      NFS is great for acute activity, but is not ideal for chronic activity. Any data that a job will
      read more than once should be copied at the start to the scratch disk of a compute node
      using $TMPDIR (and, perhaps, $SLURM_SUBMIT_DIR), any intermediary job data should be
      produced in $TMPDIR, and once a job is near to finishing, those data should be copied
@@ -1733,7 +1755,7 @@ 
      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.
@@ -1754,24 +1776,24 @@ 
      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): 
      
+     
      HPC Committee’s initial batch about 6 students (end of 2019): 
      
          
        
          
      • 10000 iterations job in Fluent finished in \(<26\) hours vs. 46 hours in Calcul Quebec
      
      
      • 
      
    • 
-     
      NAG’s MAC spoofer analyzer [18, 17], such as https://github.com/smokhov/atsm/tree/master/examples/flucid
+     
      NAG’s MAC spoofer analyzer [18, 17], such as https://github.com/smokhov/atsm/tree/master/examples/flucid
      
      
          
        
          
      • compilation of forensic computing reasoning cases about false or true positives of
          hardware address spoofing in the labs
      
      
      • 
      
    • 
-     
      S4 LAB/GIPSY R&D Group’s: 
      
+     
      S4 LAB/GIPSY R&D Group’s: 
      
          
        
          
      • MARFCAT and MARFPCAT (OSS signal processing and machine learning tools for
          vulnerable and weak code analysis and network packet capture analysis) [20, 15, 6]
@@ -1816,7 +1838,7 @@ 
        3.3   
      https://doi.org/10.1177/0278364920913945
      
        • 
      
      • 
-     
        The work “Haotao Lai. An OpenISS framework specialization for deep learning-based
+     
        The work “Haotao Lai. An OpenISS framework specialization for deep learning-based
      person re-identification.  Master’s thesis, Department of Computer Science and
      Software Engineering, Concordia University, Montreal, Canada, August 2019.
      https://spectrum.library.concordia.ca/id/eprint/985788/” using TensorFlow and Keras
@@ -1830,15 +1852,15 @@ 
        3.3   
          
        • 
          
      • Haotao     Lai     et al.             OpenISS     keras-yolo3     v0.1.0,     June     2021.
          https://github.com/OpenISS/openiss-yolov3
      
-     
      and theirs forks by the team.
+     
      and theirs forks by the team.
 
      
      
    
-
    
+
    
 
    
-   
    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
@@ -1848,102 +1870,102 @@ 
      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 2.15.3.0  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
    
-
    For long term users who started off with Grid Engine here are some resources to make a transition
+   
    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.
 
    
      
      
      
    • 
-     
      Queues are called “partitions” in SLURM. Our mapping from the GE queues to SLURM
+     
      Queues are called “partitions” in SLURM. Our mapping from the GE queues to SLURM
      partitions is as follows:
                                                                                
 
                                                                                
 
      
-     
      +     
            GE  => SLURM
      s.q    ps
      g.q    pg
      a.q    pa
 
      
-     
       We also have a new partition pt that covers SPEED2 nodes, which previously did not
+     
       We also have a new partition pt that covers SPEED2 nodes, which previously did not
      exist.
      
      • 
      
    • 
-     
      Commands and command options mappings are found in Figure 10 from
      https://slurm.schedmd.com/rosetta.pdf
      https://slurm.schedmd.com/pdfs/summary.pdf
      Other related helpful resources from similar organizations who either used SLURM for awhile or
+     
      Commands and command options mappings are found in Figure 10 from
      https://slurm.schedmd.com/rosetta.pdf
      https://slurm.schedmd.com/pdfs/summary.pdf
      Other related helpful resources from similar organizations who either used SLURM for awhile or
      also transitioned to it:
      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 10: Rosetta Mappings of Scheduler Commands from SchedMD
      
+     
       
+ PIC
+
      Figure 10: Rosetta Mappings of Scheduler Commands from SchedMD
      
      
      
      
      • 
      
    • 
-     
      NOTE: If you have used UGE commands in the past you probably still have these lines there;
+     
      NOTE: If you have used UGE commands in the past you probably still have these lines there;
      they should now be removed, as they have no use in SLURM and will start giving
      “command not found” errors on login when the software is removed:
-     
      csh/tcsh: Sample .tcshrc file:
+     
      csh/tcsh: Sample .tcshrc file:
                                                                                
 
                                                                                
 
      
-     
      +     
            # Speed environment set up
      if ($HOSTNAME == speed-submit.encs.concordia.ca) then
         source /local/pkg/uge-8.6.3/root/default/common/settings.csh
      endif
 
      
-     
      
-     
      Bourne shell/bash: Sample .bashrc file:
+     
      
+     
      Bourne shell/bash: Sample .bashrc file:
                                                                                
 
                                                                                
 
      
-     
      +     
            # Speed environment set up
      if [ $HOSTNAME = "speed-submit.encs.concordia.ca" ]; then
          . /local/pkg/uge-8.6.3/root/default/common/settings.sh
          printenv ORGANIZATION | grep -qw ENCS || . /encs/Share/bash/profile
      fi
 
      
-     
      
-     
      Note that you will need to either log out and back in, or execute a new shell, for the
+     
      
+     
      Note that you will need to either log out and back in, or execute a new shell, for the
      environment changes in the updated .tcshrc or .bashrc file to be applied (important).
 
      
      
    
-
    
+
    
 
    
-   
    A.3    Phases
    
-
    Brief summary of Speed evolution phases.
-
    
+   
    A.3    Phases
    
+
    Brief summary of Speed evolution phases.
+
    
 
    
-   
    A.3.1    Phase 4
    
-
    Phase 4 had 7 SuperMicro servers with 4x A100 80GB GPUs each added, dubbed as “SPEED2”. We
+   
    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
    
-
    Phase 3 had 4 vidpro nodes added from Dr. Amer totalling 6x P6 and 6x V100 GPUs
+   
    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
    
-
    Phase 2 saw 6x NVIDIA Tesla P6 added and 8x more compute nodes. The P6s replaced 4x of FirePro
+   
    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.
                                                                                
 
                                                                                
-
    
+
    
 
    
-   
    A.3.4    Phase 1
    
-
    Phase 1 of Speed was of the following configuration:
+   
    A.3.4    Phase 1
    
+
    Phase 1 of Speed was of the following configuration:
 
    
      
      
      
    • Sixteen,  32-core  nodes,  each  with  512 GB  of  memory  and  approximately  1 TB  of
@@ -1953,20 +1975,20 @@ 
      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
@@ -1977,35 +1999,35 @@ 
    Udemy
    
                                                                                
 
    
 
    
-   
    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
+   
    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 $0
-bash
+   
    + [speed-submit] [/home/a/a_user] > bash
+ bash-4.4$ echo $0
+ bash
 
    
 
    
 
       Note how the command prompt changed from [speed-submit] [/home/a/a_user] > to
 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.
 
    
@@ -2015,41 +2037,41 @@ 
    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
@@ -2070,9 +2092,9 @@ 
      B.
 
                                                                                
 
      
-         
      -         mkdir -m 750 /speed-scratch/$USER/output
-          
+         
      +           mkdir -m 750 /speed-scratch/$USER/output
+           
 
      
          
      
          
      5. 
@@ -2082,8 +2104,9 @@ 
      B.
 
                                                                                
 
      
-         
      -         mkdir -m 750 /speed-scratch/$USER/recovery
+         
      +           mkdir -m 750 /speed-scratch/$USER/recovery
+          
 
      
          
      
          
      
@@ -2093,7 +2116,7 @@ 
      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
      
      
        
      
      • 
      
        Create directories for recovery, temporary, and configuration files. For example, to create these
@@ -2102,8 +2125,9 @@ 
        
-     mkdir -m 750 -p /speed-scratch/$USER/comsol/{recovery,tmp,config}
+     
        +      mkdir -m 750 -p /speed-scratch/$USER/comsol/{recovery,tmp,config}
+      
 
        
      
        
      
        • 
@@ -2114,16 +2138,17 @@ 
        
-     -recoverydir /speed-scratch/$USER/comsol/recovery
-     -tmpdir /speed-scratch/$USER/comsol/tmp
-     -configuration/speed-scratch/$USER/comsol/config
+     
        +      -recoverydir /speed-scratch/$USER/comsol/recovery
+      -tmpdir /speed-scratch/$USER/comsol/tmp
+      -configuration/speed-scratch/$USER/comsol/config
+      
 
        
      
      
 
      In the above example, $USER is an environment variable containing your ENCS username.
 
      
 
      
-   
      B.3.4    Example of setting working directories for Python Modules
      
+   
      B.3.4    Example of setting working directories for Python Modules
      
 
      By default when adding a python module the /tmp directory is set as the temporary repository for
 files downloads. The size of the /tmp directory on speed-submit is too small for pytorch. To add a
 python module 
      
@@ -2134,8 +2159,9 @@ 
      
+     
              mkdir /speed-scratch/$USER/tmp
+      
 
      
      
      
      
      
@@ -2145,8 +2171,9 @@ 
      
+     
              setenv TMPDIR /speed-scratch/$USER/tmp
+      
 
      
      
      
      
      
@@ -2154,17 +2181,17 @@ 
         In the above example, $USER is an environment variable containing your ENCS username.
 
      
 
      
-   
      B.4    How do I check my job’s status?
      
+   
      B.4    How do I check my job’s status?
      
 
      When a job with a job id of 1234 is running or terminated, the status of that job can be tracked using
 ‘sacct -j 1234’. squeue -j 1234 can show while the job is sitting in the queue as well. Long term
 statistics on the job after its terminated can be found using sstat -j 1234 after slurmctld purges it
 its tracking state into the database.
 
      
 
      
-   
      B.5    Why is my job pending when nodes are empty?
      
+   
      B.5    Why is my job pending when nodes are empty?
      
 
      
 
      
-   
      B.5.1    Disabled nodes
      
+   
      B.5.1    Disabled nodes
      
 
      It is possible that one or a number of the Speed nodes are disabled. Nodes are disabled if they require
 maintenance. To verify if Speed nodes are disabled, see if they are in a draining or drained
 state:
@@ -2172,7 +2199,7 @@ 
      B.5.1
 
                                                                                
 
      
-   
      +   
       [serguei@speed-submit src] % sinfo --long --Node
 Thu Oct 19 21:25:12 2023
 NODELIST   NODES PARTITION       STATE CPUS    S:C:T MEMORY TMP_DISK WEIGHT AVAIL_FE REASON
@@ -2221,17 +2248,17 @@ 
      B.5.1
 and the disabled nodes have a state of idle.
 
      
 
      
-   
      B.5.2    Error in job submit request.
      
+   
      B.5.2    Error in job submit request.
      
 
      It is possible that your job is pending, because the job requested resources that are not available
 within Speed. To verify why job id 1234 is not running, execute ‘sacct -j 1234’. A summary of the
 reasons is available via the squeue command.
-
      
+
      
 
      
                                                                                
 
                                                                                
-   
      C    Sister Facilities
      
-
      Below is a list of resources and facilities similar to Speed at various capacities. Depending on your
+   
      C    Sister Facilities
      
+
      Below is a list of resources and facilities similar to Speed at various capacities. Depending on your
 research group and needs, they might be available to you. They are not managed by HPC/NAG of
 AITS, so contact their respective representatives.
 
      
@@ -2250,7 +2277,7 @@ 
      C   
      
    5. 
-     
      There are various Lambda Labs other GPU servers and like computers acquired by individual
+     
      There are various Lambda Labs other GPU servers and like computers acquired by individual
      researchers; if you are member of their research group, contact them directly. These resources
      are not managed by us. 
      
          
        
@@ -2285,8 +2312,8 @@ 
        C   
-   
        References
        
+
        
+   
        References
        
 
        
     
        
     
        
diff --git a/doc/web/speed-manual.css b/doc/web/speed-manual.css
index d521c14..7166477 100644
--- a/doc/web/speed-manual.css
+++ b/doc/web/speed-manual.css
@@ -62,11 +62,11 @@ div.par-math-display, div.math-display{text-align:center;}
 body{ margin:1em auto; max-width:80ch; padding:0 .62em; }
 h1,h2,h3,h4,h5 { line-height:1.2; }
 @media print{ body{ max-width:none } }
-.partHead, likepartHead { font-size: 2em; }
-.chapterHead, likechapterHead { font-size: 1.7411em; }
-.sectionHead, likesectionHead { font-size: 1.5157em; }
-.subsectionHead, likesubsectionHead { font-size: 1.3195em; }
-.subsubsectionHead, likesubsubsectionHead { font-size: 1.1487em; }
+.partHead, .likepartHead { font-size: 2em; }
+.chapterHead, .likechapterHead { font-size: 1.7411em; }
+.sectionHead, .likesectionHead { font-size: 1.5157em; }
+.subsectionHead, .likesubsectionHead { font-size: 1.3195em; }
+.subsubsectionHead, .likesubsubsectionHead { font-size: 1.1487em; }
  @media (prefers-color-scheme: dark) { img[src^="speed-manual"]{filter: invert(1); } } 
 li p.indent { text-indent: 0em }
 li p:first-child{ margin-top:0em; }
@@ -168,6 +168,10 @@ div.author{white-space: nowrap;}
 div.abstract p {margin-left:5%; margin-right:5%;}
 div.abstract {width:100%;}
 .abstracttitle{text-align:center;margin-bottom:1em;}
+.subsectionToc, .likesubsectionToc {margin-left:1em;}
+.subsubsectionToc, .likesubsubsectionToc {margin-left:2em;}
+.paragraphToc, .likeparagraphToc {margin-left:3em;}
+.subparagraphToc, .likesubparagraphToc {margin-left:4em;}
 figure.float, div.figure {margin-left: auto; margin-right: auto;}
 figure.figure {text-align:center;}
 figcaption.caption {text-indent:-2em; margin-left:3em; margin-right:1em; text-align:center;}

From a4cb04f57387edbd16cb14086eb162cb2a1572e5 Mon Sep 17 00:00:00 2001
From: carlos-encs <110119864+carlos-encs@users.noreply.github.com>
Date: Wed, 6 Mar 2024 13:43:09 -0500
Subject: [PATCH 3/4] Jupyter example

---
 src/README.md          |  16 ++
 src/gpu-ml-model.ipynb | 467 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 483 insertions(+)
 create mode 100644 src/gpu-ml-model.ipynb

diff --git a/src/README.md b/src/README.md
index 3cf3ebe..57b3439 100644
--- a/src/README.md
+++ b/src/README.md
@@ -35,6 +35,7 @@ run certcain things.
       + [Configuration and execution](#configuration-and-execution)
    * [CUDA](#cuda)
       + [Special Notes for sending CUDA jobs to the GPU Partition (`pg`)](#special-notes-for-sending-cuda-jobs-to-the-gpu-partition-pg)
+      + [Jupyter notebook example: Jupyter-Pytorch-CUDA](#jupyter-example-gpu-pytorch)
    * [Python Modules](#python-modules)
 
 
@@ -405,6 +406,21 @@ We have several versions of CUDA installed in:
 
 For CUDA to compile properly for the GPU queue, edit your `Makefile` replacing `/usr/local/cuda` with one of the above.
 
+
+### Jupyter notebook example: Jupyter-Pytorch-CUDA
+
+Example prepared to run on speed, extracted from: https://developers.redhat.com/learning/learn:openshift-data-science:configure-jupyter-notebook-use-gpus-aiml-modeling/resource/resources:how-examine-gpu-resources-pytorch
+
+From speed-submit: 
+- Download `gpu-ml-model.ipynb` from this github to your `/speed-scratch/$USER space`
+- `salloc --mem=10Gb --gpus=1`
+
+From the node (interactive session):
+- `module load singularity/3.10.4/default`
+- `srun singularity exec -B $PWD\:/speed-pwd,/speed-scratch/$USER\:/my-speed-scratch,/nettemp --env SHELL=/bin/bash --nv /speed-scratch/nag-public/jupyter-pytorch-cuda.sif /bin/bash -c '/opt/conda/bin/jupyter notebook --no-browser --notebook-dir=/speed-pwd --ip="*" --port=8888 --allow-root'`
+- Follow the steps described in: https://nag-devops.github.io/speed-hpc/#jupyter-notebooks
+- When Jupyter is running on the browser, open `gpu-ml-model.ipynb` and run each cell
+
 
 ## Python Modules
 
diff --git a/src/gpu-ml-model.ipynb b/src/gpu-ml-model.ipynb
new file mode 100644
index 0000000..b03dc70
--- /dev/null
+++ b/src/gpu-ml-model.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{\n",
+    " \"cells\": [\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 1,\n",
+    "   \"id\": \"5596f562-286a-4e39-aa35-723f210a84f3\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"import torch\\n\",\n",
+    "    \"import torch.nn as nn\\n\",\n",
+    "    \"import torch.nn.functional as F\\n\",\n",
+    "    \"from torch.utils.data import TensorDataset\\n\",\n",
+    "    \"import torch.optim as optim\\n\",\n",
+    "    \"import torchvision\\n\",\n",
+    "    \"from torchvision import datasets\\n\",\n",
+    "    \"import torchvision.transforms as transforms\\n\",\n",
+    "    \"import matplotlib.pyplot as plt\\n\",\n",
+    "    \"from tqdm import tqdm\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 2,\n",
+    "   \"id\": \"5f8981b6-5194-4dce-891a-febe26d26ac3\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n",
+    "    \"print(device)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 3,\n",
+    "   \"id\": \"8462da36-52db-4fdb-8e0d-4b78aa5d2435\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"'NVIDIA A100-SXM4-80GB MIG 1g.20gb'\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 3,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"torch.cuda.get_device_name(0)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 4,\n",
+    "   \"id\": \"afe4c059-c3dc-4a57-a657-a72fdf573b66\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"False , cpu\\n\"\n",
+    "     ]\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"X_train = torch.IntTensor([0, 30, 50, 75, 70])\\n\",\n",
+    "    \"print(X_train.is_cuda, \\\",\\\", X_train.device)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 5,\n",
+    "   \"id\": \"eda6746c-6c18-4350-888c-8651abad09af\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"True , cuda:0\\n\"\n",
+    "     ]\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"X_train = X_train.cuda()\\n\",\n",
+    "    \"# Alternative method: specify the device using the variable\\n\",\n",
+    "    \"# X_train = X_train.to(device)\\n\",\n",
+    "    \"# Confirm that the Tensor is on the GPU now\\n\",\n",
+    "    \"print(X_train.is_cuda, \\\",\\\", X_train.device)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 6,\n",
+    "   \"id\": \"fa4778e3-4b9f-417f-bfc5-c317fc517066\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"batch_size = 100\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"class SimpleNet(nn.Module):\\n\",\n",
+    "    \"    def __init__(self):\\n\",\n",
+    "    \"        super(SimpleNet, self).__init__()\\n\",\n",
+    "    \"        self.fc1 = nn.Linear(784, 784)\\n\",\n",
+    "    \"        self.fc2 = nn.Linear(784, 10)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    def forward(self, x):\\n\",\n",
+    "    \"        x = x.view(batch_size, -1)\\n\",\n",
+    "    \"        x = self.fc1(x)\\n\",\n",
+    "    \"        x = F.relu(x)\\n\",\n",
+    "    \"        x = self.fc2(x)\\n\",\n",
+    "    \"        output = F.softmax(x, dim=1)\\n\",\n",
+    "    \"        return output\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 7,\n",
+    "   \"id\": \"d4ad8baa-9e75-4c69-aca3-dd2fd82e0c38\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"model = SimpleNet().to(device)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 8,\n",
+    "   \"id\": \"0b9f3e4c-d4d7-4894-b118-7fa10e220a58\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"train_transform=transforms.Compose([\\n\",\n",
+    "    \"        transforms.ToTensor(),\\n\",\n",
+    "    \"        transforms.Normalize((0.1307,), (0.3081,))\\n\",\n",
+    "    \"        ])\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"test_transform=transforms.Compose([\\n\",\n",
+    "    \"        transforms.ToTensor(),\\n\",\n",
+    "    \"        transforms.Normalize((0.1307,), (0.3081,)),\\n\",\n",
+    "    \"        ])\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Set up a training data set\\n\",\n",
+    "    \"trainset = datasets.FashionMNIST('./data', train=True, download=True,\\n\",\n",
+    "    \"                  transform=train_transform)\\n\",\n",
+    "    \"train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\\n\",\n",
+    "    \"                                          shuffle=False, num_workers=2)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Set up a test data set\\n\",\n",
+    "    \"testset = datasets.FashionMNIST('./data', train=False,\\n\",\n",
+    "    \"                  transform=test_transform)\\n\",\n",
+    "    \"test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\\n\",\n",
+    "    \"                                        shuffle=False, num_workers=2)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 9,\n",
+    "   \"id\": \"bcbdb9d0-2129-4ac5-a189-08489f19a63a\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"
        \"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"labels_map = {\\n\",\n",
+    "    \"    0: \\\"T-Shirt\\\",\\n\",\n",
+    "    \"    1: \\\"Trouser\\\",\\n\",\n",
+    "    \"    2: \\\"Pullover\\\",\\n\",\n",
+    "    \"    3: \\\"Dress\\\",\\n\",\n",
+    "    \"    4: \\\"Coat\\\",\\n\",\n",
+    "    \"    5: \\\"Sandal\\\",\\n\",\n",
+    "    \"    6: \\\"Shirt\\\",\\n\",\n",
+    "    \"    7: \\\"Sneaker\\\",\\n\",\n",
+    "    \"    8: \\\"Bag\\\",\\n\",\n",
+    "    \"    9: \\\"Ankle Boot\\\",\\n\",\n",
+    "    \"}\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Plotting 9 random different items from the training data set, trainset.\\n\",\n",
+    "    \"figure = plt.figure(figsize=(8, 8))\\n\",\n",
+    "    \"for i in range(1, 3 * 3 + 1):\\n\",\n",
+    "    \"    sample_idx = torch.randint(len(trainset), size=(1,)).item()\\n\",\n",
+    "    \"    img, label = trainset[sample_idx]\\n\",\n",
+    "    \"    figure.add_subplot(3, 3, i)\\n\",\n",
+    "    \"    plt.title(labels_map[label])\\n\",\n",
+    "    \"    plt.axis(\\\"off\\\")\\n\",\n",
+    "    \"    plt.imshow(img.view(28,28), cmap=\\\"gray\\\")\\n\",\n",
+    "    \"plt.show()\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 10,\n",
+    "   \"id\": \"9ef4f746-f342-4adf-bce6-cfdef0c8a1df\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def train(model, device, train_loader, optimizer, epoch):\\n\",\n",
+    "    \"    \\\"\\\"\\\"Model training function\\\"\\\"\\\"\\n\",\n",
+    "    \"    model.train()\\n\",\n",
+    "    \"    print(device)\\n\",\n",
+    "    \"    for batch_idx, (data, target) in tqdm(enumerate(train_loader)):\\n\",\n",
+    "    \"        data, target = data.to(device), target.to(device)\\n\",\n",
+    "    \"        optimizer.zero_grad()\\n\",\n",
+    "    \"        output = model(data)\\n\",\n",
+    "    \"        loss = F.nll_loss(output, target)\\n\",\n",
+    "    \"        loss.backward()\\n\",\n",
+    "    \"        optimizer.step()\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 11,\n",
+    "   \"id\": \"fd843896-03b1-4cb7-a5c0-b420bba2cff9\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def test(model, device, test_loader):\\n\",\n",
+    "    \"    \\\"\\\"\\\"Model evaluating function\\\"\\\"\\\"\\n\",\n",
+    "    \"    model.eval()\\n\",\n",
+    "    \"    test_loss = 0\\n\",\n",
+    "    \"    correct = 0\\n\",\n",
+    "    \"    with torch.no_grad():\\n\",\n",
+    "    \"        for data, target in test_loader:\\n\",\n",
+    "    \"            data, target = data.to(device), target.to(device)\\n\",\n",
+    "    \"            output = model(data)\\n\",\n",
+    "    \"            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\\n\",\n",
+    "    \"            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\\n\",\n",
+    "    \"            correct += pred.eq(target.view_as(pred)).sum().item()\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    test_loss /= len(test_loader.dataset)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    print('\\\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n",
+    "    \"        test_loss, correct, len(test_loader.dataset),\\n\",\n",
+    "    \"        100. * correct / len(test_loader.dataset)))\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 12,\n",
+    "   \"id\": \"f47b42fe-4905-48fe-ad88-7f903e281ea1\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def test(model, device, test_loader):\\n\",\n",
+    "    \"    model.eval()\\n\",\n",
+    "    \"    test_loss = 0\\n\",\n",
+    "    \"    correct = 0\\n\",\n",
+    "    \"    # Use the no_grad method to increase computation speed\\n\",\n",
+    "    \"    # since computing the gradient is not necessary in this step.\\n\",\n",
+    "    \"    with torch.no_grad():\\n\",\n",
+    "    \"        for data, target in test_loader:\\n\",\n",
+    "    \"            data, target = data.to(device), target.to(device)\\n\",\n",
+    "    \"            output = model(data)\\n\",\n",
+    "    \"            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\\n\",\n",
+    "    \"            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\\n\",\n",
+    "    \"            correct += pred.eq(target.view_as(pred)).sum().item()\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    test_loss /= len(test_loader.dataset)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    print('\\\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n",
+    "    \"        test_loss, correct, len(test_loader.dataset),\\n\",\n",
+    "    \"        100. * correct / len(test_loader.dataset)))\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 13,\n",
+    "   \"id\": \"a4d951f1-ade1-44be-93f1-de53051109eb\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"EPOCHS = 5\\n\",\n",
+    "    \"# our optimization strategy used in training.\\n\",\n",
+    "    \"optimizer = optim.Adadelta(model.parameters(), lr=0.01)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 14,\n",
+    "   \"id\": \"c6072415-652e-4fb7-a0e4-57467ae23d23\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"EPOCH: 1\\n\",\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"600it [00:06, 89.07it/s]\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"Test set: Average loss: -0.5645, Accuracy: 6205/10000 (62%)\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"EPOCH: 2\\n\",\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"600it [00:06, 91.25it/s]\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"Test set: Average loss: -0.6295, Accuracy: 6881/10000 (69%)\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"EPOCH: 3\\n\",\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"600it [00:06, 94.15it/s]\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"Test set: Average loss: -0.6669, Accuracy: 7086/10000 (71%)\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"EPOCH: 4\\n\",\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"600it [00:06, 91.84it/s]\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"Test set: Average loss: -0.6833, Accuracy: 7143/10000 (71%)\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"EPOCH: 5\\n\",\n",
+    "      \"cuda:0\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"600it [00:06, 90.96it/s] \\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"Test set: Average loss: -0.6974, Accuracy: 7196/10000 (72%)\\n\",\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"for epoch in range(1, EPOCHS + 1):\\n\",\n",
+    "    \"        print( f\\\"EPOCH: {epoch}\\\")\\n\",\n",
+    "    \"        train(model, device, train_loader, optimizer, epoch)\\n\",\n",
+    "    \"        test(model, device, test_loader)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 15,\n",
+    "   \"id\": \"b3fb979c-ce84-4657-996e-8509912581de\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"# Saving the model's weights!\\n\",\n",
+    "    \"torch.save(model.state_dict(), \\\"mnist_fashion_SimpleNet.pt\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": null,\n",
+    "   \"id\": \"4cbda6a9-b468-45f6-a346-3f86fa2e9214\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": []\n",
+    "  }\n",
+    " ],\n",
+    " \"metadata\": {\n",
+    "  \"kernelspec\": {\n",
+    "   \"display_name\": \"Python 3 (ipykernel)\",\n",
+    "   \"language\": \"python\",\n",
+    "   \"name\": \"python3\"\n",
+    "  },\n",
+    "  \"language_info\": {\n",
+    "   \"codemirror_mode\": {\n",
+    "    \"name\": \"ipython\",\n",
+    "    \"version\": 3\n",
+    "   },\n",
+    "   \"file_extension\": \".py\",\n",
+    "   \"mimetype\": \"text/x-python\",\n",
+    "   \"name\": \"python\",\n",
+    "   \"nbconvert_exporter\": \"python\",\n",
+    "   \"pygments_lexer\": \"ipython3\",\n",
+    "   \"version\": \"3.11.8\"\n",
+    "  }\n",
+    " },\n",
+    " \"nbformat\": 4,\n",
+    " \"nbformat_minor\": 5\n",
+    "}"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 71a7eda106fb616fdaa77122330962caaee04066 Mon Sep 17 00:00:00 2001
From: carlos 
Date: Wed, 6 Mar 2024 14:00:42 -0500
Subject: [PATCH 4/4] Update gpu-ml-model.ipynb

---
 src/gpu-ml-model.ipynb | 883 ++++++++++++++++++++---------------------
 1 file changed, 432 insertions(+), 451 deletions(-)

diff --git a/src/gpu-ml-model.ipynb b/src/gpu-ml-model.ipynb
index b03dc70..620e436 100644
--- a/src/gpu-ml-model.ipynb
+++ b/src/gpu-ml-model.ipynb
@@ -2,466 +2,447 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "id": "5596f562-286a-4e39-aa35-723f210a84f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import TensorDataset\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torchvision import datasets\n",
+    "import torchvision.transforms as transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f8981b6-5194-4dce-891a-febe26d26ac3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8462da36-52db-4fdb-8e0d-4b78aa5d2435",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NVIDIA A100-SXM4-80GB MIG 1g.20gb'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.get_device_name(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "afe4c059-c3dc-4a57-a657-a72fdf573b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False , cpu\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = torch.IntTensor([0, 30, 50, 75, 70])\n",
+    "print(X_train.is_cuda, \",\", X_train.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eda6746c-6c18-4350-888c-8651abad09af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True , cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train = X_train.cuda()\n",
+    "# Alternative method: specify the device using the variable\n",
+    "# X_train = X_train.to(device)\n",
+    "# Confirm that the Tensor is on the GPU now\n",
+    "print(X_train.is_cuda, \",\", X_train.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fa4778e3-4b9f-417f-bfc5-c317fc517066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 100\n",
+    "\n",
+    "class SimpleNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(SimpleNet, self).__init__()\n",
+    "        self.fc1 = nn.Linear(784, 784)\n",
+    "        self.fc2 = nn.Linear(784, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x.view(batch_size, -1)\n",
+    "        x = self.fc1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.fc2(x)\n",
+    "        output = F.softmax(x, dim=1)\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d4ad8baa-9e75-4c69-aca3-dd2fd82e0c38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SimpleNet().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0b9f3e4c-d4d7-4894-b118-7fa10e220a58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_transform=transforms.Compose([\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize((0.1307,), (0.3081,))\n",
+    "        ])\n",
+    "\n",
+    "test_transform=transforms.Compose([\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize((0.1307,), (0.3081,)),\n",
+    "        ])\n",
+    "\n",
+    "# Set up a training data set\n",
+    "trainset = datasets.FashionMNIST('./data', train=True, download=True,\n",
+    "                  transform=train_transform)\n",
+    "train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
+    "                                          shuffle=False, num_workers=2)\n",
+    "\n",
+    "# Set up a test data set\n",
+    "testset = datasets.FashionMNIST('./data', train=False,\n",
+    "                  transform=test_transform)\n",
+    "test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
+    "                                        shuffle=False, num_workers=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bcbdb9d0-2129-4ac5-a189-08489f19a63a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_map = {\n",
+    "    0: \"T-Shirt\",\n",
+    "    1: \"Trouser\",\n",
+    "    2: \"Pullover\",\n",
+    "    3: \"Dress\",\n",
+    "    4: \"Coat\",\n",
+    "    5: \"Sandal\",\n",
+    "    6: \"Shirt\",\n",
+    "    7: \"Sneaker\",\n",
+    "    8: \"Bag\",\n",
+    "    9: \"Ankle Boot\",\n",
+    "}\n",
+    "\n",
+    "# Plotting 9 random different items from the training data set, trainset.\n",
+    "figure = plt.figure(figsize=(8, 8))\n",
+    "for i in range(1, 3 * 3 + 1):\n",
+    "    sample_idx = torch.randint(len(trainset), size=(1,)).item()\n",
+    "    img, label = trainset[sample_idx]\n",
+    "    figure.add_subplot(3, 3, i)\n",
+    "    plt.title(labels_map[label])\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img.view(28,28), cmap=\"gray\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9ef4f746-f342-4adf-bce6-cfdef0c8a1df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(model, device, train_loader, optimizer, epoch):\n",
+    "    \"\"\"Model training function\"\"\"\n",
+    "    model.train()\n",
+    "    print(device)\n",
+    "    for batch_idx, (data, target) in tqdm(enumerate(train_loader)):\n",
+    "        data, target = data.to(device), target.to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        output = model(data)\n",
+    "        loss = F.nll_loss(output, target)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fd843896-03b1-4cb7-a5c0-b420bba2cff9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test(model, device, test_loader):\n",
+    "    \"\"\"Model evaluating function\"\"\"\n",
+    "    model.eval()\n",
+    "    test_loss = 0\n",
+    "    correct = 0\n",
+    "    with torch.no_grad():\n",
+    "        for data, target in test_loader:\n",
+    "            data, target = data.to(device), target.to(device)\n",
+    "            output = model(data)\n",
+    "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
+    "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
+    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
+    "\n",
+    "    test_loss /= len(test_loader.dataset)\n",
+    "\n",
+    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
+    "        test_loss, correct, len(test_loader.dataset),\n",
+    "        100. * correct / len(test_loader.dataset)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f47b42fe-4905-48fe-ad88-7f903e281ea1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test(model, device, test_loader):\n",
+    "    model.eval()\n",
+    "    test_loss = 0\n",
+    "    correct = 0\n",
+    "    # Use the no_grad method to increase computation speed\n",
+    "    # since computing the gradient is not necessary in this step.\n",
+    "    with torch.no_grad():\n",
+    "        for data, target in test_loader:\n",
+    "            data, target = data.to(device), target.to(device)\n",
+    "            output = model(data)\n",
+    "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
+    "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
+    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
+    "\n",
+    "    test_loss /= len(test_loader.dataset)\n",
+    "\n",
+    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
+    "        test_loss, correct, len(test_loader.dataset),\n",
+    "        100. * correct / len(test_loader.dataset)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a4d951f1-ade1-44be-93f1-de53051109eb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "{\n",
-    " \"cells\": [\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 1,\n",
-    "   \"id\": \"5596f562-286a-4e39-aa35-723f210a84f3\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"import torch\\n\",\n",
-    "    \"import torch.nn as nn\\n\",\n",
-    "    \"import torch.nn.functional as F\\n\",\n",
-    "    \"from torch.utils.data import TensorDataset\\n\",\n",
-    "    \"import torch.optim as optim\\n\",\n",
-    "    \"import torchvision\\n\",\n",
-    "    \"from torchvision import datasets\\n\",\n",
-    "    \"import torchvision.transforms as transforms\\n\",\n",
-    "    \"import matplotlib.pyplot as plt\\n\",\n",
-    "    \"from tqdm import tqdm\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 2,\n",
-    "   \"id\": \"5f8981b6-5194-4dce-891a-febe26d26ac3\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n",
-    "    \"print(device)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 3,\n",
-    "   \"id\": \"8462da36-52db-4fdb-8e0d-4b78aa5d2435\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"data\": {\n",
-    "      \"text/plain\": [\n",
-    "       \"'NVIDIA A100-SXM4-80GB MIG 1g.20gb'\"\n",
-    "      ]\n",
-    "     },\n",
-    "     \"execution_count\": 3,\n",
-    "     \"metadata\": {},\n",
-    "     \"output_type\": \"execute_result\"\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"torch.cuda.get_device_name(0)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 4,\n",
-    "   \"id\": \"afe4c059-c3dc-4a57-a657-a72fdf573b66\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"False , cpu\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"X_train = torch.IntTensor([0, 30, 50, 75, 70])\\n\",\n",
-    "    \"print(X_train.is_cuda, \\\",\\\", X_train.device)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 5,\n",
-    "   \"id\": \"eda6746c-6c18-4350-888c-8651abad09af\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"True , cuda:0\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"X_train = X_train.cuda()\\n\",\n",
-    "    \"# Alternative method: specify the device using the variable\\n\",\n",
-    "    \"# X_train = X_train.to(device)\\n\",\n",
-    "    \"# Confirm that the Tensor is on the GPU now\\n\",\n",
-    "    \"print(X_train.is_cuda, \\\",\\\", X_train.device)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 6,\n",
-    "   \"id\": \"fa4778e3-4b9f-417f-bfc5-c317fc517066\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"batch_size = 100\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"class SimpleNet(nn.Module):\\n\",\n",
-    "    \"    def __init__(self):\\n\",\n",
-    "    \"        super(SimpleNet, self).__init__()\\n\",\n",
-    "    \"        self.fc1 = nn.Linear(784, 784)\\n\",\n",
-    "    \"        self.fc2 = nn.Linear(784, 10)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"    def forward(self, x):\\n\",\n",
-    "    \"        x = x.view(batch_size, -1)\\n\",\n",
-    "    \"        x = self.fc1(x)\\n\",\n",
-    "    \"        x = F.relu(x)\\n\",\n",
-    "    \"        x = self.fc2(x)\\n\",\n",
-    "    \"        output = F.softmax(x, dim=1)\\n\",\n",
-    "    \"        return output\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 7,\n",
-    "   \"id\": \"d4ad8baa-9e75-4c69-aca3-dd2fd82e0c38\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"model = SimpleNet().to(device)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 8,\n",
-    "   \"id\": \"0b9f3e4c-d4d7-4894-b118-7fa10e220a58\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"train_transform=transforms.Compose([\\n\",\n",
-    "    \"        transforms.ToTensor(),\\n\",\n",
-    "    \"        transforms.Normalize((0.1307,), (0.3081,))\\n\",\n",
-    "    \"        ])\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"test_transform=transforms.Compose([\\n\",\n",
-    "    \"        transforms.ToTensor(),\\n\",\n",
-    "    \"        transforms.Normalize((0.1307,), (0.3081,)),\\n\",\n",
-    "    \"        ])\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Set up a training data set\\n\",\n",
-    "    \"trainset = datasets.FashionMNIST('./data', train=True, download=True,\\n\",\n",
-    "    \"                  transform=train_transform)\\n\",\n",
-    "    \"train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\\n\",\n",
-    "    \"                                          shuffle=False, num_workers=2)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Set up a test data set\\n\",\n",
-    "    \"testset = datasets.FashionMNIST('./data', train=False,\\n\",\n",
-    "    \"                  transform=test_transform)\\n\",\n",
-    "    \"test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\\n\",\n",
-    "    \"                                        shuffle=False, num_workers=2)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 9,\n",
-    "   \"id\": \"bcbdb9d0-2129-4ac5-a189-08489f19a63a\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"data\": {\n",
-    "      \"image/png\": 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\",\n",
-    "      \"text/plain\": [\n",
-    "       \"
        \"\n",
-    "      ]\n",
-    "     },\n",
-    "     \"metadata\": {},\n",
-    "     \"output_type\": \"display_data\"\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"labels_map = {\\n\",\n",
-    "    \"    0: \\\"T-Shirt\\\",\\n\",\n",
-    "    \"    1: \\\"Trouser\\\",\\n\",\n",
-    "    \"    2: \\\"Pullover\\\",\\n\",\n",
-    "    \"    3: \\\"Dress\\\",\\n\",\n",
-    "    \"    4: \\\"Coat\\\",\\n\",\n",
-    "    \"    5: \\\"Sandal\\\",\\n\",\n",
-    "    \"    6: \\\"Shirt\\\",\\n\",\n",
-    "    \"    7: \\\"Sneaker\\\",\\n\",\n",
-    "    \"    8: \\\"Bag\\\",\\n\",\n",
-    "    \"    9: \\\"Ankle Boot\\\",\\n\",\n",
-    "    \"}\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"# Plotting 9 random different items from the training data set, trainset.\\n\",\n",
-    "    \"figure = plt.figure(figsize=(8, 8))\\n\",\n",
-    "    \"for i in range(1, 3 * 3 + 1):\\n\",\n",
-    "    \"    sample_idx = torch.randint(len(trainset), size=(1,)).item()\\n\",\n",
-    "    \"    img, label = trainset[sample_idx]\\n\",\n",
-    "    \"    figure.add_subplot(3, 3, i)\\n\",\n",
-    "    \"    plt.title(labels_map[label])\\n\",\n",
-    "    \"    plt.axis(\\\"off\\\")\\n\",\n",
-    "    \"    plt.imshow(img.view(28,28), cmap=\\\"gray\\\")\\n\",\n",
-    "    \"plt.show()\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 10,\n",
-    "   \"id\": \"9ef4f746-f342-4adf-bce6-cfdef0c8a1df\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"def train(model, device, train_loader, optimizer, epoch):\\n\",\n",
-    "    \"    \\\"\\\"\\\"Model training function\\\"\\\"\\\"\\n\",\n",
-    "    \"    model.train()\\n\",\n",
-    "    \"    print(device)\\n\",\n",
-    "    \"    for batch_idx, (data, target) in tqdm(enumerate(train_loader)):\\n\",\n",
-    "    \"        data, target = data.to(device), target.to(device)\\n\",\n",
-    "    \"        optimizer.zero_grad()\\n\",\n",
-    "    \"        output = model(data)\\n\",\n",
-    "    \"        loss = F.nll_loss(output, target)\\n\",\n",
-    "    \"        loss.backward()\\n\",\n",
-    "    \"        optimizer.step()\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 11,\n",
-    "   \"id\": \"fd843896-03b1-4cb7-a5c0-b420bba2cff9\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"def test(model, device, test_loader):\\n\",\n",
-    "    \"    \\\"\\\"\\\"Model evaluating function\\\"\\\"\\\"\\n\",\n",
-    "    \"    model.eval()\\n\",\n",
-    "    \"    test_loss = 0\\n\",\n",
-    "    \"    correct = 0\\n\",\n",
-    "    \"    with torch.no_grad():\\n\",\n",
-    "    \"        for data, target in test_loader:\\n\",\n",
-    "    \"            data, target = data.to(device), target.to(device)\\n\",\n",
-    "    \"            output = model(data)\\n\",\n",
-    "    \"            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\\n\",\n",
-    "    \"            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\\n\",\n",
-    "    \"            correct += pred.eq(target.view_as(pred)).sum().item()\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"    test_loss /= len(test_loader.dataset)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"    print('\\\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n",
-    "    \"        test_loss, correct, len(test_loader.dataset),\\n\",\n",
-    "    \"        100. * correct / len(test_loader.dataset)))\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 12,\n",
-    "   \"id\": \"f47b42fe-4905-48fe-ad88-7f903e281ea1\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"def test(model, device, test_loader):\\n\",\n",
-    "    \"    model.eval()\\n\",\n",
-    "    \"    test_loss = 0\\n\",\n",
-    "    \"    correct = 0\\n\",\n",
-    "    \"    # Use the no_grad method to increase computation speed\\n\",\n",
-    "    \"    # since computing the gradient is not necessary in this step.\\n\",\n",
-    "    \"    with torch.no_grad():\\n\",\n",
-    "    \"        for data, target in test_loader:\\n\",\n",
-    "    \"            data, target = data.to(device), target.to(device)\\n\",\n",
-    "    \"            output = model(data)\\n\",\n",
-    "    \"            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\\n\",\n",
-    "    \"            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\\n\",\n",
-    "    \"            correct += pred.eq(target.view_as(pred)).sum().item()\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"    test_loss /= len(test_loader.dataset)\\n\",\n",
-    "    \"\\n\",\n",
-    "    \"    print('\\\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n",
-    "    \"        test_loss, correct, len(test_loader.dataset),\\n\",\n",
-    "    \"        100. * correct / len(test_loader.dataset)))\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 13,\n",
-    "   \"id\": \"a4d951f1-ade1-44be-93f1-de53051109eb\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"EPOCHS = 5\\n\",\n",
-    "    \"# our optimization strategy used in training.\\n\",\n",
-    "    \"optimizer = optim.Adadelta(model.parameters(), lr=0.01)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 14,\n",
-    "   \"id\": \"c6072415-652e-4fb7-a0e4-57467ae23d23\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"EPOCH: 1\\n\",\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"600it [00:06, 89.07it/s]\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\",\n",
-    "      \"Test set: Average loss: -0.5645, Accuracy: 6205/10000 (62%)\\n\",\n",
-    "      \"\\n\",\n",
-    "      \"EPOCH: 2\\n\",\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"600it [00:06, 91.25it/s]\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\",\n",
-    "      \"Test set: Average loss: -0.6295, Accuracy: 6881/10000 (69%)\\n\",\n",
-    "      \"\\n\",\n",
-    "      \"EPOCH: 3\\n\",\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"600it [00:06, 94.15it/s]\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\",\n",
-    "      \"Test set: Average loss: -0.6669, Accuracy: 7086/10000 (71%)\\n\",\n",
-    "      \"\\n\",\n",
-    "      \"EPOCH: 4\\n\",\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"600it [00:06, 91.84it/s]\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\",\n",
-    "      \"Test set: Average loss: -0.6833, Accuracy: 7143/10000 (71%)\\n\",\n",
-    "      \"\\n\",\n",
-    "      \"EPOCH: 5\\n\",\n",
-    "      \"cuda:0\\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stderr\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"600it [00:06, 90.96it/s] \\n\"\n",
-    "     ]\n",
-    "    },\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"\\n\",\n",
-    "      \"Test set: Average loss: -0.6974, Accuracy: 7196/10000 (72%)\\n\",\n",
-    "      \"\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"for epoch in range(1, EPOCHS + 1):\\n\",\n",
-    "    \"        print( f\\\"EPOCH: {epoch}\\\")\\n\",\n",
-    "    \"        train(model, device, train_loader, optimizer, epoch)\\n\",\n",
-    "    \"        test(model, device, test_loader)\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 15,\n",
-    "   \"id\": \"b3fb979c-ce84-4657-996e-8509912581de\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": [\n",
-    "    \"# Saving the model's weights!\\n\",\n",
-    "    \"torch.save(model.state_dict(), \\\"mnist_fashion_SimpleNet.pt\\\")\"\n",
-    "   ]\n",
-    "  },\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": null,\n",
-    "   \"id\": \"4cbda6a9-b468-45f6-a346-3f86fa2e9214\",\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [],\n",
-    "   \"source\": []\n",
-    "  }\n",
-    " ],\n",
-    " \"metadata\": {\n",
-    "  \"kernelspec\": {\n",
-    "   \"display_name\": \"Python 3 (ipykernel)\",\n",
-    "   \"language\": \"python\",\n",
-    "   \"name\": \"python3\"\n",
-    "  },\n",
-    "  \"language_info\": {\n",
-    "   \"codemirror_mode\": {\n",
-    "    \"name\": \"ipython\",\n",
-    "    \"version\": 3\n",
-    "   },\n",
-    "   \"file_extension\": \".py\",\n",
-    "   \"mimetype\": \"text/x-python\",\n",
-    "   \"name\": \"python\",\n",
-    "   \"nbconvert_exporter\": \"python\",\n",
-    "   \"pygments_lexer\": \"ipython3\",\n",
-    "   \"version\": \"3.11.8\"\n",
-    "  }\n",
-    " },\n",
-    " \"nbformat\": 4,\n",
-    " \"nbformat_minor\": 5\n",
-    "}"
+    "EPOCHS = 5\n",
+    "# our optimization strategy used in training.\n",
+    "optimizer = optim.Adadelta(model.parameters(), lr=0.01)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c6072415-652e-4fb7-a0e4-57467ae23d23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPOCH: 1\n",
+      "cuda:0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "600it [00:06, 89.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test set: Average loss: -0.5645, Accuracy: 6205/10000 (62%)\n",
+      "\n",
+      "EPOCH: 2\n",
+      "cuda:0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "600it [00:06, 91.25it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test set: Average loss: -0.6295, Accuracy: 6881/10000 (69%)\n",
+      "\n",
+      "EPOCH: 3\n",
+      "cuda:0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "600it [00:06, 94.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test set: Average loss: -0.6669, Accuracy: 7086/10000 (71%)\n",
+      "\n",
+      "EPOCH: 4\n",
+      "cuda:0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "600it [00:06, 91.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test set: Average loss: -0.6833, Accuracy: 7143/10000 (71%)\n",
+      "\n",
+      "EPOCH: 5\n",
+      "cuda:0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "600it [00:06, 90.96it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test set: Average loss: -0.6974, Accuracy: 7196/10000 (72%)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(1, EPOCHS + 1):\n",
+    "        print( f\"EPOCH: {epoch}\")\n",
+    "        train(model, device, train_loader, optimizer, epoch)\n",
+    "        test(model, device, test_loader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3fb979c-ce84-4657-996e-8509912581de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving the model's weights!\n",
+    "torch.save(model.state_dict(), \"mnist_fashion_SimpleNet.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cbda6a9-b468-45f6-a346-3f86fa2e9214",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 5
 }