Introduction

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)

Step 1: Connect to the FASRC VPN (see VPN setup documentation)

Step 2: Launch the Remote Desktop app

• Cannon cluster
• FASSE cluster

Step 3: When the Remote Desktop app opens, click the terminal icon to launch a terminal (or click Applications -> Terminal Emulator).

Step 4: 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.

MOE databases

FASRC has MOE databases available in two locations:

1. Most of the MOE Auxiliary Databases are available to everyone with cluster access in /n/holylabs/rc_admin/Everyone/moe_databases:
2. Databases are also available in the $MOE/project folder. You can open them in File -> Open -> Type in the address bar $MOE/project.

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.