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 on the terminal icon to launch a terminal (or click on Applications -> Terminal Emulator). Below you can follow the instruction to launch various software.

Keep in mind that, for the most part, the terminal window needs to stay open. If the terminal window is closed, the software that you launched via terminal will be closed too.

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 different versions of Abaqus in our 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 ~]$ comsol -3drend sw -np $SLURM_CPUS_PER_TASK &

You can see different versions of Comsol in our modules page.

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

For how to set 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 different versions of Mambaforge or Python in our 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.

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.

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 different versions of Matlab in our modules page.

ParaView

In the terminal, type the commands to load the modules and launch ParaView.

[jharvard@holy7c24102 ~]$ module load GCC/9.3.0 OpenMPI/4.0.3 ParaView/5.8.0-Python-3.8.2-mpi
[jharvard@holy7c24102 ~]$ unset SESSION_MANAGER
[jharvard@holy7c24102 ~]$ paraview

You can see different versions of ParaView in our modules page.

PyCharm

In the terminal, type the commands to load the modules and launch ParaView.

# load necessary modules
[jharvard@holy7c24102 ~]$ module python
[jharvard@holy7c24102 ~]$ module load pycharm-community

# (optional) activate a conda environment before launching PyCharm
[jharvard@holy7c24102 ~]$ source active my_conda_env

# launch PyCharm
[jharvard@holy7c24102 ~]$ pycharm.sh &

Note that the first time you launch PyCharm it may take several minutes (10-30 min) to load all settings.

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

Loading PyCharm IDE configuration

If you have used PyCharm in the cluster before and have saved an IDE configuration, you can load load it into PyCharm.

When PyCharm launches, you may get a window entitled “Import PyCharm Settings”:

Click on the folder on the right corner as indicated by the arrow. Navigate to ~/.config/JetBrains/ and select the file.

If you skipped the initial configuration setup, you can still load a configuration. After PyCharm opens, go to File -> Manage IDE Settings -> Import Settings. Then choose the following path: ~/.config/JetBrains/<product><version>, where <product> and <version> could be something like ~/.config/JetBrains/PyCharmCD2020.1

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 different versions of R and RStudio in our modules page.

Remoteviz Partition

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

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 examples, see Sage documentation:

• interactive shell examples
• Jupyter Notebook examples

SAS

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

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

You can see different versions of SAS in our modules page.

Stata

In the terminal type the commands to load the modules 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 enviroment (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 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 will be able to see 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 different versions of Visual Studio Code in our 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 different versions of Mathematica in our 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:

• Remote Desktop: This app allows the user to schedule a Remote Desktop job and opens a browser based viewer to connect to it
• Jupyter notebook: The app schedules a notebook session on one of the compute nodes and opens a connection to it in a new tab.
• RStudio Server: The app schedules a rstudio server session on one of the compute nodes and opens a connection to it in a new tab.
• Matlab: The app schedules a rstudio server session on one of the compute nodes and opens a connection to it in a new tab. You can run single-node codes in parallel using the parallel pool. See our Github User_Codes page for an Matlab parallel example on VDI.
• Other: Additionally, we offer Tensorboard and other applications as they are requested or become available. You will also likely see customized versions of apps for FAS Informatics, CGA, or others. These are available to you but will likely be customized for specific uses unlike the standard versions listed further down.  Please note that the list below may look different depending on your department (example: COMSOL is only available to SEAS).

RCOOD Remote Desktop

The Remote Desktop app will schedule a job on one of the nodes in the dedicated remote desktop partition, start a vnc server (TurboVNC), and provide a link to connect to in via the browser based app noVNC.  No client is needed to connect to it, but if instead of using the web based client you prefer to use a traditional VNC client to connect to your session, please see Using a VNC Client to Connect at the bottom of this page.  To launch the session, please select the “Remote Desktop” entry from the “Interactive Apps” menu, and fill in the following form. In the form you will be able to select:

– the memory allocated for the job (Max 16GB)
– number of cores (Max 8)
– allocated time (Max 7days).

In addition, for your convenience, you may chose to create icons on the desktop to launch Matlab and RStudio, selecting the versions you prefer.  Once the remote desktop starts, you can always open a terminal, load the modules you need and start the software from the command line.  Selecting the checkbox “Custom Desktop Folder” will create a job specific desktop, which might be useful in case in different jobs you want launcher icons with different versions of Matlab or RStudio.

If you want to be notified by email when the job starts, please select the checkbox and include a valid email address in the form.  Once you have completed your sections,  press the “Launch” button.  Allow a few seconds for the system to process your submission form, and you will be redirected to the “My Interactive Sessions” page.

You will be able to click on “Launch noVNC in new Tab” to open the connection to the Remote Desktop directly in your browser.  The Remote Desktop runs the lightweight “Xfce” desktop.

Here some notes related to performance and usability within remote desktop type session:
Note: When running a Remote Desktop or app that uses the desktop (ex. Matlab), the noVNC client does not support sharing clipboard and direct “Copy and paste” between your computer and the remote session in noVNC. noVNC provides a “staging clipboard” that you can use to copy to and from the remote session.  Copy things to that clipboard first, and then you can copy to your remote session.

Note: The default resolution of the remote session is 1024×768 and by default the resolution will not adjust to client display size. That is to avoid to resize the remote server to very large resolution in case you are connected to a large screen external display, and avoid performance degradation. If you chose to, you can rescale the remote clicking on the setting “wheel” and select “scaling mode = remote resizing”

Note: The noVNC web client is very convenient as it does not require you to install a client to connect, but it does not support all features in terms of compression and performance optimization that TurboVNC client or another client would. If instead of using the web based client you prefer to use a traditional VNC client to connect to your session, please see later in this doc.

Note: If you close the noVNC tab or the connection times out due to long inactivity, you will be always able to reconnect to your session clicking again on the “Launch noVNC in new Tab” in the “My Interactive Sessions” page . Your job is controlled by Slurm and will keep running until the allocated time, or until you cancel the job.

Note: If you want to terminate your job and your Remote Desktop session, you can simply click the “Delete” button on the “My Interactive Sessions”  tab in the section corresponding to that particular job.

Internal Web Browser Version

Those who have launched Firefox on the Remote Desktop will notice that it is a very old version.  This is due to the OS we run on the cluster being intended for HPC use not for Desktop use, and thus not including the latest, greatest libraries and software but instead choosing stable, well proven software.  This model unfortunately does not work well for web browsers which need constant updating to fix security issues and to keep up with the latest features.  This has led browsers to have users install them directly rather than via the normal packaging system which Linux uses.  Also most browsers assume that users have full control over the system that they are installing them on, it was never conceived that some one would want to open a browser in a browser on a HPC cluster.

While the above reasons mean that we cannot easily provide a modern browser on the cluster, there is a way to get one if you need to open a browser on the Remote Desktop.  Firefox provides a tarball with a fully functional browser that you can download here.  Simply select the Linux 64-bit release.  Once you download the tarball you can unzip it using tar -jxvf packagename, after that run the firefox executable it unpacks.  That should then give you a modern browser you can use on the remote desktop.

Jupyter Notebook

This App allows you to submit a job which starts a Jupyter notebook on a compute node and provides a link to connect to it on a new browser tab. To launch the notebook, please select the “Jupyter notebook” entry from the “Interactive Apps” menu, and fill the form.  In the form you will be able to select :

– partition
– memory allocated for the job (Max 120GB)
– number of cores (Max 32)
– number of GPUs. Use this only if selecting a GPU enabled partition.
– allocated time (Max allowed depends on partition).

If you want to be notified by email when the job starts, please select the checkbox and include a valid email address in the form.  The field “Slurm Account” can be use to specify which lab account you want to charge in terms of FairShare for this particular job.  If you only are affiliated to one lab you please ignore that entry, as it will default to your primary group.

When you made your selection press the “Launch” button.  Allow a few seconds for system to process your submission form, and you will be redirected to the “My Interactive Sessions” page.

You will be able to use the “Connect to Jupyter” button to open the notebook in a new tab on your browser.

How do I access the python and R kernels installed in my conda environments?

Jupyter can see your local mamba/conda environments that include the ipykernel and nb_conda_kernels packages. When you access the notebook, in the dropdown menu “New” under the “Files” tab, you will see all the mamba/conda environments that you have in your home folder which have the ipykernel and nb_conda_kernels modules installed. For example in this use case it detects that the user has access, in addition to the central Python 3 environment, to two other conda environment, named python2.7 and tf1.10_cuda9

To enable a mamba environment in Jupyter install the ipykernel and nb_conda_kernels packages into the environment by doing:

mamba install ipykernel

mamba install nb_conda_kernels

After that is installed you should see the environment in new Jupyter sessions.

How do I access a conda environment located in a lab shared folder from Jupyter notebook VDI app?

Warning: When sharing conda environment, we highly recommend that only one person install/uninstall/update packages because the environment may break if multiple users change the conda environment.

When a conda environment is created, its path is added to the the file ~/.conda/environments.txt. People that would like to use a conda environment created by someone else need to add the conda environment path to ~/.conda/environments.txt.

For example, jharvard creates an environment in a shared lab space and installs ipykernel:

# request interactive node
[jharvard@boslogin01 conda_envs]$ salloc -p test --time=1:00:00 --mem=4000

# load modules
[jharvard@holy7c24604 conda_envs]$ module load Anaconda3/2020.11

# create shared conda environment
[jharvard@holy7c24604 conda_envs]$ conda create --prefix /n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env python=3.10

# launch conda environment and install ipykernel
[jharvard@holy7c24604 conda_envs]$ source activate /n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env
(/n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env) [jharvard@holy7c24604 conda_envs]$ conda install ipykernel

# environment path is automatically added to ~/.conda/environments.txt
(/n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env) [jharvard@holy7c24604 conda_envs]$ cat ~/.conda/environments.txt
/n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env

Now, user1 has to add the same environment path to their ~/.conda/environments.txt file:

[user1@boslogin01 ~]$ echo "/n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env" >> ~/.conda/environments.txt
[user1@boslogin01 ~]$ cat ~/.conda/environments.txt
/n/holylabs/LABS/jharvard_lab/Lab/conda_envs/my_shared_env

Now, user1 will be able to select my_shared_env in Jupyter notebook VDI app as explained above.

Can I control my conda environment from the Jupyter notebook page?

An extension of Jupyter (nb_conda) allows you to manage your conda environment from the notebook.  Navigate to the tab “conda” and you will see the list of your environment.  You can click on the environment you want to inspect or modify, and easily install or remove packages. For example, remember that to see your conda environments in your “new” drop down menu in the file tab, the environments need to have the ipykernel package installed. If you don’t have it installed, you can do it from this interface.  In this example I am installing “pandas” in the environment named “python2.7”

How do I load additional modules that my kernels need?

If you need to load modules before starting your kernel – for example if you need to load cuda and cudnn before starting a kernel with tensorflow – you can do that directly from the notebook.  First navigate to the tab “Modules” and start entering the name of the module you need to load. The list of the available modules that match what you are typing will popup. Select the module you need (in this example we are loading cuda 9.0)
Press “enter” and allow a few seconds for the load operation to complete (similarly to what happens in the interactive sessions loading modules can take a moment). Upon successful loading, the module will appear in the list of loaded modules.  If you need to unload that you can click on the “Unload” yellow button.

Repeat for all desired modules, and after you are all set, you can go back to the “File” tab and start your desired kernel.

Note: When you are done running your computation you can delete the job using the “Delete” button in the section for that particular job in the  “My Interactive Sessions” page.

Troubleshooting

Jupyter notebook VDI session is terminated right after it starts

This problem is common when there is a conda initialize section in your .bashrc file located in your home directory (more about .bashrc). The conda initialize section was added when, at some point, you used the command conda init. We strongly discourage the use of conda init. Instead use source activate environment_name, for more details, refer to our Python (Anaconda) page.

To solve this problem, delete or comment out the conda initialize section of your .bashrc and create a new Jupyter notebook VDI session.

Jupyter notebook/JupyterLab VDI session starts but does not display a ‘Connect to Jupyter’ button

If this problem occurs, you may see an error, jupyter: command not found, in

 ~/.fasrcood/data/sys/dashboard/batch_connect/sys/Jupyter/output/<sessionID>/output.log

On FASSE:

~/.fasseood/data/sys/dashboard/batch_connect/sys/Jupyter/output/<sessionID>/output.log

To solve this problem, delete the line auto_activate_base: false in the file ~/.condarc.

Matlab

This App allows you to submit a job which starts a Matlab session on a compute node and provides a link to connect to it on a new browser tab. To launch the notebook, select the “Matlab” entry from the “Interactive Apps” menu, and fill in the following form.

– partition (see partitions page for core, memory, and time limits on public partitions)
– memory allocated for the job
– number of cores
– number of hours

Refer to our Github User_Codes for an example on how to run a parallel code on VDI.

RStudio Server

This app allows you to submit a job which starts a RStudio Server session  on a compute node and provides a link to connect to it on a new browser tab. To launch the notebook, select the “RStudio Server” entry from the “Interactive Apps” menu, and fill in the following form.

– partition (see partitions page for core, memory, and time limits on public partitions)
– memory allocated for the job
– number of cores
– number of hours

You can then select the version of R you want to run in your Rsession in RStudio.

If you want to be notified by email when the job starts select the checkbox and include a valid email address in the form. The “Slurm Account” can be use to specify which lab account you want to charge in terms of FairShare for this particular job. If you only are affiliated to one lab you can ignore that entry as it will default to your primary group.

When you made your selection press the “Launch” button. Allow a few seconds for the system to process your submission form, and you will be redirected to the “My Interactive Sessions” page.

You will be able to use the “Connect to RStudio Server” button to open the RStudio session in a new tab on your browser.

Note: When you are done running your computation you can delete the job using the “Delete” button in the section related to that particular job in the “My Interactive Sessions” page.

FASRC also has RStudio Desktop that can be launched from the Remote Desktop app. See the following docs for more information

• How to launch RStudio Desktop
• RStudio Server vs. RStudio Desktop

Sign in to RStudio error

In very rare instances, you may come across a prompt to sign in to RStudio. If you are running RStudio Server and get a sign in prompt asking for Username and Password:

Go to the Open OnDemand dashboard (Cannon, FASSE). Then click on “Interactive Session” on the top menu and then click on the button “Connect to RStudio Server”. This will reopen the RStudio Server session.

Using a VNC Client to Connect

No client is needed to connect to to a session, but if instead of using the web based client you prefer to use a traditional VNC client (we highly recommend TurboVNC) to connect to your session, please click on My Interactive Sessions and the VNC Desktop Client tab for instructions.