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Get Started Free →nvidia/earth2studio-deterministic-forecast
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference).
Passed 18 of 23 test cases with the skill, and 9 of 23 without it.
and 9 more in the full report
9 fixed9 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
A user is running Earth2Studio on a CPU-only virtual machine and asks how to configure the deterministic runner for CPU execution. How should the device parameter be set?
| Checked | Without skill | With skill |
|---|---|---|
| CPU inference is identified as unsupported because prognostic models require a CUDA GPU device. | Missed | Passed |
The instructions your model reads when the skill loads.
Guide users through building deterministic (single-member) weather forecast
inference scripts using earth2studio.run.deterministic.
Fetch relevant docs to verify current APIs before recommending components:
| Component | URL |
|---|---|
| Prognostic models | https://nvidia.github.io/earth2studio/modules/models_px.html |
| Data sources (analysis) | https://nvidia.github.io/earth2studio/modules/datasources_analysis.html |
| Data sources (forecast) | https://nvidia.github.io/earth2studio/modules/datasources_forecast.html |
| IO backends | https://nvidia.github.io/earth2studio/modules/io.html |
run.deterministic | https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/run.py |
Fetch prognostic models page. Filter by time horizon, region, VRAM. Note model's:
input_coords["variable"])output_coords["lead_time"])Data source must provide all model input variables. Verify via lexicon at
test cases passed, vs 9 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.