If you are looking for information on the PostgreSQL app (including PostGIS and pgvector) on Cannon Open OneDemand (OOD) please see this page on our github.

For persistent database hosting options please see virtual machines and virtual hosting.

FASRC has implemented two different Open OnDemand (OOD, formerly called VDI) applications for RStudio:

• RStudio Server through the OOD app “RStudio Server”
• RStudio Desktop through the OOD app “Remote Desktop” then launching RStudio Desktop

In this doc, we attempt to explain the major difference between the two.

RStudio Server

RStudio Server is our go-to RStudio app because it contains a wide range of precompiled R packages from bioconductor and rocker/tidyverse. This means that installing R packages in RStudio Server is pretty straightforward. Most times, it will be sufficient to simply:

> install.packages("package_name")

This simplicity was possible because RStudio Server runs inside a Singularity container, meaning that it does not use the host operating system (OS). For more information on Singularity, refer to our Singularity on the cluster docs.

Important notes:

• User-installed R libraries will be installed in ~/R/ifxrstudio/\<IMAGE_TAG\>
• This app contains many pre-compiled packages from bioconductor and rocker/tidyverse.
• FAS RC environment modules (e.g. module load) and Slurm (e.g. sbatch) are not accessible from this app.
• For the RStudio with environment module and Slurm support, go to our Open OnDemand page select Interactive Apps > Remote Desktop and refer to Open OnDemand Remote Desktop: How to open software

This app is useful for most applications, including multi-core jobs. However, it is not suitable for multi-node jobs. For multi-node jobs, the recommended app is RStudio Desktop.

Installing R packages in RStudio Server in the FASSE cluster

If you are using FASSE Open OnDemand and need to install R packages in RStudio Server, you will likely need to set the proxies as explained in our Proxy Settings documentation. Before installing packages, execute these two commands in RStudio Server:

> Sys.setenv(http_proxy="http://rcproxy.rc.fas.harvard.edu:3128")
> Sys.setenv(https_proxy="http://rcproxy.rc.fas.harvard.edu:3128")

Running as a batch or interactive job

The RStudio Server OOD app hosted on Cannon at rcood.rc.fas.harvard.edu and FASSE at fasseood.rc.fas.harvard.edu runs RStudio Server in a Singularity container (see Singularity on the cluster). The path to the Singularity image on both Cannon and FASSE clusters is the same:

/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_<VERSION>.sif

Where <VERSION> corresponds to the Bioconductor version listed in the “R version” dropdown menu. For example:

R 4.2.3 (Bioconductor 3.16, RStudio 2023.03.0)

uses the Singularity image:

/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif

As mentioned above, when using the RStudio Server OOD app, user-installed R packages by default go in:

~/R/ifxrstudio/RELEASE_<VERSION>

Batch job

The command-line invocation in a batch job would be, for example (this will run the R script myscript.R):

This is an example of a batch script named runscript.sh that executes R script myscript.R inside the Singularity container RELEASE_3_16:

#!/bin/bash
#SBATCH -c 1 # Number of cores (-c)
#SBATCH -t 0-01:00 # Runtime in D-HH:MM
#SBATCH -p test # Partition to submit to
#SBATCH --mem=1G # Memory pool for all cores (see also --mem-per-cpu)
#SBATCH -o myoutput_%j.out # File to which STDOUT will be written, %j inserts jobid
#SBATCH -e myerrors_%j.err # File to which STDERR will be written, %j inserts jobid

# set R packages and rstudio server singularity image locations
my_packages=${HOME}/R/ifxrstudio/RELEASE_3_16
rstudio_singularity_image="/n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif"

# run myscript.R using RStudio Server signularity image
singularity exec --cleanenv --env R_LIBS_USER=${my_packages} ${rstudio_singularity_image} Rscript myscript.R

To submit the job, execute the command:

sbatch runscript.sh

Interactive job

Or to run R interactively (this will launch an R shell that you can interact with) — not applicable to FASSE where interactive jobs are not allowed:

singularity exec --cleanenv --env R_LIBS_USER=$HOME/R/ifxrstudio/RELEASE_3_16 /n/singularity_images/informatics/ifxrstudio/ifxrstudio:RELEASE_3_16.sif R

RStudio Desktop

RStudio Desktop is a “bare” version of RStudio. Although it has some precompiled R packages, it is a much more limited list than the RStudio Server app.

RStudio Desktop runs on the host operating system (OS), the same environment as when you ssh to Cannon or FASSE.

This app is particularly useful to run multi-node applications because the you can specify the exact modules and packages that you need to load

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.

To enable your development environment:

1. Get to a shell interface: you could SSH to a login node, or connect to Open OnDemand on your cluster and use the Clusters menu item to get shell access, or start a Remote Desktop session and use the Terminal Emulator.
2. Create the dev directory
   1. Cannon: mkdir $HOME/.fasrcood/dev
   2. FASSE: mkdir $HOME/.fasseood/dev
3. This will make the Develop menu item appear in the upper right when you view the Open OnDemand interface
4. Now you can access your sandbox development environment: either click on the item in the Develop menu in the dashboard, or navigate in the shell to $HOME/.fasrcood/dev, create an app folder in there and start your development.                                     

Once your dev path is created, you can start working on applications there.

To get started, FASRC has some example apps, which (at time of writing) run on the Cannon cluster without any modification needed.

Remote Desktop
SAS
Matlab
RStudio
Jupyter

Have a look at these to get a sense of how the app repositories are structured and how they refer to resources.
SAS and Matlab run within a Remote Desktop session, while RStudio uses Singularity containers.

For examples of additional apps, OSC provides links to applications that are implemented in Open OnDemand there and at other contributing institutions: https://osc.github.io/ood-documentation/latest/install-ihpc-apps.html

To get a copy of an application to work with, navigate to your dev folder, then clone the Github repo of the app.
cd $HOME/.fasrcood/dev
git clone https://github.com/fasrc/ood-rstudio-rocker

The new app will appear when you go to the menu item Develop->My Sandbox Apps (Development).

If you’re on the FASSE cluster and want to be able to run your app, you’ll want to update form.yml.erb to use
cluster: "fasse"
and
bc_queue:
 value: "fasse"
or another appropriate partition: https://docs.rc.fas.harvard.edu/kb/fasse/#SLURM_and_Partitions

As an example of additional ways to specify container locations:
A previous version of the RStudio app listed the Singularity container location directly in the script file.
https://github.com/fasrc/fas-ondemand-rstudio/blob/master/template/script.sh.erb
This lists a location for the Singularity images that is not accessible on the Cannon or FASSE cluster. You will need to update the location to use the path for the directory of the container you have created.

Feel free to reach out to rchelp@rc.fas.harvard.edu or visit our office hours to help deploy a container or answer further questions.

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.

STATA, a powerful statistical software package, is widely used by researchers, analysts, and academics across various disciplines. Renowned for its versatility, STATA enables users to efficiently analyze, manage, and visualize data, making it an indispensable tool for both novice and advanced data practitioners. Its intuitive command syntax facilitates seamless data manipulation, regression analysis, time-series modeling, and more, empowering users to uncover meaningful insights from complex datasets. With its robust suite of features and user-friendly interface, STATA continues to be a cornerstone in statistical analysis and research methodology.

Usage

Open OnDemand

STATA can be run from Open OnDemand (OOD, formerly known as VDI) by clicking on Stata icon or choosing it from the Interactive Apps menu, and specifying your resource needs. Hit Launch, wait for the session to start, and click the “Launch Stata” button.

You can also launch Stata from the Remote Desktop app on OOD.

Output file permissions

Stata appears to override filesystem-level permissions structures such as file-ACLs.  In a test using stata/17.0-fasrc01 the .dta files produced by Stata were consistent with the user’s umask, despite default file-ACLs that should have created different effective permissions.  It appears as though Stata is modifying the permissions after writing the file (i.e. after the default file-ACLs have been applied).  The solution for this should be to set the desired umask in the Slurm submission script or on the command line prior to submitting the batch job (though a ‘umask’ command in the Slurm submission script would be preferable in most cases).

Running Stata on FASSE

For how to set the proxies on Stata, see our FASSE proxy documentation.

Troubleshooting

Stata I/O error

If you get the error:

I/O error writing .dta file
Usually such I/O errors are caused by the disk or file system being full.

This is because Stata writes temporary files to disk (instead of only storing on memory). The location that Stata writes temporary files to disk is set, by default, to \tmp. If \tmp‘s size is smaller than your combined datasets, then Stata will not have enough space to write temporary files.

Solution: set the environmental variable STATATMP to a directory that is large enough.

Stata stand-alone app (Open OnDemand)

You can avoid this error by increasing the value in the option “Minimum size of STATATMP in GB”. We recommend increasing to twice the size of the datasets that you are using.

If your total size is more than 68GB, check the size available on each partition on Cannon and FASSE (see last column “/scratch size (GB)”).

On FASSE, if you do not have access to a partition that has enough /scratch space, you can:

1. use partition serial_requeue (do note that serial_requeue jobs may be preempted)
2. use the Stata with Remote Desktop app (explained below), but instead of using /scratch, use /n/netscratch/PI_lab

Command line interface or Stata from Remote Desktop app

You must set STATATMP before launching Stata with:

export STATATMP=/scratch/$USER/stata_dir
mkdir -p $STATATMP

Alternatively, you can set to a lab share as well. However, local scratch has better performance.

Additionally, when you submit a job, you can add the slurm option --tmp to request that the local disk has a minimum size. For example, this requests a disk with at least 150GB:

#SBATCH --tmp=150G

Examples

You can find examples on how to run STATA in both serial and parallel modes, in the FASRC User Codes GitHub repository.

Resources

These are some external resources with many examples on how to use Stata:

• UCLA Advanced Research Computing
• Stata has multiple resources for tutorials
• Data Carpentry
• University of Winconsin-Madison Social Science Computing Cooperative