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MESA JupyterLab

Repo: idss-mesa/jupyterlab · Image: harbor.cyverse.org/vice/mesa-jupyterlab:latest (GPU: :gpu) · In the portal: Applications → MESA Apps → MESA JupyterLab

A JupyterLab workbench for Python, R, and Julia, built on the Project Jupyter datascience-notebook image1. RStudio Server, Shiny Server, and VS Code open from the JupyterLab Launcher, and the AI coding agents and MESA MCP servers are ready in every terminal.

What's inside

Category Tools
IDEs JupyterLab; RStudio Server, Shiny Server, and VS Code (code-server) as Launcher cards
Science Python 3.13, R, and Julia with the datascience stack (NumPy, SciPy, pandas, tidyverse, …); Miniconda and Mamba
AI agents and MCP Claude Code, Codex, OpenCode, Antigravity, Claude Code Router; the irods, mesa, formation, and filesystem MCP servers — see AI agents in the MESA apps
CyVerse data GoCommands, iRODS configuration, S3/OSN mounts, AWS CLI
Developer tools GitHub CLI, Git Credential Manager, Go 1.25, Node.js 22

Start it

  1. In the MESA Portal, open Applications → MESA Apps.
  2. On MESA JupyterLab, click Instant Launch, or Launch with Options to pick CPU cores, memory, the time limit, or the GPU build. See Starting applications.
  3. The app opens in a new tab at the JupyterLab interface, in ~/data-store (your Data Store).

You can also start it from the Discovery Environment by searching for MESA JupyterLab.

First steps

  1. Open a Terminal from the Launcher.
  2. Run cyverse-login to give the tools and agents your CyVerse access.
  3. Optionally run aiverde-setup to connect AI Verde models.
  4. Start an agent: claude, codex, opencode, or agy.

The details are in AI agents in the MESA apps. Keep notebooks and results under ~/data-store: the rest of the container is deleted when the analysis ends.

RStudio inside JupyterLab. Click the RStudio card in the Launcher to open RStudio Server in a new tab, with the same files and R installation.

GPU build

harbor.cyverse.org/vice/mesa-jupyterlab:gpu is the same workbench on an NVIDIA GPU. Choose GPU on the app's card in the portal. It adds:

Adds Details
PyTorch torch 2.14 and torchvision 0.29 (CUDA 12.6) in the default Python 3 kernel
ML libraries transformers, accelerate, peft, sentence-transformers, safetensors, bitsandbytes, Lightning, timm, torchmetrics, TorchGeo, CuPy, huggingface_hub
JupyterLab GPU Dashboards (NVDashboard) in the left sidebar; Jupyter AI chat with an @OpenCode persona
Local LLMs An Ollama server on the GPU — see Local models on a GPU
GPU tools nvidia-smi, nvtop, nvitop, mesa-gpu-check

Check the GPU from a notebook or terminal:

python -c 'import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))'
mesa-gpu-check

Jupyter AI's @OpenCode starts on an AI Verde model, which cannot read the key that aiverde-setup saves. To chat with the local model instead, run ollama-setup in a terminal, then pick ollama/qwen3.5:9b in @OpenCode's model picker.

Run it on your own computer

docker run --rm -p 8888:8888 -e IPLANT_USER=$USER harbor.cyverse.org/vice/mesa-jupyterlab:latest

Open http://localhost:8888/lab; RStudio is at http://localhost:8888/rstudio/. Outside CyVerse there is no password, so publish the port only on your own machine. The image is built for linux/amd64.

  1. MESA JupyterLab README, https://github.com/idss-mesa/jupyterlab. ↩

Machine-readable versions of this page: Markdown twin · raw source on GitHub · llms.txt · llms-full.txt (whole site). See For AI agents.