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Before

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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?

You need one of these, and a project:

• a NASA identity, or
• a STAR ID.

See How to get access.)

Step 1

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: Log

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In

1. Open rp.sciencecloud.nasa.gov in your web browser.
2. Sign in. You

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1. will be briefly redirected to the Science Cloud login service (STAR) to verify your identity.

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1. Once signed in, you will land on the platform's main launch screen. There is nothing to install.

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

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Step 2

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: 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

The launch screen is called Server Options.

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Which software set? The Images list holds the ready-made science stacks — for example Earth Science Server (with AI Intelligence). An Other image box lets a project use its own image. (An image is a packaged set of software.)

Which project? The Project list picks which project this session belongs to. It decides which project storage you can reach and which budget the session is charged to.

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.

Which software stack do you need? Select a ready-made science stack from the Images dropdown list. (An image is a packaged set of software, for example, Earth Science Server with AI Intelligence). (Note: If your project uses a custom container image, you can enter it into the "Other image" box).

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.

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Step 3 — 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 — Know 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.

The rule: keep your work in ~, in /efs/…, or in object storage. Everything else disappears when your workspace restarts.

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Two things worth knowing:

• EFS shows you every project you belong to. Open /efs/ and you see a folder for each one. You cannot open other people's folders — the permissions stop you.
• The file browser and the terminal show the same files, laid out differently. The left-hand file browser and the command line have the same permissions. Do not worry if the two views look different.

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The same files, two ways: the file browser on the left, a terminal on the right. The user list is blurred on purpose.

Which one should I use?

Step 5 — Do the science

Open the Launcher and start a notebook. Write code in a cell. Press Shift+Enter to run it. The result appears under the cell.

This part is normal Jupyter. If you have used Jupyter anywhere else, you already know it.

Step 5b — Make your own kernel from a conda environment

Sometimes the ready-made software set does not have the package versions you need. You can build your own environment and use it as a kernel. (A kernel is the entry in the notebook's "choose your language" menu. An environment is your own private set of packages.)

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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.

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Step 6 — Get AI help while you work

The Notebook Intelligence tool puts an AI assistant in JupyterLab.

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The Notebook Intelligence chat panel, open beside the Launcher.

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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 — Browse and pull in 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 — Need more power? Two choices

Choice A — many machines at once (Dask)

Use this when one analysis is too slow or too big for your workspace. Dask spreads the work over many machines and brings the answer back.

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Choice B — send the job to the background (NBQueue)

Use this when a notebook takes a long time and you do not want to sit and watch it.

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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 — Work with your team on the 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 — Watch what you are spending

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

• This month — hours used, compute cost, and storage cost split by type.
• Funding period to date — what you have used, what you were given, and what is left.
• When the numbers were last updated.

Step 11 — Stop when you finish

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

• Stop your notebook when you are done for the day.
• Shut down any Dask cluster.
• Stop the collaboration session when the group is finished.

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.

Where to get help

Email support@sciencecloud.nasa.gov, or use the Science Cloud Support Portal.