Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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Create Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Do these steps IN ORDER. Do not skip any step.
earth2studio/models/px/<name>.py with triple inheritancetest/models/px/test_<name>.py with mock testsuv run pytest test/models/px/test_<name>.py -vmake format && make lint⚠️ CRITICAL: Always use
uv runfor Python commands:
- ✅
uv run pytest .../uv run python ...- ❌
pytest .../python ...(missing dependencies)Stuck or wrong output: Do not keep retrying the same fix. Follow Self-Improvement to patch this skill before continuing.
Implement a prognostic model wrapper connecting third-party ML weather models to Earth2Studio. Prognostic models time-integrate forward—given initial state, they predict future states by stepping through time (e.g., 6-hour increments).
| Context | Location |
|---|---|
| Harbor eval | Write to /workspace/output/earth2studio/models/px/... |
Harbor + --copy-repo | Full checkout at /workspace/repo |
| Local clone | Directory with pyproject.toml |
Never read evals/targets/ — grader references only.
Load on demand during the matching step:
| File | Content | Load at |
|---|---|---|
references/skeleton-template.py | Full model skeleton with FILL comments | Steps 3–6 |
references/method-templates.py | Canonical method implementations | Steps 4–6 |
references/testing-guide.py | Test skeleton and mock patterns | Step 7 |
references/validation-guide.md | Comparison scripts, PR, code review | Steps 10–11 |
If $ARGUMENTS provided, use it. Otherwise ask:
Please provide a reference inference script URL/path.
Analyze: packages, architecture, I/O shapes, time step, resolution, checkpoint.
Propose pyproject.toml group (alphabetical, add to all). Every
prognostic model must have an optional dependency extra, even when no packages
are required:
model-name = ["package1>=version", "package2"]
# or, when no additional packages are required:
model-name = []
[CONFIRM] Present dependencies and ask user to approve.
Edit pyproject.toml: add the model extra alphabetically, even if it is
empty, and update the all aggregate.
File: earth2studio/models/px/<lowercase>.py
Required inheritance (all three):
class ModelName(torch.nn.Module, AutoModelMixin, PrognosticMixin):
Required imports:
import numpy as np
import torch
from earth2studio.models.auto import AutoModelMixin, Package
from earth2studio.models.batch import batch_coords, batch_func
from earth2studio.models.px.base import PrognosticMixin
from earth2studio.models.utils import create_coords_from_lat_lon, handshake_dim
from earth2studio.lexicon import E2STUDIO_VOCAB
from earth2studio.utils import check_optional_dependencies
from loguru import logger
SPDX header (required at top of every .py file):
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: Apache-2.0
Canonical method order:
__init__ 2. input_coords 3. output_coords (@batch_coords)load_default_package 5. load_model 6. to (optional)__call__ (@batch_func) 9. _default_generatorcreate_iteratorinput_coords rules:
batch: np.empty(0)time: np.empty(0) (dynamic)lead_time: starts at np.timedelta64(0, "h")lat: 90 to -90 (north to south); this is the public Earth2Studio convention even if the source model uses the opposite orderlon: 0 to 360input_coords or output_coordsE2STUDIO_VOCAB (282 entries in earth2studio/lexicon/base.py)output_coords: Use handshake_dim/handshake_coords for input validation, then increment lead_time. Prefer a shared coordinate-check helper and call it from output_coords, __call__, and iterator setup before model execution.
__call__: @batch_func decorated, shape (batch, time, lead_time, var, lat, lon).
Reshape to model format → call model → reshape back.
create_iterator: MUST yield initial condition first (step 0).
Use front_hook/rear_hook for perturbation injection.
load_default_package: Lock HuggingFace URLs: hf://org/repo@commit
load_model: Use package.resolve(), map_location="cpu", eval() mode,
decorate with @check_optional_dependencies().
File: test/models/px/test_<name>.py
Required tests:
| Function | Purpose |
|---|---|
test_<model>_call | Single forward pass (parametrize device/time) |
test_<model>_iter | Iterator produces sequence |
test_<model>_exceptions | Invalid coords raise errors |
test_<model>_package | Real weights (@pytest.mark.package) |
Create PhooModelName dummy matching interface for mock tests.
Run tests:
uv run pytest test/models/px/test_<name>.py -m "not package" -v
uv run pytest test/models/px/test_<name>.py::test_<model>_package --package -v
Do not omit the package test. If arbitrary random inputs are not physically valid for the real checkpoint, use a stable model-appropriate synthetic input while still loading real weights and running a forward pass.
earth2studio/models/px/__init__.py (alphabetical)docs/modules/models_px.rst (alphabetical). This is required for
every new prognostic model so the API docs include the generated page.docs/userguide/about/install.md (alphabetical tab) for the
model extra, even when the extra is empty. Include model-specific notes plus
both pip install earth2studio[model-name] and
uv add earth2studio --extra model-name instructions.CHANGELOG.md under ### Added. This is required for every new
prognostic model.Format and lint:
make format && make lint && make license
Follow references/validation-guide.md. Create uncommitted vanilla, E2S,
comparison, and sanity-check scripts; do not commit generated outputs or images.
Use PR-safe placeholders for plots so the user can upload images manually.
[CONFIRM] User must visually inspect plots before proceeding.
Follow references/validation-guide.md and use:
references/pr-body-template.mdreferences/pr-comment-template.mdBefore creating the PR, verify pyproject.toml has the model extra, the
all extra includes it, install docs include both pip and uv commands, and
docs/modules/models_px.rst plus CHANGELOG.md are updated.
Do not include machine names, absolute paths, device inventory, or uploaded image links in PR text. Use plot placeholders instead.
User: Create IdentityModel - returns input unchanged, 6h step, 181x360, vars: t2m, u10m, v10m, msl
Agent: [reads SKILL.md, creates identity.py with triple inheritance,
creates test_identity.py, runs pytest, runs make format && lint]
User: Add Pangu-Weather wrapper
GitHub: https://github.com/198808xc/Pangu-Weather
Agent: [reads SKILL.md, fetches inference.py, creates pangu.py,
creates test_pangu.py, runs pytest]
@property
def input_coords(self) -> CoordSystem:
return CoordSystem({
"batch": np.empty(0),
"time": np.empty(0),
"lead_time": np.array([np.timedelta64(0, "h")]),
"variable": np.array(["t2m", "u10m", ...]),
# Public Earth2Studio convention is north-to-south latitude.
"lat": np.linspace(90, -90, 181),
"lon": np.linspace(0, 359, 360),
})
@batch_coords()
def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
output = input_coords.copy()
output["lead_time"] = input_coords["lead_time"] + np.timedelta64(6, "h")
return output
def create_iterator(self, x, coords):
yield x, coords # Initial condition (step 0)
while True:
x, coords = self.front_hook(x, coords)
x, coords = self(x, coords)
x, coords = self.rear_hook(x, coords)
yield x, coords
| Error | Solution |
|---|---|
OptionalDependencyFailure | uv add --optional <group> <pkg> |
| Coordinate handshake fails | Check handshake_dim indices match dim position |
| Iterator wrong shapes | Debug reshape logic with random input |
ModuleNotFoundError: pytest | Use uv run pytest not pytest |
DO:
uv run python for ALL Python commandsloguru.logger, never print()torch.nn.Module + AutoModelMixin + PrognosticMixincreate_iteratorfront_hook()/rear_hook() in _default_generatorDON'T:
evals/targets/아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
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Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.