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- Start a workspace on demand. No ticket, no waiting.
- Pick the size. From a small 4 GB workspace up to a Standard (4 GB / 4 CPU), Large (8 GB / 4 CPU), Extra Large (16 GB / 4 CPU), or an on-demand node with up to 64 GB / 8 - CPU machine, or a GPU machine. (A GPU is a chip that is fast for AI and image work.)
- Software is ready. Earth-science, Python, and AI software stacks are pre-installed. Power users can bring their own container image. (A container image is a packaged set of software.)
- Many machines for one job. Spread a large analysis over many machines when you need to.
- Background jobs. Send a long notebook to run on its own. Your session stays free, and the job keeps running after you close the browser.
- Capacity follows demand. The platform adds and removes machines automatically.
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- Track cost. Compute hours, compute cost, and storage cost for the project.
- Set and share out budgets. Give each member an allocation. The platform blocks giving out more than the project holds.
- See platform usage. Who is using what, and how much.
- Manage members and roles. Add members, remove members, and promote a member to project admin.
- Turn on collaboration. The project-level switch described above.
- Cross-account AWS access. Register one of your own AWS roles, and the platform hands it to your notebooks at start-up. With it you can use any AWS service in your own account from the Research Platform — for example, start an HPC cluster there, or read a mission bucket. (HPC means high-performance computing: a large group of machines for heavy jobs.) No access keys are copied, and no secrets sit in a notebook.
- Audit trail. Every account, membership, and budget change, with who did it and what changed.
See RPAdmin — the admin console.
Coming
These are agreed and being worked on. No dates. They are listed in rough order of how far along they are.
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- Project-defined machine sizes. A project admin sets the sizes builds and maintains their own team can picklist of server types for their team, instead of everyone using one platform-wide list.
- GPU machines. A GPU is a chip that is fast for AI and image work.
- Session detail. Per-session CPU, memory, and storage detail, to understand a cost and size the next run better.
- Review before publish. A draft-and-approve step before an AI asset goes live in the catalog.
- More clouds in the data browser. Google Cloud and Microsoft Azure support.
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