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This documentation includes guidance, instructions, general information about the Research IT Service managed Research Cluster. Researcher execute computing jobs on the cluster in containerized environments known as Docker “containers” which are essentially lightweight virtual machines, each assigned dedicated CPU, RAM, and GPU hardware, and each well isolated from other users’ processes. The Research Cluster uses the Kubernetes container management/orchestration system to route users’ containers onto compute nodes, monitors performance, and applies resource limits/quotas as appropriate.

Complex Machine Learning workflows are supported through terminal/SSH logins and a full Linux/Ubuntu CUDA development suite. Users may install additional library packages (e.g. conda/pip, CRAN) as needed, or can opt to replace the default environment entirely by launching their own custom Docker containers. High High-speed cluster-local storage houses workspaces and common training corpora (e.g. CIFAR, ImageNet).

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Getting Started

The are two ways in which to access the Research Cluster - via ssh or via the datahub.

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title	Accessing the Research Cluster via SSH

First, login via SSH to the “dsmlp-login.ucsd.edu" Linux server using your UC San Diego Active Directory (AD) username (with ‘@dsmlp-login.ucsd.edu’) and password. After logging in, you will be in a login node for the Research Cluster and should not perform any computation in the login node.

Login step-by-step guidance:

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Open command line interface - known as the 'Terminal' for MacOS and 'Command Prompt' for Windows.

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Getting Started

The are two ways in which to access the Research Cluster - via SSH or via the Datahub.

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title	Accessing the Research Cluster via SSH

First, login via SSH to the “researchcluster-login.ucsd.edu" Linux server login node using your UC San Diego Active Directory (AD) username (with ‘@researchcluster-login.ucsd.edu’) and password. After logging in, you will be in a login node for the Research Cluster and should not perform any computation in the login node.

Login step-by-step guidance:

Open command line interface - known as the 'Terminal' for MacOS and 'Command Prompt' for Windows.
Enter command ‘ssh ADusername@researchcluster-login.ucsd.edu'.
1. If the researchcluster-login.ucsd.edu login node is currently down/unavailable, you may TEMPORARILY use the “dsmlp-login.ucsd.edu” login node instead. The dsmlp-login.ucsd.edu login node is intended for instructional class work use by students as its first priority, so it’s recommended to always use the researchcluster-login.ucsd.edu for research work.

You may be asked a question after entering your username. Select 'yes’ to continue connecting.

Enter your password. Note: Your password will not display as you enter it.
A successful login will display your last login information. For example, ‘Last login: Thu Aug 3 10:25:19 2023 from 137.110.14.162’. Note that you are now in the Login Node.

IMPORTANT: DO NOT RUN JOBS IN THE LOGIN NODE. ! Jobs must only be run in a launched container. Follow the guidance in the next section (Launching a Container), before running your compute jobs.

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title	Accessing the Research Cluster via the datahub

Login page: https://datahub.ucsd.edu/hub/login .
- Enter UCSD email address and AD password. You will be sent a push to confirm via DUODuo.
Select the your chosen notebook environment.
- Research Cluster users will have a choice of multiple environments to select.
- If joining a PI/lab specific environment, your may only see the name of your PI/lab’s environment.
- The ‘public’ folder will include storage that is shared and where datasets can be stored for all to access.
Next, you’ll be directed to your environment and see the Jupyter Notebook interface.
Click ‘New’ and select the kernel you wills to start up.
When done using the datahub, select ‘logout’ to terminate kernels and end session.

Launching a Container

After signing into the login node, you can start a pod/container using launching a standard Research Cluster launch script or a customize container launch script.

Once started, containers are accessible in either a Bash Shell (command-line) or a Jupyter/Python Notebook environment. Users may access their Jupyter notebook by copying and pasting the launch script link provided by pasting the link in the browser address bar. This link will work as long as your container is active and will cease to work once you logout. Docker container image and CPU/GPU/RAM settings are all configurable - see the “Customization” and "Launch Script Command-line Options" sections below for more details.

Containers terminate when automatically when users exit the interactive shell.

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title	Standard launch scripts

The standard launch scripts are predefined meaning they have specific RAM and CPU (and/or GPU) configurations. Other launch scripts are available at /softwareopt/common64launch-sh/dsmlp/bin/ .

Launch Script	Description	#GPU	#CPU	RAM	Container Image(s)
launch-scipy-ml.sh	Python 3, PyTorch, TensorFlow	0	2	8	ucsdets/scipy-ml-notebook:2020.2.9
launch-scipy-ml-gpu.sh	Python 3, PyTorch, TensorFlow	1	4	16	ucsdets/scipy-ml-notebook:2020.2.9
launch-datascience.sh	Python 3, Datascience, R	0	2	8	ucsdets/datascience-notebook:2020.2-stable
launch-rstudio.sh	R-Studio	1	4	16	ucsdets/datascience-rstudio:latest

Standard images with ‘pytorch’ include the GNU Screen utility, which may be used to manage multiple terminal sessions in a window-like manner.

Web Interface Tool

The Research Cluster uses the web interface tool known as Jupyterhub Notebooks as an alternative graphical interface option for users who prefer computing in this type of interface rather than in the command line interface.

To access the web interface tool, users are directed to sign-in at https://datahub.ucsd.edu/ (or via selecting the login button up top).

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Expand

title	Launching a Jupyter Notebook

Click on the "Log In" button above, or visit https://datahub.ucsd.edu/ and sign in with your UC San Diego Google account and password.
(Note: only '@ucsd.edu' addresses are currently accepted, not departmental or divisional addresses such as '@eng.ucsd.edu' or '@physics.ucsd.edu'.)

Click the button.

Select a software and hardware configuration via the "Spawner options" page:

Open a blank Python 3 notebook:

When your work is complete, please shut down your Notebook via the Control Panel's "Stop my Server" option:

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title	Monitoring Resource Usage in the command line terminal

Users can view the container CPU and memory (RAM) utilization in the Bash command line interface by using the ‘htop’ command. To see GPU usage, enter the `/usr/local/nvidia/bin/nvidia-smi` command for a container that uses GPU.

Modifying Containers

Certain modifications can be made to containers to allow for users to adjust their environment to accommodate specific computing needs.

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Expand

title	Container Termination Messages

Containers may occasionally exit (or unexpectedly terminate) with one of the following error messages:

OOMKilled	Container memory (CPU RAM) limit was reached.
DeadlineExceeded	Container time limit (default 6 hours) exceeded - see above.
Error	Unspecified error. Contact ITS/ETS for assistance.

Note: These errors will show up in 'kubectl get pods' in the status column.

Data Storage / Datasets

There are two types of persistent file storage are available within containers - private/home directory and shared directory storage.

A private home directory ($HOME) for automatically generated for each cluster user. User's private home directory is limited to a 100GB storage allocation by default.
A shared directory - for group shared data or for datasets used to distribute common data (e.g. CIFAR-10, Tiny ImageNet) for individual access. Shared directory storage can vary as this storage may be a mounted storage.

In specific cases, Research IT may make allowances to temporarily increase storage in a user’s private home directory. These requests may be submitted by emailing rcd-support@ucsd.edu.

Standard Datasets

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title	Standard Datasets

Name	Path	Size	#Files	Notes
MNIST	/datasets/MNIST	53M	4
ImageNet Fall 2011	/datasets/imagenet	1300G	14M
ImageNet 32x32 2010	/datasets/imagenet-ds	1800M	2.6M	ILSVRC2012 Downsampled 32x32,64x64
Tiny-ImageNet	/datasets/Tiny-ImageNet	353M	120k
CIFAR-10	/datasets/CIFAR-10	178M	9
Caltech256	/datasets/Caltech256	1300M	30k
ShapeNet	/datasets/ShapeNet	204G	981k	ShapeNetCore v1/v2
MJSynth	/datasets/MJSynth	36G	8.9M	Synthetic Word Dataset

Contact Research IT to request installation of additional datasets.

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title	Chicago Booth Kilts Center for Marketing: Nielsen Datasets

The Nielsen subscription dataset are available to authorized users at /uss/dsmlp-a/nielsen-dataset/. All of the datasets have been decompressed into this read-only directory making it easy for users to use software (Ex: Stata, your own code) to read directly from the Nielsen directories. In the interest of being mindful of server space, please do not duplicate these large datasets to your home directory and delete unneeded data from your home directory once you've completed your analyses and have your output files.

File Transfer

Users can utilize commands (e.g. 'git', 'scp', 'sftp', and 'curl') in the bash shell (command line interface) to import code or data from external servers that are both on and off-campus. Files can be copied into the cluster from external sources using Globus, SCP/SFTP, or RSYNC.

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title	Copying Data Into the Cluster: Using Globus

See the page on using Globus to transfer data to and from your computer or another Globus collection.

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Expand

title	Copying Data Into the Cluster: rsync

On MacOS or Linux, 'rsync' can be used from a terminal window to synchronize data sets.

Example using the Mac/Linux ‘rsync’ command line program:

Code Block

slithy:ME198 agt$ rsync -avr tub_1_17-11-18 <username>@dsmlp-login.ucsd.edu
pod agt-9924 up and running; starting rsync
building file list ... done
rsync complete; deleting pod agt-9924
sent 557671 bytes  received 20 bytes  53113.43 bytes/sec
total size is 41144035  speedup is 73.78
slithy:ME198 agt$

Customizing a Container Environment

Each launch script specifies the default Docker image to use, the required number of CPU cores, GPU cards, and GB RAM assigned to a container. When creating a customized container, it is recommended to use CPU-only containers until your code is fully tested and a test training run has been completed successfully. It is important to note that PyTorch, Tensorflow, and Caffe toolkits can easily switch between CPU and GPU , as such, a successful run in a CPU-only container should also be successful in a container with GPU.

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title	An example launch configuration is as follows:

Code Block

K8S_DOCKER_IMAGE="ucsdets/instructional:cse190fa17-latest"
K8S_ENTRYPOINT="/run_jupyter.sh"

K8S_NUM_GPU=1  # max of 1 (contact ETS to raise limit)
K8S_NUM_CPU=4  # max of 8 ("")
K8S_GB_MEM=32  # max of 64 ("")

# Controls whether an interactive Bash shell is started
SPAWN_INTERACTIVE_SHELL=YES

# Sets up proxy URL for Jupyter notebook inside
PROXY_ENABLED=YES
PROXY_PORT=8888

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title	Users may copy an existing launch script into their home directory, then modify that private copy such as;

Code Block
$ cp -p `which launch-pytorch.sh` $HOME/my-launch-pytorch.sh $ nano $HOME/my-launch-pytorch.sh $ $HOME/my-launch-pytorch.sh

Adjusting Container Environment and CPU/RAM/GPU limits

All running containers in the cluster have a maximum configuration limit of 8 CPU, 64GB, and 1 GPU. You may run eight 1 CPU-core containers, one 8-core container, or any configuration within the these bounds. Requests may be submitted to rcd-support@ucsd.edu to to increases to these default limits, as well as, to request other adjustments (including software) to your container environment.

Alternate Docker Images

In addition to configuration settings, users can import alternate or custom Docker images. The cluster servers will pull container images from dockerhub.io or elsewhere if requested. You can can create or modify these Docker images as needed.

Adjusting Launch Script Environments Command Line Options

Users can change the default variables within a launch script environment variables using specific command line options.

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Expand

title	An example launch script adjustment to the RAM (-m) and the GPU (-v):

Code Block
[cs190f @ieng6-201]:~:56$ launch-py3torch-gpu.sh -m 64 -v k5200

Custom Python Packages (Anaconda/PIP)

Users may install additional Python packages within their containers using the PIP tool or standard Anaconda package management system. Users should only install Python packages after launching a container. When Python packages are installed, they are installed in a user’s home directory. As such, these packages will be available for all containers launched thereafter by the user.

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Expand

title	PIP command to install TensorBoard:

Code Block
pip install -U --user jupyter-tensorboard jupyter nbextension enable jupyter_tensorboard/tree --user

Note: You’ll need to exit your Pod/container and restart for the change to take effect.

Running Jobs in a Background Container and Long-Running Jobs

To minimize the impact of abandoned/runaway jobs, the cluster allows for containers to run jobs in the background container for up to 12 hours of execution time. Users need to specify that a job should run in a background container by using the "-b" command line option (see example below). To support longer run times, the default execution time can be extended upon request to rcd-support@ucsd.edu.

Note to users: Please be considerate and terminate any unused background jobs. GPU cards are limited and assigned to containers on an exclusive basis. When attached to a container, GPUs are unusable by others even if the GPU is idle while attached to your container.

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title	Reconnecting a background container:

In the event that your background container is disconnected, use the ‘kubesh <pod-name>’ command to connect or reconnect to a background container.

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Expand

title	An example of entering a background container command:

Code Block

[amoxley@dsmlp-login]:~:504$ launch-scipy-ml.sh -b
Attempting to create job ('pod') with 2 CPU cores, 8 GB RAM, and 0 GPU units.
   (Adjust command line options, or edit "/software/common64/dsmlp/bin/launch-scipy-ml.sh" to change this configuration.)
pod/amoxley-5497 created
Mon Mar 9 14:04:10 PDT 2020 starting up - pod status: Pending ; containers with incomplete status: [init-support]
Mon Mar 9 14:04:15 PDT 2020 pod is running with IP: 10.43.128.17 on node: its-dsmlp-n25.ucsd.edu
ucsdets/scipy-ml-notebook:2019.4-stable is now active.

Connect to your background pod via: "kubesh amoxley-5497"
Please remember to shut down via: "kubectl delete pod amoxley-5497" ; "kubectl get pods" to list running pods.
You may retrieve output from your pod via: "kubectl logs amoxley-5497".
PODNAME=amoxley-5497
[amoxley@dsmlp-login]:~:505$ kubesh amoxley-5497

amoxley@amoxley-5497:~$ hostname
amoxley-5497
amoxley@amoxley-5497:~$ exit
exit

[amoxley@dsmlp-login]:~:506$ kubectl get pods
NAME           READY   STATUS    RESTARTS   AGE
amoxley-5497   1/1     Running   0          45s

[amoxley@dsmlp-login]:~:507$ kubectl delete pod amoxley-5497
pod "amoxley-5497" deleted
[amoxley@dsmlp-login]:~:508$

Run-TIme Error Messages

There may be instances where you receive a CUDA run-time error while running a job in a container. Below are a few of the more commonly encountered errors. These errors can typically be resolved by user adjustments. However, If users encounter a run-time error that requires more assistance to resolve, please contact rcd-support@ucsd.edu.

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Expand

title	(30) unknown error

This indicates a hardware error on the assigned GPU, and usually requires a reboot of the cluster node to correct. As a temporary workaround, you may explicitly direct your job to another node (see 'Adjusting Launch Script Environments Command Line Options” in this user guide).

Code Block
RuntimeError: cuda runtime error (30) : unknown error at /opt/conda/conda-bld/pytorch_1503966894950/work/torch/lib/THC/THCGeneral.c:70

Please report this type of error directly to rcd-support@ucsd.edu for assistance.

Monitoring Cluster Status

Users can enter the ‘cluster-status’ command for insight into the number of jobs currently running and GPU/CPU/RAM allocated. Alternatively, users can refer the the cluster ‘Node Status’ ‘Node Status’ page for updates on containers (or images).

Expand

title	An example of a 'cluster-status' command output:

Cluster Hardware Specifications

The Research Cluster shares hardware infrastructure with the Data Science and Machine Learning Platform (DSMLP). As such, the information about the hardware specifications for the Research Cluster are described in the Cluster architecture diagram (as displayed in reference to the DSMLP).

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Expand

title	Example: PyTorch Session with TensorFlow examples

Code Block

slithy:~ agt$
slithy:~ agt$ ssh cs190f@ieng6.ucsd.edu
Password:
Last login: Thu Oct 12 12:29:30 2017 from slithy.ucsd.edu
============================ NOTICE =================================
Authorized use of this system is limited to password-authenticated
usernames which are issued to individuals and are for the sole use of
the person to whom they are issued.
 
Privacy notice: be aware that computer files, electronic mail and
accounts are not private in an absolute sense.  For a statement of
"ETS (formerly ACMS) Acceptable Use Policies" please see our webpage
at http://acms.ucsd.edu/info/aup.html.
=====================================================================
 

Disk quotas for user cs190f (uid 59457):
     Filesystem  blocks   quota   limit   grace   files   quota   limit   grace
acsnfs4.ucsd.edu:/vol/home/linux/ieng6
                      11928  5204800 5204800                 272        9000        9000      
=============================================================
Check Account Lookup Tool at http://acms.ucsd.edu
=============================================================

[…]

Thu Oct 12, 2017 12:34pm - Prepping cs190f
[cs190f @ieng6-201]:~:56$ launch-pytorch-gpu.sh
Attempting to create job ('pod') with 2 CPU cores, 8 GB RAM, and 1 GPU units.  (Edit /home/linux/ieng6/cs190f/public/bin/launch-pytorch.sh to change this configuration.)
pod "cs190f -4953" created
Thu Oct 12 12:34:41 PDT 2017 starting up - pod status: Pending ;
Thu Oct 12 12:34:47 PDT 2017 pod is running with IP: 10.128.7.99
tensorflow/tensorflow:latest-gpu is now active.

Please connect to: http://ieng6-201.ucsd.edu:4957/?token=669d678bdb00c89df6ab178285a0e8443e676298a02ad66e2438c9851cb544ce

Connected to cs190f-4953; type 'exit' to terminate processes and close Jupyter notebooks.
cs190f@cs190f-4953:~$ ls
TensorFlow-Examples
cs190f@cs190f-4953:~$
cs190f@cs190f-4953:~$ git clone https://github.com/yunjey/pytorch-tutorial.git
Cloning into 'pytorch-tutorial'...
remote: Counting objects: 658, done.
remote: Total 658 (delta 0), reused 0 (delta 0), pack-reused 658
Receiving objects: 100% (658/658), 12.74 MiB | 24.70 MiB/s, done.
Resolving deltas: 100% (350/350), done.
Checking connectivity... done.
cs190f@cs190f-4953:~$ cd pytorch-tutorial/
cs190f@cs190f-4953:~/pytorch-tutorial$ cd tutorials/02-intermediate/bidirectional_recurrent_neural_network/
cs190f@cs190f-4953:~/pytorch-tutorial/tutorials/02-intermediate/bidirectional_recurrent_neural_network$ python main-gpu.py
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Processing...
Done!
Epoch [1/2], Step [100/600], Loss: 0.7028
Epoch [1/2], Step [200/600], Loss: 0.2479
Epoch [1/2], Step [300/600], Loss: 0.2467
Epoch [1/2], Step [400/600], Loss: 0.2652
Epoch [1/2], Step [500/600], Loss: 0.1919
Epoch [1/2], Step [600/600], Loss: 0.0822
Epoch [2/2], Step [100/600], Loss: 0.0980
Epoch [2/2], Step [200/600], Loss: 0.1034
Epoch [2/2], Step [300/600], Loss: 0.0927
Epoch [2/2], Step [400/600], Loss: 0.0869
Epoch [2/2], Step [500/600], Loss: 0.0139
Epoch [2/2], Step [600/600], Loss: 0.0299
Test Accuracy of the model on the 10000 test images: 97 %
cs190f@cs190f-4953:~/pytorch-tutorial/tutorials/02-intermediate/bidirectional_recurrent_neural_network$ cd $HOME
cs190f@cs190f-4953:~$ nvidia-smi
Thu Oct 12 13:30:59 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.81                 Driver Version: 384.81                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 108...  Off  | 00000000:09:00.0 Off |                  N/A |
| 23%  27C    P0     56W / 250W |      0MiB / 11172MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

cs190f@cs190f-4953:~$ exit

Licensed Software

Installing licensed software is allowed in the Research Cluster; however, certain software versions are required to be compatible for installation in a cluster environment. The purchase of licensed software is the responsibility of the user or their sponsoring department. Research IT Services is available to assist with the installation of licensed software. For questions about installing licensed software, please email rcd-support@ucsd.edu.

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title	Stata

For users with provisioned Stata licensing, the launch-scipy-ml.sh container is capable of executing Stata. Stata can be installed in your home directory by the Research IT Services team and can be executed using the command '~/stata-se' from within a container.

Acknowledging Research IT Services

Papers, presentations, and other publications that feature research that benefited from the Research Cluster computing resource, services or support expertise may include in the text the following acknowledgement:

This research was done using the UC San Diego Research Cluster computing resource, supported by Research IT Services and provided by Academic Technology Services / IT Services.

Versions Compared

Old Version 47

New Version Current

Key

Getting Started

Getting Started

Launching a Container

Web Interface Tool

Modifying Containers

Data Storage / Datasets

Standard Datasets

File Transfer

Customizing a Container Environment

Adjusting Container Environment and CPU/RAM/GPU limits

Alternate Docker Images

Adjusting Launch Script Environments Command Line Options

Custom Python Packages (Anaconda/PIP)

Running Jobs in a Background Container and Long-Running Jobs

Run-TIme Error Messages

Monitoring Cluster Status

Cluster Hardware Specifications

Licensed Software

Acknowledging Research IT Services