Accessing the machines, intro

Communication:

Links for the complimentary parts of this course:

• Zoom
• Slack
• JSC Training Page
• Judoor project page invite
• This document: https://go.fzj.de/bringing-dl-workloads-to-jsc
• Our mailing list for AI news
• Survey at the end of the course
• Virtual Environment template
• SOURCE of the course/slides on Github

Goals for this course:

• Make sure you know how to access and use our machines 👩‍💻
• Put your data in way that supercomputer can use it fast 💨
• Distribute your ML workload 💪
• Important: This is NOT a basic AI course 🙇‍♂️
  • If you need one, check fast.ai

Team:

Schedule for day 1

Note

Please open this document on your own browser! We will need it for the exercises. https://go.fzj.de/bringing-dl-workloads-to-jsc

Jülich Supercomputers

What is a supercomputer?

• Compute cluster: Many computers bound together locally
• Supercomputer: A damn lot of computers bound together locally😒
  • with a fancy network 🤯

Anatomy of a supercomputer

• Login Nodes: Normal machines, for compilation, data transfer, scripting, etc. No GPUs.
• Compute Nodes: Guess what?
  • For compute! With GPUs! 🤩
• High-speed, ultra-low-latency network
• Shared networked file systems
• Some numbers we should (more or less) know about them:
  • Nodes
  • Cores, Single-core Performance
  • RAM
  • Network: Bandwidth, Latency
  • Accelerators (e.g. GPUs)

JURECA DC Compute Nodes

• 192 Accelerated Nodes (with GPUs)
• 2x AMD EPYC Rome 7742 CPU 2.25 GHz (128 cores/node)
• 512 GiB memory
• Network Mellanox HDR infiniband (FAST💨 and EXPENSIVE💸)
• 4x NVIDIA A100 with 40gb 😻
• TL;DR: 24576 cores, 768 GPUs 💪
• Way deeper technical info at Jureca DC Overview

How do I use a Supercomputer?

• Batch: For heavy compute, ML training
• Interactively: Jupyter

You don’t use the whole supercomputer

You submit jobs to a queue asking for resources

You don’t use the whole supercomputer

And get results back

You don’t use the whole supercomputer

You are just submitting jobs via the login node

You don’t use the whole supercomputer

You are just submitting jobs via the login node

You don’t use the whole supercomputer

You are just submitting jobs via the login node

You don’t use the whole supercomputer

You don’t use the whole supercomputer

And get results back

Supercomputer Usage Model

• Using the the supercomputer means submitting a job to a batch system.
• No node-sharing. The smallest allocation for jobs is one compute node (4 GPUs).
• Maximum runtime of a job: 24h.

Recap:

• Login nodes are for submitting jobs, move files, compile, etc
• NOT FOR TRAINING NEURAL NETS

Recap:

• User submit jobs
• Job enters the queue
• When it can, it runs
• Sends results back to user

Connecting to Jureca DC

Getting compute time

• Go to https://go.fzj.de/bringing-dl-workloads-to-jsc-project-join
• Join the course project training2449
• Sign the Usage Agreements (Video)
• Compute time allocation is based on compute projects. For every compute job, a compute project pays.
• Time is measured in core-hours. One hour of Jureca DC is 128 core-hours.
• Example: Job runs for 8 hours on 64 nodes of Jureca DC: 8 * 128 * 48 = 65536 core-h!

Jupyter

jupyter-jsc.fz-juelich.de

• Jupyter-JSC uses the queue
• When you are working on it, you are using project time ⌛️
• Yes, if you are just thinking and looking at the 📺, you are burning project time🤦‍♂️
• It’s useful for small tests - not for full-fledged development 🙄

Jupyter

Pay attention to the partition - DON’T RUN IT ON THE LOGIN NODE!!!

Connecting to Jureca DC

VSCode

• Download VScode: code.visualstudio.com
• Install and run it
  • On the local terminal, type code
• Install Remote Development Tools
• Install Remote: SSH
• If you have Windows, you need WSL as explained on the email.

VSCode

Now with the remote explorer tab

SSH

• SSH is a secure shell (terminal) connection to another computer
• You connect from your computer to the LOGIN NODE
• Security is given by public/private keys
• A connection to the supercomputer needs a
  1. Key,
  2. Configuration
  3. Key/IP address known to the supercomputer

SSH

Create key in VSCode’s Terminal (menu View->Terminal)

mkdir ~/.ssh/
ssh-keygen -a 100 -t ed25519 -f ~/.ssh/id_ed25519-JSC

$ ssh-keygen -a 100 -t ed25519 -f ~/.ssh/id_ed25519-JSC
Generating public/private ed25519 key pair.
Enter passphrase (empty for no passphrase): 
Enter same passphrase again: 
Your identification has been saved in /Users/strube1/.ssh/id_ed25519-JSC
Your public key has been saved in /Users/strube1/.ssh/id_ed25519-JSC.pub
The key fingerprint is:
SHA256:EGNNC1NTaN8fHwpfuZRPa50qXHmGcQjxp0JuU0ZA86U strube1@Strube-16
The keys randomart image is:
+--[ED25519 256]--+
|      *++oo=o. . |
|     . =+o .= o  |
|      .... o.E..o|
|       .  +.+o+B.|
|        S  =o.o+B|
|          . o*.B+|
|          . . =  |
|           o .   |
|            .    |
+----[SHA256]-----+

SSH

Configure SSH session

code $HOME/.ssh/config

Windows users, from Ubuntu WSL (Change username for your user on windows)

ls -la /mnt/c/Users/
mkdir /mnt/c/Users/USERNAME/.ssh/
cp $HOME/.ssh/* /mnt/c/Users/USERNAME/.ssh/

SSH

Configure SSH session

Host jureca
        HostName jureca.fz-juelich.de
        User [MY_USERNAME]   # Here goes your username, not the word MY_USERNAME.
        AddressFamily inet
        IdentityFile ~/.ssh/id_ed25519-JSC
        MACs hmac-sha2-512-etm@openssh.com

Copy contents to the config file and save it

REPLACE [MY_USERNAME] WITH YOUR USERNAME!!! 🤦‍♂️

SSH

JSC restricts from where you can login

So we need to:

1. Find our ip range
2. Add the range and key to Judoor

SSH

Find your ip/name range

Open https://www.whatismyip.com

SSH

Find your ip/name range

• Let’s keep this inside vscode: code key.txt and paste the number you got

SSH

Did everyone get their own ip address?

SSH - EXAMPLE

• I will use the number 93.199.55.163
• YOUR NUMBER IS DIFFERENT
• Seriously

SSH - Example: 93.199.55.163

• Go to VSCode and make it simpler, replace the 2nd half with "0.0/16":
  • It was 93.199.55.163
  • Becomes 93.199.0.0/16 (with YOUR number, not with the example)
• Add a from="" around it
• So, it looks like this, now: from="93.199.0.0/16"
• Add a second magic number, with a comma: ,10.0.0.0/8 🧙‍♀️
• I promise, the magic is worth it 🧝‍♂️ (If time allows)
• In the end it looks like this: from="93.199.0.0/16,10.0.0.0/8" 🎬
• Keep it open, we will use it later
• If you are from FZJ, also add “134.94.0.0/16” with a comma

SSH - Example: 93.199.0.0/16

Copy your ssh key

• Terminal: code ~/.ssh/id_ed25519-JSC.pub

• Something like this will open:

• ssh-ed25519 AAAAC3NzaC1lZDE1NTA4AAAAIHaoOJF3gqXd7CV6wncoob0DL2OJNfvjgnHLKEniHV6F strube@demonstration.fz-juelich.de

• Paste this line at the same key.txt which you just opened

SSH

Example: 93.199.0.0/16

• Put them together and copy again:

• from="93.199.0.0/16,10.0.0.0/8" ssh-ed25519 AAAAC3NzaC1lZDE1NTA4AAAAIHaoOJF3gqXd7CV6wncoob0DL2OJNfvjgnHLKEniHV6F strube@demonstration.fz-juelich.de

SSH

• Let’s add it on Judoor
• Do it for JURECA and JUDAC with the same key

SSH

Add new key to Judoor

This might take some minutes

SSH: Exercise

That’s it! Give it a try (and answer yes)

$ ssh jureca
The authenticity of host 'jrlogin03.fz-juelich.de (134.94.0.185)' cannot be established.
ED25519 key fingerprint is SHA256:ASeu9MJbkFx3kL1FWrysz6+paaznGenChgEkUW8nRQU.
This key is not known by any other names
Are you sure you want to continue connecting (yes/no/[fingerprint])? Yes
**************************************************************************
*                            Welcome to Jureca DC                   *
**************************************************************************
...
...
strube1@jrlogin03~ $ 

SSH: Exercise

Make sure you are connected to the supercomputer

# Create a folder for myself
mkdir $PROJECT_training2449/$USER

# Create a shortcut for the project on the home folder
rm -rf ~/course ; ln -s $PROJECT_training2449/$USER ~/course

# Enter course folder and
cd ~/course

# Where am I?
pwd

# We well need those later
mkdir ~/course/.cache
mkdir ~/course/.config
mkdir ~/course/.fastai

rm -rf $HOME/.cache ; ln -s ~/course/.cache $HOME/
rm -rf $HOME/.config ; ln -s ~/course/.config $HOME/
rm -rf $HOME/.fastai ; ln -s ~/course/.fastai $HOME/

Working with the supercomputer’s software

• We have literally thousands of software packages, hand-compiled for the specifics of the supercomputer.
• Full list
• Detailed documentation

Software

Tool for finding software: module spider

strube1$ module spider PyTorch
------------------------------------------------------------------------------------
  PyTorch:
------------------------------------------------------------------------------------
    Description:
      Tensors and Dynamic neural networks in Python with strong GPU acceleration. 
      PyTorch is a deep learning framework that puts Python first.

     Versions:
        PyTorch/1.7.0-Python-3.8.5
        PyTorch/1.8.1-Python-3.8.5
        PyTorch/1.11-CUDA-11.5
        PyTorch/1.12.0-CUDA-11.7
     Other possible modules matches:
        PyTorch-Geometric  PyTorch-Lightning
...

What do we have?

module avail (Inside hierarchy)

Module hierarchy

• Stage (full collection of software of a given year)

• Compiler

• MPI

• Module

• Eg: module load Stages/2023 GCC OpenMPI PyTorch

What do I need to load such software?

module spider Software/version

Example: PyTorch

Search for the software itself - it will suggest a version

Example: PyTorch

Search with the version - it will suggest the hierarchy

Example: PyTorch

(make sure you are still connected to Jureca DC)

$ python
Python 3.9.18 (main, Jan 24 2024, 00:00:00)  
[GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'torch'

Oh noes! 🙈

Let’s bring Python together with PyTorch!

Example: PyTorch

Copy and paste these lines

# This command fails, as we have no proper pytorch
python -c "import torch ; print(torch.__version__)" 
# So, we load the correct modules...
module load Stages/2024
module load GCC OpenMPI Python PyTorch
# And we run a small test: import pytorch and ask its version
python -c "import torch ; print(torch.__version__)" 

Should look like this:

$ python -c "import torch ; print(torch.__version__)" 
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'torch'
$ module load Stages/2024
$ module load GCC OpenMPI Python PyTorch
$ python -c "import torch ; print(torch.__version__)" 
2.1.2

Python Modules

Some of the python softwares are part of Python itself, or of other softwares. Use “module key”

module key toml
The following modules match your search criteria: "toml"
------------------------------------------------------------------------------------

  Jupyter: Jupyter/2020.2.5-Python-3.8.5, Jupyter/2021.3.1-Python-3.8.5,
    Jupyter/2021.3.2-Python-3.8.5, Jupyter/2022.3.3, Jupyter/2022.3.4
    Project Jupyter exists to develop open-source software, open-standards,
    and services for interactive computing across dozens of programming languages.
    

  PyQuil: PyQuil/3.0.1
    PyQuil is a library for generating and executing Quil programs on the Rigetti
    Forest platform.

  Python: Python/3.8.5, Python/3.9.6, Python/3.10.4
    Python is a programming language that lets you work more quickly and integrate 
    your systems more effectively.

------------------------------------------------------------------------------------

VSCode

Editing files on the supercomputers

VSCode

VSCode

• You can have a terminal inside VSCode:
  • Go to the menu View->Terminal

VSCode

• From the VSCode’s terminal, navigate to your “course” folder and to the name you created earlier.

• cd $HOME/course/
  pwd

• This is out working directory. We do everything here.

Demo code

Create a new file “matrix.py” on VSCode on Jureca DC

code matrix.py

Paste this into the file:

import torch

matrix1 = torch.randn(3,3)
print("The first matrix is:\n", matrix1)

matrix2 = torch.randn(3,3)
print("The second matrix is:\n", matrix2)

result = torch.matmul(matrix1,matrix2)
print("The result is:\n", result)

How to run it on the login node

module load Stages/2024 
module load GCC OpenMPI Python PyTorch 
python matrix.py

But that’s not what we want… 😒

So we send it to the queue!

HOW?🤔

SLURM 🤯

Simple Linux Utility for Resource Management

Slurm submission file

• Simple text file which describes what we want and how much of it, for how long, and what to do with the results

Slurm submission file example

code jureca-matrix.sbatch

#!/bin/bash
#SBATCH --account=training2449           # Who pays?
#SBATCH --nodes=1                        # How many compute nodes
#SBATCH --job-name=matrix-multiplication
#SBATCH --ntasks-per-node=1              # How many mpi processes/node
#SBATCH --cpus-per-task=1                # How many cpus per mpi proc
#SBATCH --output=output.%j        # Where to write results
#SBATCH --error=error.%j
#SBATCH --time=00:01:00          # For how long can it run?
#SBATCH --partition=dc-gpu         # Machine partition
#SBATCH --reservation=training2449_day1 # For today only

module load Stages/2024
module load GCC OpenMPI PyTorch  # Load the correct modules on the compute node(s)

srun python matrix.py            # srun tells the supercomputer how to run it

Submitting a job: SBATCH

sbatch jureca-matrix.sbatch

Submitted batch job 412169

Are we there yet?

Are we there yet? 🐴

squeue --me

squeue --me
   JOBID  PARTITION    NAME      USER    ST       TIME  NODES NODELIST(REASON)
   412169 gpus         matrix-m  strube1 CF       0:02      1 jsfc013

ST is status:

• PD (pending),
• CF(configuring),
• R (running),
  
• CG (completing)

Reservations

• Some partitions have reservations, which means that only certain users can use them at certain times.
• For this course, it’s called training2449_day1

Job is wrong, need to cancel

scancel <JOBID>

Check logs

By now you should have output and error log files on your directory. Check them!

# Notice that this number is the job id. It's different for every job
cat output.412169 
cat error.412169 

Or simply open it on VSCode!

Extra software, modules and kernels

You want that extra software from pip….

venv/Kernel template

cd $HOME/course/
git clone https://gitlab.jsc.fz-juelich.de/kesselheim1/sc_venv_template.git

Example: Let’s install some software!

• Even though we have PyTorch, we don’t have PyTorch Lightning Flash
• Same for fast.ai and wandb
• We will install them in a virtual environment

Example: Let’s install some software!

• Edit the file sc_venv_template/requirements.txt

• Add these lines at the end:

• fastai==2.7.15
  scipy==1.11.1
  matplotlib==3.7.2
  scikit-learn==1.3.1
  pandas==2.0.3
  torch==2.1.2
  accelerate==1.1.1
  pyarrow==18.1.0
  tqdm==4.67.1
  transformers==4.46.3
  sentencepiece==0.2.0
  datasets==3.1.0
  torchrun_jsc==0.0.10

• Run on the terminal: sc_venv_template/setup.sh

Example: Activating the virtual environment

• source sc_venv_template/activate.sh

Example: Activating the virtual environment

source sc_venv_template/activate.sh 
The activation script must be sourced, otherwise the virtual environment will not work.
Setting vars
The following modules were not unloaded:
  (Use "module --force purge" to unload all):
 1) Stages/2024

jureca01 $ python
Python 3.11.3 (main, Jun 25 2023, 13:17:30) [GCC 12.3.0]
>>> import fastai
>>> fastai.__version__
'2.7.14'

Let’s train a 🐈 classifier!

• This is a minimal demo, to show some quirks of the supercomputer

• code cats.py

• from fastai.vision.all import *
  from fastai.callback.tensorboard import *
  #
  print("Downloading dataset...")
  path = untar_data(URLs.PETS)/'images'
  print("Finished downloading dataset")
  #
  def is_cat(x): return x[0].isupper()
  # Create the dataloaders and resize the images
  dls = ImageDataLoaders.from_name_func(
      path, get_image_files(path), valid_pct=0.2, seed=42,
      label_func=is_cat, item_tfms=Resize(224))
  print("On the login node, this will download resnet34")
  learn = vision_learner(dls, resnet34, metrics=accuracy)
  cbs=[SaveModelCallback(), TensorBoardCallback('runs', trace_model=True)]
  # Trains the model for 6 epochs with this dataset
  learn.unfreeze()
  learn.fit_one_cycle(6, cbs=cbs)

Submission file for the classifier

code fastai.sbatch

#!/bin/bash
#SBATCH --account=training2449
#SBATCH --mail-user=MYUSER@fz-juelich.de
#SBATCH --mail-type=ALL
#SBATCH --nodes=1
#SBATCH --job-name=cat-classifier
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=128
#SBATCH --output=output.%j
#SBATCH --error=error.%j
#SBATCH --time=00:20:00
#SBATCH --partition=dc-gpu
#SBATCH --reservation=training2449_day1 # For today only

cd $HOME/course/
source sc_venv_template/activate.sh # Now we finally use the fastai module

srun python cats.py

Submit it

sbatch fastai.sbatch

Submission time

• Check error and output logs, check queue

Probably not much happening…

• $ cat output.7948496 
  The activation script must be sourced, otherwise the virtual environment will not work.
  Setting vars
  Downloading dataset...

• $ cat err.7948496 
  The following modules were not unloaded:
  (Use "module --force purge" to unload all):
  
  1) Stages/2024

💥

What happened?

• It might be that it’s not enough time for the job to give up
• Check the error.${JOBID} file
• If you run it longer, you will get the actual error:

• Traceback (most recent call last):
    File "/p/project/training2449/strube1/cats.py", line 5, in <module>
      path = untar_data(URLs.PETS)/'images'
      ...
      ...
      raise URLError(err)
  urllib.error.URLError: <urlopen error [Errno 110] Connection timed out>
  srun: error: jwb0160: task 0: Exited with exit code 1

🤔…

What is it doing?

• This downloads the dataset:

• path = untar_data(URLs.PETS)/'images'

• And this one downloads the pre-trained weights:

• learn = vision_learner(dls, resnet34, metrics=accuracy)

Remember, remember

Remember, remember

Compute nodes have no internet connection

• But the login nodes do!
• So we download our dataset before…
  • On the login nodes!

On the login node:

• Comment out the line which does AI training:

• # learn.fit_one_cycle(6, cbs=cbs)

• Call our code on the login node!

• source sc_venv_template/activate.sh # So that we have fast.ai library
  python cats.py

Run the downloader on the login node

$ source sc_venv_template/activate.sh
$ python cats.py 
Downloading dataset...
 |████████-------------------------------| 23.50% [190750720/811706944 00:08<00:26]
 Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to /p/project/ccstao/cstao05/.cache/torch/hub/checkpoints/resnet34-b627a593.pth
100%|█████████████████████████████████████| 83.3M/83.3M [00:00<00:00, 266MB/s]

Run it again on the compute nodes!

• Un-comment back the line that does training:

• learn.fit_one_cycle(6, cbs=cbs)

• Submit the job!

• sbatch fastai.sbatch

Masoquistically waiting for the job to run?

watch squeue --me

(To exit, type CTRL-C)

Check output files

• You can see them within VSCode

• The activation script must be sourced, otherwise the virtual environment will not work.
  Setting vars
  Downloading dataset...
  Finished downloading dataset
  epoch     train_loss  valid_loss  error_rate  time    
  Epoch 1/1 : |-----------------------------------| 0.00% [0/92 00:00<?]
  Epoch 1/1 : |-----------------------------------| 2.17% [2/92 00:14<10:35 1.7452]
  Epoch 1/1 : |█----------------------------------| 3.26% [3/92 00:14<07:01 1.6413]
  Epoch 1/1 : |██---------------------------------| 5.43% [5/92 00:15<04:36 1.6057]
  ...
  ....
  Epoch 1/1 :
  epoch     train_loss  valid_loss  error_rate  time    
  0         0.049855    0.021369    0.007442    00:42     

• 🎉
• 🥳

Tools for results analysis

• We already ran the code and have results
• To analyze them, there’s a neat tool called Tensorboard
• And we already have the code for it on our example!

• cbs=[SaveModelCallback(), TensorBoardCallback('runs', trace_model=True)]

Example: Tensorboard

• The command

• tensorboard --logdir=runs  --port=9999 serve

• Opens a connection on port 9999… OF THE SUPERCOMPUTER.
• This port is behind the firewall. You can’t access it directly…
• We need to bypass the firewall 🏴‍☠️
  • SSH PORT FORWARDING

Example: Tensorboard

Port Forwarding

Port forwarding demo:

• On VSCode’s terminal:

• cd $HOME/course/
  source sc_venv_template/activate.sh
  tensorboard --logdir=runs  --port=12345 serve

• Note the tab PORTS next to the terminal
• On the browser: http://localhost:12345

Tensorboard on Jureca DC

Day 1 recap

As of now, I expect you managed to:

• Stay awake for the most part of this morning 😴
• Have your own ssh keys 🗝️🔐
• A working ssh connection to the supercomputers 🖥️
• Can edit and transfer files via VSCode 📝
• Submit jobs and read results 📫
• Access web services on the login nodes 🧙‍♀️
• Is ready to make great code! 💪

ANY QUESTIONS??

Feedback is more than welcome!

Helmholtz Blablador

Blablador

• Blablador is our Large Language Model inference server (eg. ChatGPT)
• It’s a service for the Helmholtz Association.
  • It’s fast, free and PRIVATE - I don’t record your conversations!
• Anyone here can use it

Blablador

VScode + Continue.dev

Obtaining a token

• Go to helmholtz codebase at http://codebase.helmholtz.cloud
• Log in with your email
• On the left side, click on your profile, and then on “Preferences”
• On “Access tokens”, click “Add new token”,
  • give it a name,
  • put an expiration date (max 1 year)
  • and choose “api” in the “scopes” section
• Click “Create Personal Access Token”
  • You will see a “………………………..” - copy this and save somewhere.

Blablador

Blablador on VSCode!

• Add continue.dev extension to VSCode
• On Continue, choose to add model, choose Other OpenAI-compatible API
• Click in Open Config.json at the end

Blablador: VScode + Continue.dev

• Inside config.json, add at the "models" section:

•     {
        "model": "AUTODETECT",
        "title": "Blablador",
        "apiKey": "ADD_BLABLADOR_TOKEN_HERE",
        "apiBase": "https://api.helmholtz-blablador.fz-juelich.de/v1",
        "provider": "openai"
      },

• REPLACE THE APIKEY WITH YOUR OWN TOKEN!!!!

Blablador on VSCode

• Click on the “Continue.dev extension on the left side of VSCode.
• Select some code from our exercises, select it and send it to continue with cmd-shift-L (or ctrl-shift-L)
• Ask it to add unit tests, for example.

Backup slides

There’s more!

• Remember the magic? 🧙‍♂️
• Let’s use it now to access the compute nodes directly!

Proxy Jump

Accessing compute nodes directly

• If we need to access some ports on the compute nodes

Proxy Jump - SSH Configuration

Type on your machine “code $HOME/.ssh/config” and paste this at the end:

# -- Compute Nodes --
Host *.jureca
        User [ADD YOUR USERNAME HERE]
        StrictHostKeyChecking no
        IdentityFile ~/.ssh/id_ed25519-JSC
        ProxyJump jureca

Proxy Jump: Connecting to a node

Example: A service provides web interface on port 9999

On the supercomputer:

srun --time=00:05:00 \
     --nodes=1 --ntasks=1 \
     --partition=dc-gpu \
     --account training2449 \
     --cpu_bind=none \
     --pty /bin/bash -i

bash-4.4$ hostname # This is running on a compute node of the supercomputer
jwb0002

bash-4.4$ cd $HOME/course/
bash-4.4$ source sc_venv_template/activate.sh
bash-4.4$ tensorboard --logdir=runs  --port=9999 serve

Proxy Jump

On your machine:

• ssh -L :3334:localhost:9999 jrc002i.jureca

• Mind the i letter I added at the end of the hostname

• Now you can access the service on your local browser at http://localhost:3334

Now that’s really the end! 😓