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Before You Start

To log in, you must already be a member of an active project and have either:

• A standard NASA identity, OR
• A STAR ID (for external partners).

(Don't have these yet? See How to get access.)

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Quick Start Summary

While the full document below provides comprehensive instructions and explanations, here is a quick start guide to get you up and running immediately.

1. Go to rp.sciencecloud.nasa.gov and log in.
2. Pick a machine size. Start it.
3. Keep files in ~, in /efs/<project>, or in object storage. Everything else is lost on restart.
4. Work in a notebook. Ask the AI assistant when you are stuck. Need your own packages? Build a conda environment in ~/envs and register it as a kernel.
5. Big job? Dask for parallel work, NBQueue for long background runs.
6. Same file as a colleague? Use the collaboration tile.
7. Check the usage panel.
8. Stop everything when you finish.

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Getting Started: Step-by-Step

Step 1: Log In

1. Open rp.sciencecloud.nasa.gov in your web browser.
2. Sign in. You will be briefly redirected to the Science Cloud login service (STAR) to verify your identity.
3. Once signed in, you will land on the platform's main launch screen. There is nothing to install.

The sign-in page. Use your NASA Launchpad or NASA Science Cloud identity.

Step 2: Choose Your Settings and Start

1. Once logged in, you will land on the Server Options screen. You need to make three choices here before starting your workspace:

How big of a machine do you need? Choose your compute power from the Server Type dropdown list:

Coming later: Two changes to this list are on the roadmap: adding GPU machines, and letting project admins build and maintain their own custom list of server types for their team, instead of everyone choosing from one platform-wide list.

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Which project are you working on? Select your active project from the Project dropdown list. This determines which project storage you can access and which budget will be charged for this session.

      2. Once you have made your selections, click Start. Your workspace will open in a minute or two.Image Modified

The Server Options screen: pick the image, the server type, and the project.

Step 3

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: Find Your Way Around JupyterLab

You are now in JupyterLab. The important parts:

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JupyterLab. The file browser is on the left; the Launcher tiles are in the middle.

Step 4

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: Know

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Where to Put Your Files and What Survives a Restart

This is the most important thing to learn on day one. Some places keep your files. Some do not.

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Step 5

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: Do the science

1. Open the Launcher and start a new notebook.

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2. Type your code

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1. into a cell.
2. Press Shift + Enter to run it. The

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1. results will appear immediately below the cell.

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Note: This behaves exactly like a standard Jupyter Notebook. If you

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've used Jupyter

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before, you

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'll feel right at home!

Step 5b

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: Create a Custom Kernel from a

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Conda Environment

Sometimes, the

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pre-

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configured software environments don't include the specific package versions your project requires. When this happens, you can build your own Conda environment and use it as a custom kernel.

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• Environment: Your private, isolated set of software packages.
• Kernel: The computation engine that runs your code (this appears as an option in the notebook's "choose your language" menu

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• ).

Important: Always build your environment

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in your home folder.

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This ensures your custom packages persist and survive any system restarts.

1. To get started, open a Terminal from the Launcher and run the following commands:

# 1. create the environment (mamba is the fast version of conda)
mamba create -n myscience python=3.12 ipykernel -y

# 2. turn it on
conda activate myscience

# 3. add the packages you need
mamba install -c conda-forge xarray netcdf4 -y

That      That is all. The kernel appears in JupyterLab by itself, because the platform watches your environments for you.

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Kernel, then Change Kernel. Your own environment appears in the list.

     2. Use Kernel → Change Kernel in your notebook and pick it.

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Why this survives a restart

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The platform is specifically set up to keep your environments safely tucked away in ~/users_conda_envs

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(inside your home folder). So, when you run mamba create -n myscience, it writes everything to /home/<your

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-username>/users_conda_envs/myscience

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. Because it's in your home folder, it'll be right there waiting for you tomorrow!

Two quick things to remember:

1. Always put ipykernel in the environment. If you forget this package, there won't be a kernel to pick from the notebook menu.
2. You can use conda or mamba. Both do the job, but mamba is much faster.

 Kernel not showing up? If the kernel doesn't appear in your notebook menu, you can easily register it by hand. Just run this in your terminal: python -m ipykernel install --user --name myscience --display-name "My science env"

Step 6

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: Get AI

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Help While You Work

The Notebook Intelligence tool puts an AI assistant in JupyterLab.

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Give the assistant new skills. The AgentRegistry panel in the left sidebar lists approved MCP servers — small services that teach the assistant a new trick, such as searching a data catalog. Press Add to NBI on one, and your assistant can use it. See the tool reference.

Step 7

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: Browse and

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Pull in

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Data

The data browser (jupyterlab-bxplorer) opens cloud storage inside JupyterLab. No command line needed.

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Coming later: Google Cloud and Microsoft Azure storage.

Step 8

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: Need

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More Power? Two

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Choices

Choice A — many machines at once (Dask)

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Open the notebook and press NBQueue: Send to queue in the toolbar at the top. Choose the machine, and send it. Then check the status in the NBQueue panel. When it finishes, the finished notebook, with its output, is waiting for you.

Step 9

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: Work with

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Your Team on the

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Same File

If your project has collaboration turned on, your Launcher shows a Collaboration tile named "Start <project> Collaboration" — where <project> is the project you chose on the launch screen.

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Full detail: Working together on the same notebook.

Step 10

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: Watch What You Are Spending

Open Resource Tracker — the tile under Admin tools in the Launcher. It shows:

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Step 11

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:  Stop When You Finish

This is the step people forget, and it is the one that costs money.

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Your files stay. Only the running machines stop. An idle session is closed automatically after about an hour, but do not rely on that — an open browser tab can keep it alive.

The one-minute version

1. Go to rp.sciencecloud.nasa.gov and log in.
2. Pick a machine size. Start it.
3. Keep files in ~, in /efs/<project>, or in object storage. Everything else is lost on restart.
4. Work in a notebook. Ask the AI assistant when you are stuck. Need your own packages? Build a conda environment in ~/envs and register it as a kernel.
5. Big job? Dask for parallel work, NBQueue for long background runs.
6. Same file as a colleague? Use the collaboration tile.
7. Check the usage panel.
8. Stop everything when you finish.

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Where to get help

Email If you need help, contact support@sciencecloud.nasa.gov, or use the Science Cloud Support Portal.