57 docs tagged with "Services"

Accessing compute notes in Pantarhei

Access of On-Premises Cluster Pantarhei

Access of On-Premises Cluster Wukong

Accessing and Using Anvil

As the main account administrator for CIROH subaccount, here are some best practices to follow within your subaccount:

AWS Data Science Tools

AWS Research Cloud

CIROH AWS Office Hours

Computing infrastructure access for consortium members and partners

Google Research Cloud

CIROH-2i2c JupyterHub is a cloud-based JupyterHub environment specifically designed for hydrological researchers. It is powered by 2i2c JupyterHub, a cloud-based JupyterHub environment deployed on Google Cloud

Want to keep up with the latest updates in the cloud? If so, consider checking out the block pages for CIROH's cloud services partners.

cuahsi jupyterhub

Need help with NGIAB or CIROH Cyberinfrastructure. Join us for office hours!

AWS documentations

Ready-to-run job script examples for Pantarhei — Python, C, MPI, GPU, Jupyter, and VSCode tunneling, sourced from the pantarhei-examples GitHub repository.

{/ TODO/FIXME: Randomly pulling CSS files from the homepage is pretty wonky, and the cta-# files are of unknown provenance. Can all of this be moved to Infima styling? /}

GPU job examples for Pantarhei using CUDA C and Python (CuPy, PyTorch) on the gpu and gpu-more-time partitions.

Hydrologic C examples for Pantarhei, including SCS Curve Number runoff estimation, Clark routing, baseflow separation, and an MPI-enabled parallel version.

GPU-accelerated SCS Curve Number hydrologic workflow using CuPy inside a Jupyter session on Pantarhei's GPU partition.

SCS Curve Number hydrologic workflow using NumPy and Matplotlib inside a Jupyter session on Pantarhei's normal CPU partition.

Hydrologic Python examples for Pantarhei, including time-series processing, watershed metrics, and MPI-enabled analysis.

A web-based hydrologic information system for data and model sharing.

Easily launch and execute notebooks from HydroShare.

CIROH provides access to public cloud services, HPC, and on-premises infrastructure to support the research projects of CIROH's members and partners.

Request an interactive compute node on Pantarhei using salloc for testing, debugging, and exploratory work.

Accessing and Using JetStream2

Run a GPU-enabled Jupyter Lab or Jupyter Notebook server on a Pantarhei GPU compute node and connect from your local browser via SSH tunneling.

Run a Jupyter Lab or Jupyter Notebook server on a Pantarhei CPU compute node and connect from your local browser via SSH tunneling.

Learn to navigate the 2i2c JupyterHub file system.

Explore CIROH's available image configurations.

Request JupyterHub server images tailored to your needs.

Take advantage of Google Cloud Bucket storage.

MPI job examples for Pantarhei covering single-node, multi-node, and hybrid MPI+OpenMP patterns in C and Python.

A dedicated image on CIROH-2i2c JupyterHub for running NextGen In A Box.

ACCESS is an advanced computing and data resource program supported by the U.S. National Science Foundation (NSF). Please refer to https://allocations.access-ci.org/ for more information on how to get access to NSF ACCESS resources.

Accessing and Using JetStream2

Obtain an account on Pantarhei

Obtain an account on Wukong

What is On-Premises services?

Pantarhei HPC Cluster

C example job scripts for Pantarhei using Slurm

Python example job scripts for Pantarhei using Slurm

Maintain your Conda environments between sessions.

Keep your server active to sustain long-running tasks.

In tandem with the power of the public cloud, our team of researchers, hydrologists, and engineers at CIROH is committed to advancing our understanding of hydrologic processes, improving operational hydrologic forecasting techniques and workflows, collaborating on community water modeling, converting forecasts into practical solutions, and utilizing water predictions to help guide decision-making processes.

Push to your GitHub repositories from the cloud.

An end-to-end hydrologic modeling workflow using the NextGen framework in Python from a Jupyter environment.

Debug version conflicts between your Python packages.

Proficient users acquainted with the Linux command line interface have the option to utilize standard job submission utilities for the purpose of managing and executing tasks on the computational nodes within the Pantarhei system.

Submitting and managing jobs on Pantarhei using Slurm

System Architecture of Pantarhei

System Architecture of Wukong

AWS tags for cost tracking

A dedicated image on CIROH-2i2c JupyterHub for running NextGen In A Box.

Run a VSCode tunnel on a Pantarhei compute node and connect from your local machine using the VS Code Remote Tunnels extension.

Wukong HPC Cluster