OrangeGrid Examples

This repository provides code examples for commonly used applications within the OrangeGrid cluster. In addition to exploring these examples on the web you can use git to download them into your home directory on the cluster which will allow you to run them directly. The command is

git clone http://github.com/SyracuseUniversity/OrangeGridExamples

Start here

• hostname: A simple example of submitting a job to the cluster, monitoring it, and checking its output

Getting the most out of OrangeGrid

These examples discuss general techniques for optimizing performance and throughput

• Checkpointing: Learn how to save work that your jobs are doing, so if they exit for any reason HTCondor can restart them where they left off.
• Parallelism: Learn how to divide a task into lots of smaller tasks that can run independently so they can spread out over the cluster.
• File management: Learn how to arrange your data into chunks that are both more efficient for later analyses and perform better on the cluster.
• multipleJobs: Learn how to submit multiple jobs from one submit file.
• HTCondorDiagnostics: Check the status of the cluster.

Using particular languages and libraries

• python: Use Python and Python packages with the Conda package manager.
• uv: Use Python and Python packages with the uv package manager.
• tensorflow: Use the Tensorflow package.
• PyTorch: Use the PyTorch package (note, the uv example also covers this in a way that may be easier to use).
• Ollama: Run an LLM non-interactively, choosing from among numerous models.
• julia: Use the Julia language and its libraries.
• octave: Use the Octave, a free and open source math-oriented language that is largely compatibe with Matlab.
• R: Use the R language and its libraries.
• Apptainer: Simplify the installation of complex packages by pulling containers from Dockerhub, or building new containers.
• blender: Render images and movies.
• CVMFS: Use software a data distributed through the CERN Virtual File System
• Grobid: use GROBID, a machine learning library for extracting, parsing and re-structuring raw documents such as PDF into structured documents.

Need Help?

Additional how-to documentation, such as connecting to clusters and running jobs, is available in Answers.

If you would like to contact us directly for assistance or requesting access, email researchcomputing@syr.edu.