In this document, you can see how to launch different software in the Open OnDemand (OOD) Remote Desktop app (available at rcood.rc.fas.harvard.edu)

You can launch the Remote Desktop app on the Cannon cluster from rcood.rc.fas.harvard.edu and on the FASSE cluster from fasseood.rc.fas.harvard.edu.

When the Remote Desktop app opens, click the terminal icon to launch a terminal (or click Applications -> Terminal Emulator). Below, you can follow the instructions to launch various software.

Keep in mind that, for the most part, the terminal window must remain open. If the terminal window is closed, the software launched via the terminal will also be closed.

Training Session: FASRC Open On Demand Users Training

Remote Desktop login

To comply with Harvard’s security policy, if the Remote Desktop session becomes idle, the Remote Desktop session will lock. You need to enter your FASRC password to log back in.

Abaqus

In the terminal, type the commands to load the modules and launch Abaqus

[jharvard@holy7c24102 ~]$ module load abaqus
[jharvard@holy7c24102 ~]$ export LANG=en_US
[jharvard@holy7c24102 ~]$ abaqus cae -mesa cpus=$SLURM_CPUS_PER_TASK &

You can see all versions of Abaqus with module spider abaqus. For more details, see the modules page.

The Abaqus license is restricted to SEAS. For more information, see our Abaqus docs.

Comsol

In the terminal, type the commands to load the modules and launch Comsol

[jharvard@holy7c24102 ~]$ module load comsol
[jharvard@holy7c24102 ~]$ export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
[jharvard@holy7c24102 ~]$ comsol -3drend sw -np $SLURM_CPUS_PER_TASK &

You can see all versions of Comsol with module spider comsol. For more details, see the modules page.

The Comsol license is restricted to SEAS. For more information, see our Comsol docs.

For how to set the Comsol temporary directory, see our Comsol Troubleshooting doc.

Jupyter Notebook

(optional) Creating and loading a mamba/conda environment

Note: this is a one-time setup to ensure that your conda environment can be loaded in Jupyter Notebook.

See our Python documentation on how to create a conda environment.

Then, in order to see your conda environment in Jupyter Notebook, ensure that you have installed the packages ipykernel and nb_conda_kernels. To do so, launch a terminal in the Remote Desktop and type the commands:

[jharvard@holy7c24102 ~]$ module load python
[jharvard@holy7c24102 ~]$ source activate my_conda_environment
[jharvard@holy7c24102 ~]$ mamba install ipykernel
[jharvard@holy7c24102 ~]$ mamba install nb_conda_kernels

For more information on creating conda environments for TensorFlow and PyTorch, see our GitHub documentation:

• TensorFlow
• PyTorch

You can see all versions of Python with module spider python. For more details, see the modules page.

Launching Jupyter Notebook

In the Remote Desktop terminal, type the commands to load the modules and launch Jupyter Notebook:

[jharvard@holy7c24102 ~]$ module load python
# (optional) load conda environment
[jharvard@holy7c24102 ~]$ source activate my_conda_environment
# launch jupyter notebook
[jharvard@holy7c24102 ~]$ jupyter notebook

After the jupyter notebook command, it may hang for a few seconds. Be patient, a Firefox window will open soon after.

To select my_conda_environment as the kernel, go to Kernel -> Change kernel, and select the kernel (i.e. conda environment) of your choice.

Note: If you prefer to launch Jupyter Lab, note that conda environments cannot be loaded when using Jupyter Lab. Only the base environment is available.

Cleanly close Jupyter Notebook

These are instructions to kill your Jupyter server and so you can exit the job cleanly.

First, close each Jupyter Notebook you have open: click on File -> Close and Halt.

Then, from the Jupyter Notebook Home Page (where you can browse files and folders), on the top right corner, click on “Quit”. Close the Firefox window.

KNIME

In the terminal, type the following commands to load the module and launch Knime.

[jharvard@holy7c24102 ~]$ module load knime
[jharvard@holy7c24102 ~]$ knime &

You can see all versions of KNIME with module spider knime. For more details, see the modules page.

LibreOffice

LibreOffice is a free and open source suite that is compatible with a wide range of formats, including those from Microsoft Word (.doc, .docx), Excel (.xls, .xlsx), PowerPoint (.ppt, .pptx) and Publisher.

LibreOffice is available in the FASRC cluster (both Cannon and FASSE) through a Singularity image. Therefore, LibreOffice is only available through the Remote Desktop app. LibreOffice does not work in the Containerized Remote Desktop app.

In the terminal type the commands to pull and create a singularity image with LibreOffice installed within the container. This command is only needed once.

[jharvard@holy7c24102 ~]$ singularity pull docker://linuxserver/libreoffice

To launch LibreOffice, in the terminal, run the command

[jharvard@holy7c24102 ~]$ singularity exec --cleanenv --env DISPLAY=$DISPLAY libreoffice_latest.sif soffice

Lumerical

In the terminal, type the commands to load the modules and launch Lumerical

[jharvard@holy7c24102 ~]$ module load lumerical-seas
[jharvard@holy7c24102 ~]$ launcher

The Lumerical license is restricted to SEAS. For more information, see our Lumerical docs.

You can see all versions of Lumerical with module spider lumerical. For more details, see the modules page.

Mathematica

In the terminal, type the commands to load the modules and launch Mathematica

[jharvard@holy7c24102 ~]$ module load mathematica
[jharvard@holy7c24102 ~]$ mathematica

You can see all versions of Mathematica with module spider mathematica. For more details, see the modules page.

Matlab

In the terminal, type the commands to load the modules and launch Matlab

[jharvard@holy7c24102 ~]$ module load matlab
[jharvard@holy7c24102 ~]$ matlab -desktop -softwareopengl

You can see all versions of Matlab with module spider matlab . For more details, see the modules page.

MOE

In the terminal, type the commands to load the modules and launch MOE

[jharvard@holy7c24102 ~]$ module load moe
[jharvard@holy7c24102 ~]$ moe

You can see all versions of MOE with module spider moe . For more details, see the modules page.

RStudio Desktop

In the terminal, type the commands to load modules

[jharvard@holy7c24102 ~]$ module load R
[jharvard@holy7c24102 ~]$ module load rstudio

Set environmental variables

[jharvard@holy7c24102 ~]$ unset R_LIBS_SITE
[jharvard@holy7c24102 ~]$ mkdir -p $HOME/apps/R_version
[jharvard@holy7c24102 ~]$ export R_LIBS_USER=$HOME/apps/R_version:$R_LIBS_USER

Launch RStudio Desktop

[jharvard@holy7c24102 ~]$ rstudio

# vanilla option (combines --no-save, --no-restore, --no-site-file, --no-init-file and --no-environ)
[jharvard@holy7c24102 ~]$ rstudio --vanila

You can see all versions of R and RStudio with module spider R and module spider rstudio, respectively. For more details, see the modules page.

Remoteviz Partition

If you have used the “FAS-RC Remote Visualization” Open OnDemand (or VDI) app, we have decommissioned it.

SageMath

You can use sage wither in a interactive shell using command line interface or by launching a Jupyter Notebook with the SageMath kernel. To launch a Jupyter Notebook, in the terminal, type the commands to load the modules and launch Jupyter

[jharvard@holy7c24102 ~]$ module load sage
[jharvard@holy7c24102 ~]$ sage -n jupyter

Ensure that you have “SageMath” kernel selected. If not, go to Kernel -> Change kernel, and select SageMath.

For example, see Sage documentation:

• interactive shell examples
• Jupyter Notebook examples

You can see all versions of SageMath with module spider sage. For more details, see the modules page.

SAS

In the terminal, type the commands to load the modules and launch SAS

[jharvard@holy7c24102 ~]$ module load sas
[jharvard@holy7c24102 ~]$ sas &

Stata

In the terminal, type the commands to load the module and launch Stata

[jharvard@holy7c24102 ~]$ module load stata/17.0-fasrc01

# if you are using single-core jobs
[jharvard@holy7c24102 ~]$ xstata-se

# if you are using multi-core jobs
[jharvard@holy7c24102 ~]$ xstata-mp "set processors $SLURM_CPUS_PER_TASK"

TensorBoard

For TensorBoard, you will first need to create a conda environment (Step 1). You only need to create a conda environment once. If you have created one, you can skip to Step 2. Or, if you have your own environment, make sure you install the TensorBoard package, and then you can skip to Step 2.

Step 1: Create conda environment

In a terminal, load Mambaforge or Python module, create a mamba environment, activate it, and install TensorBoard inside the mamba environment

[jharvard@holy7c24102 ~]$ module load python
[jharvard@holy7c24102 ~]$ module load cuda/11.7.1-fasrc01
[jharvard@holy7c24102 ~]$ module load cudnn/8.5.0.96_cuda11-fasrc01
[jharvard@holy7c24102 ~]$ conda create -n tb_tf2.10_cuda11 python=3.10 pip numpy six wheel scipy pandas matplotlib seaborn h5py jupyterlab
[jharvard@holy7c24102 ~]$ source activate tb_tf2.10_cuda11
[jharvard@holy7c24102 ~]$ conda install -c conda-forge tensorboard
[jharvard@holy7c24102 ~]$ conda install -c conda-forge tensorflow

You can see different versions of Mambaforge or Python in our modules page.

Step 2: Activate conda environment and launch TensorBoard

In a terminal, setup variables for TensorBoard. Make sure that the data you need to visualize in Tensorboard is located in the log directory MY_TB_LOGDIR. You can either use the suggested path below or use somewhere else that better suits your workflow.

# Find available port to run server on (does not output anything to screen)
[jharvard@holy7c24102 ~]$ for myport in {6818..11845}; do ! nc -z localhost ${myport} && break; done

# setup tensorboard environmental variables
[jharvard@holy7c24102 ~]$ export MY_TB_PORT=${myport}
[jharvard@holy7c24102 ~]$ export MY_TB_BASEURL=/node/${host}/${myport}/
[jharvard@holy7c24102 ~]$ export MY_TB_LOGDIR=$HOME/.tensorboard/log/$SLURM_JOBID
[jharvard@holy7c24102 ~]$ mkdir -p $MY_TB_LOGDIR

# load module, activate conda environment, and launch tensorboard
[jharvard@holy7c24102 ~]$ module load python
[jharvard@holy7c24102 ~]$ module load cuda/11.7.1-fasrc01
[jharvard@holy7c24102 ~]$ module load cudnn/8.5.0.96_cuda11-fasrc01
[jharvard@holy7c24102 ~]$ source activate tb_tf2.10_cuda11 
(tb_tf2.10_cuda11) tensorboard --host localhost --port ${MY_TB_PORT} --logdir ${MY_TB_LOGDIR} --path_prefix ${MY_TB_BASEURL}

You can see different versions of Mambaforge or Python in our modules page.

Right-click on the link that starts with “http://localhost” and click on “Open Link”. This will open a Firefox browser, where you can view your results.

Example

Using the environment created in Step 1, run the small program tb_test.py in a directory of your choice and visualize its results.

Source code of tb_test.py:

import os
import tensorflow as tf
import datetime

def create_model():
    return tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

logdir = os.getenv('MY_TB_LOGDIR')
print(logdir)

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1)
model.fit(x=x_train, 
          y=y_train, 
          epochs=5, 
          validation_data=(x_test, y_test), 
          callbacks=[tensorboard_callback])

Setup variables and run tb_test.py

# Find available port to run server on (does not output anything to screen)
[jharvard@holy7c24102 tb_example]$ for myport in {6818..11845}; do ! nc -z localhost ${myport} && break; done

# go to the directory that you have your tb_test.py file
[jharvard@holy7c24102 ~]$ cd tb_example

# setup tensorboard environmental variables
[jharvard@holy7c24102 tb_example]$ export MY_TB_PORT=${myport}
[jharvard@holy7c24102 tb_example]$ export MY_TB_BASEURL=/node/${host}/${myport}/

# this command will set MY_TB_LOGDIR to your current working directory
[jharvard@holy7c24102 tb_example]$ export MY_TB_LOGDIR=$PWD

# load modules and activate conda environment
[jharvard@holy7c24102 tb_example]$ module load python
[jharvard@holy7c24102 tb_example]$ module load cuda/11.7.1-fasrc01
[jharvard@holy7c24102 tb_example]$ module load cudnn/8.5.0.96_cuda11-fasrc01
[jharvard@holy7c24102 tb_example]$ source activate tb_tf2.10_cuda11

# run python code
(tb_tf2.10_cuda11) python tb_test.py

# launch tensorboard
(tb_tf2.10_cuda11) tensorboard --host localhost --port ${MY_TB_PORT} --logdir ${MY_TB_LOGDIR} --path_prefix ${MY_TB_BASEURL}

Right click on the link that starts with “http://localhost” and click on “Open Link”. This will open a Firefox browser where you will be able to see your results.

TotalView

TotalView is a debugging tool particularly suitable for parallel applications. The modules you need to load depend on the compilers used in the code you are trying to debug. Due to this compiler dependency, we refer you to a more elaborate TotalView documentation.

Visual Studio Code

In the terminal, type the commands to load the modules and launch Visual Studio Code

[jharvard@holy7c24102 ~]$ module load vscode
[jharvard@holy7c24102 ~]$ code --user-data-dir $HOME/.vscode/data/ &

You can see all versions of Visual Studio Code with module spider vscode. For more details, see the modules page.

Spyder is available through Anaconda Navigator on the FAS-RC Remote Desktop application on the Open OnDemand dashboard. To access it:

• go to the VDI dashboard at https://vdi.rc.fas.harvard.edu/pun/sys/dashboard/ (or https://fasseood.rc.fas.harvard.edu/pun/sys/dashboard if you are working on FASSE)
• start a new Remote Desktop session
• connect to the Remote Desktop session
• open a Terminal in the Remote Desktop session and load the relevant Anaconda module. Check https://portal.rc.fas.harvard.edu/p3/build-reports/ for the most up-to-date modules.
  • Examples:
  • if you’re working with Python 2, type the following:
    • module load Anaconda/5.0.1-fasrc02
  • if you’re working with Python 3, type the following:
    • module load Anaconda3/2020.11
• type the following:
  • anaconda-navigator
• when Anaconda Navigator loads, you may choose to remain in the “base” environment, or load your preferred environment from the “Applications on” dropdown, or create a new environment using the Environments tab to the left.
• when you are in the environment you need, click the Launch button under the icon for Spyder.

Screenshots of the process:

Overview

The FASRC Open OnDemand dashboard rcood.rc.fas.harvard.edu provides a browser based interface to the cluster using Open OnDemand.
From OpenOnDemand you will be able to :

• browse your files
• check the status of your jobs
• submit new batch jobs
• run remote desktop sessions as part of a compute job on our remote_desktop dedicated partition
• run other interactive applications – for example Rstudio sessions, Matlab, or Jupyter notebooks

To access the dashboard you will need to be logged to the FASRC VPN. If you did not request Cluster Access when signing up, you will not have a home directory or the ability to initiate jobs. You can Request to add cluster access via the dashboard .

How to connect to the OOD dashboard :

1. 1. Make sure you are connected to the FASRC VPN.
      Note: When connecting to the VPN, cluster users should connect to the VPN using the @fasrc realm (ex. – jharvard@fasrc), while FASSE (including NCF) users please make sure you are in the @fasse realm (ex. – jharvard@fasse). If you have been instructed to use a specific realm, please try that realm first.
   2. Point your browser to https://rcood.rc.fas.harvard.edu
      Note: NCF and other FASSE users should use  https://fasseood.rc.fas.harvard.edu
      Browser Compatiblity: Safari and Internet Explorer might not work as expected for some of the apps in the dashboard . We recommend to use Chrome or Firefox
   3. Enter your FASRC credentials into the authentication form.
   4. Upon successful authentication you will land on the main dashboard.
      Please Note: that the first time you log in to the Open OnDemand dashboard , it might take some time to return while it sets up files and cache in your home directory.

Quick Tour of the Dashboard:

On the dashboard you will see displayed a link to our status page as well as a message showing the current overall status, pointers to our documentation and other important information, and at the top several menus to access the different Apps for File Browsing, Job management and Interactive Computing.

• Files:
  The File Menu contains a link to open in another tab a File browser that will show you files in your Home folder.
  
• Jobs:
  This Menu offers two Apps:
  Active Jobs  will allow you to check the status of your current cluster jobs, if you are also running batch jobs.
  Job Composer will allow you to edit and submit new clusterbatch jobs via the OOD dashboard .
• Clusters:
  The App “FAS-RC Shell Access” will allow you to open a shell terminal in one of the login nodes.
  This is named Clusters as in future you may have access to more than one cluster.
  Note: you might be prompted for password and two-factor code.
• Interactive Apps:
  This is the Menu where all the Apps for Interactive sessions are contained.
  A detailed description of the currently available Apps can be found at this page.
  FASSE users should refer instead to this documentation 
• Each app will have an initial setup screen where you request time, number of cores, memory, cluster partition where the job will run, etc. For information on Partitions, time, CPU/Memory, and other SLURM (job scheduler) parameters, please see:
  Running Jobs – Partitions
  Running Jobs – General Please note that the exact content of the menu might vary depending on your user profile and what software you have access to.
  Clicking on each of the Apps will bring you to a form to schedule the job and connect to the remote session directly in your browser.
• My Interactive sessions:
  You will find here all your currently open interactive Apps and you will be able to reconnect to the sessions from here.
  
• Help:
  This menu provides links to contact user support and to reset your FASRC password.
  You will also be able to restart the web server that serves your dashboard (only needed for developers and advanced users)
  

The Clipboard Pop-Out

For some applications, such as the Remote Desktop or Matlab, for instance, you will not be able to copy/paste from and to your OOD session as expected. If your session uses the remote desktop interface to run your app/session, there will be a blue pop-out drawer on the left of the screen. This pop-out includes a Clipboard widget that will allow you to copy/paste in that session. Single screen apps such as Rstudio or Jupyter should allow direct copy/paste.