Working in Your Instance
Every managed compute instance runs three editors—JupyterLab, code-server, and R Server—against the same files. Use whichever you prefer, or switch between them mid-project.
Connect from the GoFigr web app: when an instance is Running, click the Jupyter, code-server, or R Server button to open that editor in a new tab. (Connect links are tied to you as the owner.)
Where your files live
Your home directory is /home/gofigr/notebooks. It's seeded on first boot with:
START-HERE.md— a short welcome and orientation.examples/— a runnable example notebook (tcga_lung_classifier.ipynb) and its Quarto/R counterpart (tcga_lung_analysis.qmd).
Everything in your home directory lives on the instance's data volume and persists across stop/start.
The editors
The GoFigr IPython extension is pre-installed. In a notebook, capture starts as soon as you load it:
%load_ext gofigrFrom there, figures you create are published automatically—no credentials to set up, because the instance is already signed in to your account. See the Python integration for what's captured and how.
code-server is a full-featured editor running in your browser, with the GoFigr extension already installed. Open notebooks or scripts, use the integrated terminal, and manage multi-file projects. The Python interpreter is pre-selected for you.
R Server gives you a full R IDE in the browser. library(gofigR) is installed and configured, so it works the same as a local session:
library(gofigR)
gofigR::enable()Figures publish as you run R scripts, R Markdown, or Quarto documents. See the R integration for usage details.
The Python environment
Python runs from a virtual environment at /home/gofigr/venv. It lives on your data volume, so anything you install persists across restarts, and it's already active in JupyterLab and the code-server terminal—you don't need to activate it. The gofigr client is installed here, and pip install and the notebook kernel both resolve to this environment.
If you open a plain login shell and want it active explicitly:
Installing extra packages
The instance comes pre-loaded with common Python and R packages. Need more? Add them from the integrated terminal or a notebook cell—they install to your data volume and persist across restarts:
Because installed packages live on your persistent data volume, you only install each one once—it'll be there the next time you start the instance.
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