UC San Diego's Data Science/Machine Learning Platform (DSMLP) provides undergraduate and graduate students with access to research-class CPU/GPU resources for coursework, formal independent study, and student projects.
Built and operated by IT Services (ITS), with additional financial contributions from Cognitive Science and Jacobs School of Engineering, DSMLP leverages Qualcomm Institute's current research into cost-effective machine-learning cyberinfrastructure using Kubernetes and Docker container technologies.
Please note, DSMLP is not intended to store personally identifiable information (PII) or other sensitive data. It is also against policy to manually run code without the launch.sh script. For more info, see Policies below.
For course-specific questions, such as a problem with a homework assignment, please contact your Teaching Assistant or Instructor.
To report problems with DSMLP/datahub.ucsd.edu, or to request assistance, please contact the ITS Service Desk by emailing email@example.com to create a problem ticket. Include the following information:
If necessary, you may also reach the ITS Service Desk by phone/walk-in.
If you have modified your environment using pip, see "How Do I Fix My Local Environment" below.
There are also several self-help documents in the Specialized Instructional Computing Knowledge Base.
Log out of all Google accounts or open an incognito window. When prompted, enter your full UCSD email, "firstname.lastname@example.org", as your credentials. Currently only '@ucsd.edu' addresses are accepted in the Data Science and Machine Learning Platform, not departmental or divisional addresses such as '@eng.ucsd.edu' or '@physics.ucsd.edu'
Access is restricted to authorized users. Students may obtain access to DSMLP through the following methods:
UC San Diego Extension students enrolled in a DSMLP/datahub course should fill out the Concurrent Enrollment Account form and ask their instructor to add them to the course via Canvas.
For more information on eligibility, visit the DSMLP page on blink.ucsd.edu.
Note that we perform required weekly patching on datahub/DSMLP every Wednesday from 6-8 AM Pacific Time. This may result in reduced capacity, and depending on the nature of the update, the service as a whole may be inaccessible.
After logging in, make sure you are selecting the correct container for your course on the "Select your (Course) Environment" page. The course name will be identified on the container. Some courses have multiple containers available; consult with your TA/instructor for guidance.
After launching your container, you will see the jupyter notebook server environment. To open a new jupyter notebook, select "New" (top right of page), then "Python 3".
When your work is complete, please shut down your container via "Control Panel" (top right), then click the Stop my Server button.
If you recently registered in a course, please allow 1-2 business days for resources to be listed. Otherwise, please file a IT Services support ticket, include reasons for access, what course you are registered in, and whether you are auditing the course. If auditing a course, please get the instructor's permission prior to submitting a request.
If you are no longer enrolled in any courses, please see "How long does access last after the end of term?" below on how to retrieve your files when you can no longer access your course environment.
Please reset your profile:
More information: How To: Customize your environment in DSMLP/Datahub (including jupyter notebooks)
Check if you have received a "disk quota exceeded" email. If so, please see the disk space quota section below.
Note that some errors are detected and fixed automatically within a few minutes by our system.
Users have two storage pools and each pool has a separate quota. One pool is for the course workspace, and the other pool is for personal use (/private). Files in the /public and /teams folders count against the workspace quota. Files in /private count against the personal quota.
|Files||Path in Jupyter notebook UI||Notebook Location||Quota|
|Course home directory||/||/home/USERNAME||Workspace|
|Course team directory||/teams||/home/USERNAME/teams/TEAM||Workspace|
|Course public directory||/public||/home/USERNAME/public||Workspace|
|Personal directory||/private||/home/USERNAME /private||Personal|
To view your quota please login to datahub.ucsd.edu and select Services -> disk-quota-service.
Check your disk quota to see which quota has been exceeded.
Launch a notebook for the course. If this doesn't work, please see the section below on how resolve this on dsmlp-login.ucsd.edu.
If the workspace quota has been exceeded, delete some files that are not in the /private folder.
If the personal quota has been exceeded, navigate to /private and delete some files.
Use Control Panel -> Stop to return to JupyterHub and check the quota.
Note: when you delete a file, it goes into the trash folder at .local/share/Trash. These files can accumulate, and to manually delete them, open a terminal and cd to that directory and delete the files using "rm". Files should get automatically deleted out of Trash after 7 days.
Check your disk quota to see which quota has been exceeded.
ssh to dsmlp-login.ucsd.edu, e.g. "ssh email@example.com". Use your AD password.
If the personal quota has been exceeded, delete some files from your home directory using the "rm" command.
If the workspace quota has been exceeded, run the "workspace --list" command.
[username@dsmlp-login ~]$ workspace --list
2023-03-01 11:51:58,533 - workspace - INFO - Retrieving course info...
Course_ID, Path to Course Workspace Home Directory
Change to the specified directory, in this example, using "cd /dsmlp/workspaces-fs04/COURSE_ID/home/username".
Delete some files using the "rm" command.
You can use the command "
du -h -d 1" to see which directories use the most space.
Large input datasets should reside in a single location rather than being downloaded to each student's home directory. Students who require more storage should ask their instructor (or TA) to submit a request via the instructors' course Service Desk ticket. For Independent Study students (not enrolled in a datahub course) who require more storage, please submit a Service Desk ticket.
If you're uploading large files (> 64MB) or a lot of files, then uploading them with the Jupyter UI may not be ideal.
One option is to ZIP the files, upload the ZIP, and then extract the ZIP. Or you could put the files into git and do a git pull.
Another option is to use scp to transfer the files. To do this ssh to dsmlp-login. This is your personal directory. You will notice that this path name is different than what you see inside a course notebook.
You can use scp to copy the files here from your own pc to datahub, or backup files from datahub. Type "pwd" to show the path to your home directory.
scp /local/path firstname.lastname@example.org:~/remote/path
scp email@example.com:~/remote/path /local/path
If you need to copy files to your course home directory run "workspace --list" to show the directory they are located in. It should be something like /dsmlp/fs0x-workspaces/COURSE/home/USERNAME. The directory may be invisible until you cd to it. Team files are located under the same path, but with /teams instead of home, e.g. /dsmlp/fs0x-workspaces/COURSE/teams/TEAM. Use this path in scp instead of your personal home.
Server capacity is limited, and during peak times (e.g. 10th week/finals) occasional delays are to be expected. GPU resources are the most constrained, so you may have success re-launching your job as CPU-only. If you experience capacity errors for prolonged periods, or at unexpected times of the quarter, please file a Service Desk ticket. ETS staff will respond within one business day.
DSMLP is primarily an instructional resource. Users enrolled in a course have higher priority than independent study and research users. When capacity is exhausted, or course users require resources, you may receive an eviction message notifying you to save your work and exit within ten minutes. Temporary hardware or systems issues can also reduce capacity and result in evictions.
During busy periods, we encourage you to visit the cluster status page to check resource availability.
Access is retained for one additional term, e.g. a fall course is available until the end of winter. Please submit an independent study request (https://go.ucsd.edu/2wc5gH0) to request an extension.
To retrieve your files, please scp them from dsmlp-login.ucsd.edu.
scp -r <username>@dsmlp-login.ucsd.edu:/directory/to/send /local/where/to/put
Each user has a predefined amount GPU/CPU/RAM based on their enrolled course. To see the available RAM, look at the upper right corner of your notebook server (after opening a notebook). Please be considerate and terminate any unused background jobs, since GPU cards are assigned to containers on an exclusive basis, and when attached to a container are unusable by others even if idle.
If available for your course, we encourage you to use non-GPU (CPU-only) containers until your code is fully tested and a simple training run is successful. (PyTorch, Tensorflow, and Caffe toolkits can easily switch between CPU and GPU.)
GPU types are listed on the cluster status page.
Your course may provide datasets in the public directory.
There are are also several large datasets available for use with DSMLP/Datahub. To access these datasets from datahub.ucsd.edu, launch your environment, select “New->Terminal” at the top right of the notebook server interface, and enter:
cd /datasets. To access these datasets from the command line on dsmlp-login, ssh to dsmlp-login.ucsd.edu and use the previous command to access the datasets directory.
See: How to: File/Data Transfer - Data Science/Machine Learning Platform (DSMLP)
Groups of users can be created in DSMLP/datahub for sharing datasets, code, etc. Your TA or instructor can set this up in Canvas using the instructions here: https://support.ucsd.edu/services?id=kb_article_view&sysparm_article=KB0030588. Groups will have 100 GB of storage by default; please email firstname.lastname@example.org to request additional space.
Use the control panel (button top right of the Jupyter server) to stop the notebook, there may be a delay in updating interface, please let the process complete. Logging Out Does NOT Stop the Server (but it may stop due to inactivity).
If you have a job that needs to run for an extended period of time, we recommend running from the command line. See "How do I launch a container from the command line" below. If you are running from within datahub.ucsd.edu, you must keep your browser window open. More information: https://zero-to-jupyterhub.readthedocs.io/en/stable/jupyterhub/customizing/user-management.html
Please see How To: Customize your environment in DSMLP/Datahub (including jupyter notebooks)
If your course container has additional software versions available, these can be accessed via the "New" button at the top right of your notebook server interface in datahub.ucsd.edu. Additional kernels containing those software versions will appear in the dropdown menu below the standard python notebook option.
See "Launching Containers from the Command Line - DSMLP" and "How to Select and Configure Your Container - DSMLP"
See the current installed version of cuda by referring to the "cuda install cudatoolkit" line in our scipy-ml-notebook container here: https://github.com/ucsd-ets/scipy-ml-notebook/blob/master/Dockerfile
This version may be behind the newest cuda release(s). This is because upgrading cuda requires coordinating an update of its drivers and various toolkits. Therefore we typically perform the update process during the summer academic terms.
For example, in Summer 2021, current cuda version = 10.1, updated to cuda 11+ for Fall 2021 term.
Each GPU has a limited amount of physical memory available to it, independent of the amount of RAM available to your pod. This message is seen when you exhaust that memory, and indicates that you need to work with your TA to modify how much GPU memory your work requires.
The GPUs on different compute nodes in the DataHub cluster have different amounts of memory available. Most nodes have 11GB, but others may have more or less. To view the capacities of the GPU attached to your current pod, run the command "nvidia-smi". (Note that you will not be able to see the list of processes running on the GPU from your pod.)
The amount of free memory can also be determined programmatically by using the cudaMemGetInfo() function in the CUDA Runtime API.
Subtle choices in PyTorch library usage can result in memory leaks that exhaust GPU memory. For suggestions on how to manage CUDA memory in PyTorch, see the section My model reports "cuda runtime error(2): out of memory" on the PyTorch FAQ.
Tensorflow's default behavior is to claim all available RAM on the GPU, and not let go of it until the process that claimed it (typically a notebook kernel for DSMLP) dies. If you want to reclaim the memory (incase you would like to run another process in a different notebook or swap from Tensorflow to PyTorch in the same notebook, you may restart the kernel. You may also run this command from Tensorflow that dynamically claims memory as the kernel needs it. More info can be found on the Tensorflow GPU Guide.
# Prevent TF from using all available NVRAM...
import tensorflow as tf gpus = tf.config.list_physical_devices('GPU') for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True)
1. Open New -> Terminal
2. rm ~/.local/share/jupyter/nbsignatures.db
3. Close terminal window
4. Control Panel -> Stop
5. Restart notebook
Information on using R and RStudio with DSMLP/Datahub can be found here.
After launching your environment in datahub, if you see the classic Jupyter Notebook user interface but want to use the newer JupyterLab UI, replace "/tree" in the URL with "/lab", e.g., https://datahub.ucsd.edu/user/<username>/lab.
This occurs if a read-only cell/an autograded cell has been copied. Do not copy and paste these cells in your notebook. Similar errors, such as "Failed validating 'required' in notebook" might occur if you edit or delete cells provided by the instructor. To resolve this problem, you (or your student) will need to:
UC Data Protection Levels are defined at: https://security.ucop.edu/files/documents/uc-protection-level-classification-guide.pdf
DSMLP is not suitable for storage or processing of Category P3/P4 data, which includes:
Please visit the link above for a full list of P3/P4 data.
If your project may involve P3/P4 data, please contact ETS or Research IT Services for a consultation.
If you still have questions or need additional assistance, please email email@example.com to create a support ticket, or visit support.ucsd.edu.
DSMLP is primarily designed to facilitate the execution of docker images using the
launch.sh command. By running
launch.sh, dedicated nodes are provisioned, allowing the utilization of these images to create customized environments suitable for various development workflows. It is important to note that dsmlp-login itself is prohibited for manual job execution, such as running Python scripts, Java projects, or machine learning tasks. Developing within these dedicated nodes, instead of using dsmlp-login, can help minimize any potential impact on server performance.