onboard

---

name: onboard description: Onboard a new model to Qualcomm AI Hub Models. Guides authoring a standalone folder (model.py, app.py, demo.py, test.py, manifest.yaml) that the qai-hub-models CLI can install, export, and evaluate. Ships supported_precisions: [float]; quantization is a separate later skill. Use when adding a new model recipe.

Model Onboarding

Target: a self-contained recipe folder driven by the qai-hub-models CLI via manifest.yaml + model.py. There are no generated export.py / evaluate.py scripts. Ship [float] only — add-quantization handles quantized precisions later.

Intake

Ask for two things up front (and only these):

1. Model name or source — HF repo, GitHub URL, PyPI package, or a Googleable name.
2. Dataset and metric — the accuracy dataset (ImageNet, COCO, WIDER FACE, …) and metric (Top-1, mAP, PSNR, WER, …). If the user doesn't offer one, derive it from the model card / task family and propose it — same as you derive license and task type. Then wire it: author the dataset class (either shared under qai_hub_models/datasets/ and listed under datasets: in manifest.yaml, or as a local dataset.py in the recipe folder — either is fine; put it under qai_hub_models/datasets/ if another recipe is likely to reuse it), and add get_evaluator() / get_eval_dataset_classes() / get_calibration_dataset_cls() to model.py. Skip only when the user explicitly declines or the model has no accuracy notion (a generative model with no standard eval benchmark, etc.) — call that out in the final summary.

Dataset+evaluator wiring is part of onboarding, NOT part of add-quantization. It's what lets you torch-verify the float recipe against a reference number, independent of any quantization intent. add-quantization hard-requires it and will send the user back here if it's missing.

Do NOT ask about license, task type, input shape, precisions, or multi-component structure. Derive them.

Derive before asking

• License — HF: https://huggingface.co/api/models/<owner>/<repo> (cardData.license, tags). GitHub: gh api repos/<owner>/<repo> (license.spdx_id). Record the license in manifest.yaml (license_type, license) and proceed. Never hard-block on license for a standalone / external recipe. When the license is non-commercial (CC-BY-NC, RAIL, "research only") or copyleft (AGPL, GPL), warn the user in the final summary so they can make the call — but keep authoring. Dataset licenses get the same treatment: record and warn, never block.
• Task type — model card / config / tags. Match against classification, detection, segmentation, depth, pose, super-res, ASR, TTS, LLM, VLM.
• Reference forward-pass snippet — from the model card / README / PyPI docs. Use for numerical verification against test_task.
• Input spec — preprocessor_config.json / config.json / weights.transforms().
• Sample input — from the card, or a generic one for the task.
• Multi-component structure — obvious encoder/decoder splits, autoregressive / iterative pipelines → CollectionModel (see below).
• Similar repo model — closest sibling to inherit from. Copy its patterns. The sibling's location is not what you copy — the new recipe still goes in the user's CWD as <model_id>/, not under src/qai_hub_models/models/.
• Precisions — always supported_precisions: [float]. Never declare w8a8 / w8a16 here — that's add-quantization.

Ask the user only for ambiguous task/architecture or derivations that genuinely failed. Batch questions into one message. Do NOT ask upfront for license acknowledgement — surface any concerns in the closing summary instead.

At the end, announce what you inferred (license, task, precisions, sibling reference) so the user has one place to correct anything.

Shell rules

• Never inline Python with comments in bash — write to ${TMPDIR:-/tmp}/claude/<name>.py, run with python.
• Never chain shell commands — no &&, ||, ;, pipes, redirects. Each command is a separate Bash call.
• Long-running jobs (compile/profile, 5–15 min) go into a script file and run as a background subagent task.

Design rules

1. Modify PyTorch to get the model onto device. Architecture changes are fine if they preserve numerics.
2. CPU-only in torch. No .cuda(. Grep siblings for GPU-stripping tricks.
3. Upstream code always goes through external_repos:. Not pip install, not SourceAsRoot, not inlined into model.py.
4. Never sys.modules / monkey-patch torch to unpickle a checkpoint. With external_repos: on the import path, whole-object pickles resolve against the real upstream module. Extract state_dict inside from_pretrained and discard the rest. If you're writing sys.modules['models'] = ... or patching SGD.__setstate__, stop. When a pickle references classes by their upstream-relative name (yolov6/v7-style), repo_in_sys_path from qai_hub_models.utils.asset_loaders can help — see "Weights" below.
5. Merge pre/postprocessing into the model where possible — simpler on-device story.
6. Follow existing I/O conventions for the task family.
7. Reuse qai_hub_models/utils/ before writing preprocessing from scratch. Look at similar models' app.py imports.

External Repos

Upstream code (backbones, custom layers, tokenizers) is declared in manifest.yaml and shallow-cloned into <model_folder>/external_repos/<repo_name>/ at import time. Replaces SourceAsRoot and pip-installed forks.

Manifest

external_repos:
  <repo_name>:
    repo_url: https://github.com/<owner>/<repo>.git
    commit_sha: <full 40-char SHA>              # pin to a commit, never branch/tag
    patches_filename: <repo_name>_patches.diff  # optional

<repo_name> is both the folder name under external_repos/ and the Python import path segment. Pick something short that matches the upstream project. Look at existing recipes in src/qai_hub_models/models/ for shape.

Bootstrap

external_repos/__init__.py is generated — do NOT hand-write. Run python qai_hub_models/scripts/run_codegen.py -m <model_id> (in-tree) or qai-hub-models generate-files <path> (standalone). Uses filelock for concurrent-safe imports, populates EXTERNAL_REPO_PATHS.

Importing upstream code

Always package-relative:

from .external_repos.<repo_name>.<upstream_module> import <SomeClass>
from .external_repos import EXTERNAL_REPO_PATHS  # if you need on-disk paths

The recipe folder is imported by its top-level folder name when standalone, and as qai_hub_models.models.<id> when inside the installed package tree — the package-relative form works in both cases.

Never from qai_hub_models.models.<id>.external_repos.<repo_name>... in a standalone recipe — that path only exists inside the installed package, and standalone folders are imported by their folder name, so it doesn't resolve.

The first from .external_repos.<repo_name>... import triggers the fetch, patch, and setup automatically.

If you need the on-disk path to the fetched repo (e.g. to load a bundled Hydra config or supplemental YAML), import the auto-populated dict:

from .external_repos import EXTERNAL_REPO_PATHS
config_dir = EXTERNAL_REPO_PATHS["<repo_name>"] / "configs"

Patches

Fix broken upstream (broken imports, GPU-only code paths, hard-coded absolute paths in the upstream loader, non-tracing ops, optional deps that make a module unimportable) via a <repo_name>_patches.diff file next to external_repos/__init__.py, and set patches_filename: in the manifest.

Generate the diff by hand-editing the cloned repo, then git diff > <repo>_patches.diff — a standard git diff with one diff --git a/… b/… block per file changed. Applied once at fetch time, so subsequent imports see the patched code.

Always prefer patches over runtime find_replace_in_repo (legacy runtime helper). Patches are declarative, reviewable in the PR diff, and applied once instead of every import.

Weights

Weights are separate from code. CachedWebModelAsset + load_torch:

DEFAULT_WEIGHTS = CachedWebModelAsset.from_asset_store(MODEL_ID, MODEL_ASSET_VERSION, "weights.pt")

@classmethod
def from_pretrained(cls, weights=DEFAULT_WEIGHTS):
    checkpoint = load_torch(weights)
    net = SourceModel()
    net.load_state_dict(checkpoint)
    return cls(net)

Whole-object pickle from upstream (torch.save({'model': DataParallel(...), 'optimizer': SGD(...)}, ...))? The resolver finds upstream classes via external_repos: — extract state_dict and discard the rest:

checkpoint = load_torch(weights)
state_dict = checkpoint['model'].module.state_dict()
net.load_state_dict(state_dict)

No sys.modules hacks. If the upstream doesn't ship a state_dict at all, prefer finding a state_dict-only weights source over unpickling optimizer state — if none exists, add a small patch that saves a clean state_dict once and reference that.

If the pickle references classes by their upstream-relative name (yolov6/v7 style), qai_hub_models.utils.asset_loaders.repo_in_sys_path is a context manager that temporarily adds a directory to sys.path around torch.load — useful here, and the correct alternative to sys.modules[...] = ....

Avoid Google Drive links. They gate on interactive consent screens, throttle on quota, expire, and break in CI. If the upstream README's only checkpoint pointer is a Google Drive URL, treat it as "no downloadable weights" and follow the fallback order below.

No downloadable weights (upstream is a training recipe with no checkpoint URL — README says "run python train.py", or only offers a Google Drive link)? In order of preference:

1. Find a mirror on a direct-download host — HuggingFace, GitHub Releases, PapersWithCode, or a downstream fork. HuggingFace is best (stable, CDN-backed, no consent screen); GitHub Releases is a solid second (raw https://github.com/.../releases/download/... URLs work with CachedWebModelAsset).
2. Point at a user-supplied checkpoint — make from_pretrained require an explicit checkpoint arg (no default). Document it in the manifest description and demo --help.
3. Upload once, cache forever — if you have redistribution permission, upload to the shared asset store under a stable MODEL_ASSET_VERSION. One-time out-of-band step by an internal maintainer.

Never run train.py at import time or synthesize weights — the recipe will fail on every fresh install. Never hard-code a Google Drive link as the default weights URL.

Skip external_repos only when the architecture is fully available from torch.nn primitives and torchvision.models / transformers — i.e. no upstream research repo is involved (a common example: a stock classifier torchvision ships directly; the constructor gives you the whole thing).

If you're tempted to reimplement a model architecture inline in model.py (backbone + block classes when an upstream repo already has them), stop and use external_repos:. Inlining is banned — it duplicates upstream, drifts silently, and can create license issues.

Terminology

• model_id — folder name, lowercase snake_case.
• model_name — display name in manifest, dashes, no spaces / underscores.
• template — reusable base under qai_hub_models/models/templates/<name>/, declared in manifest.yaml templates: and resolved transitively.

Folder layout

Standalone folder contains everything the CLI needs. No generated files ship in the folder.

Required

0. __init__.py — exports App, Model, MODEL_ID. Copy any sibling's shape.

1. model.py — BaseModel subclass.

   ALL IMPORTS AT THE TOP. Every import at the top of the file, immediately after the license header. No inline imports inside functions/methods — not for "heavy" imports, not for "only used in one place". Only exception: circular-import avoidance. This is the single most-violated rule in the repo — check before validating.

   ❌ def from_pretrained(cls): from .external_repos... import UpstreamModel — NO ✅ from .external_repos... import UpstreamModel at the top of the file.

   Required methods:

   • from_pretrained(cls) — classmethod to load pretrained weights; all args have defaults.
   • get_input_spec() — staticmethod, InputSpec dict of {input_name: (shape, dtype)}.
   • get_output_spec() — list of outputs.

   Optional overrides:

   • _sample_inputs_impl() — real sample inputs instead of random data.
   • get_hub_compile_options() / get_hub_profile_options() / get_hub_quantize_options() — custom AI Hub flags. get_hub_quantize_options() is required when the model needs a specific range scheme (e.g. --range_scheme min_max). Grep existing detectors for a working pattern.
   • get_unsupported_reason() — mark device attributes (e.g. Hexagon version) that can't be supported.
   • get_eval_dataset_classes() — classmethod, Sequence[type[BaseDataset]]. Default ().
   • get_evaluator() — instance method, returns a BaseEvaluator instance (not a class). Bind hyperparameters (score/IoU thresholds, class count) inside — evaluator is instantiated once and reused.
   • get_calibration_dataset_cls() — instance method, type[BaseDataset] | None. Default None.

Optional

• requirements.txt — model-specific pip deps, pinned versions required.

Not part of the folder

export.py, evaluate.py, perf.yaml, numerics.yaml, release-assets.yaml — not authored, not required. Export / evaluate are CLI-driven; perf and numerics are CI-populated.

README.md is auto-generated by qai-hub-models generate-files. For external recipes (with status: omitted), the CLI renders an external-flavored README whose commands take the recipe folder path — safe to publish alongside the recipe (e.g. on HuggingFace) without misleading a reader.

Architecture patterns

Base classes (qai_hub_models/utils/base_model.py): BaseModel (standard), CollectionModel (multi-component), PrecompiledWorkbenchModel (pre-compiled assets only).

Collection Models

Encoder + decoder splits, autoregressive decoding, iterative refinement — the top-level class inherits from WorkbenchModelCollection (not BaseModel); each component is its own BaseModel subclass; super().__init__({"encoder": encoder, "decoder": decoder}) registers them. CLI compiles each to a separate artifact.

• Manifest: is_collection_model: true.
• App: takes components as separate callables — App(encoder, decoder, version), not a single Callable.
• Demo: use demo_model_components_from_cli_args (not model_from_cli_args).
• Copy the closest sibling in the same task family. Details, KV cache handling, and templates in .claude/docs/collection-models.md.

Templates

qai_hub_models/models/templates/<name>/. Reusable base for a family of models (classifiers, detectors, segmenters, encoder-decoder pairs, etc.). When your model lists a template in its templates: field, the CLI walks the template's own dependencies and installs everything in leaf-first order.

Any folder under templates/ listed in templates: is valid — manifest.yaml is NOT required. Two flavors:

1. With manifest.yaml — declares transitive dependencies (other templates, datasets) that the CLI walks; may also carry an App, Model, and manifest-metadata inheritance.
2. With only requirements.txt — pure "code + pip deps" module. CLI walker handles this natively: template node yields empty transitive deps and just installs its requirements.txt.

Do NOT reject a template because it lacks a manifest — list it in your recipe's templates: and the CLI will install its requirements.txt. grep -l '- <template_name>' src/qai_hub_models/models/*/manifest.yaml shows how neighbors reference it.

Prefer inheriting from a template over rebuilding preprocessing / evaluators from scratch. Browse qai_hub_models/models/templates/ for what's available.

Key utilities

Browse before writing preprocessing from scratch. The fastest way to find the right function is to look at a similar model's app.py / demo.py imports.

• image_processing.py — image → tensor conversion and back:
  • app_to_net_image_inputs() — PIL/numpy → model input tensors in app.py; handles batching, channel order, [0,1] normalization.
  • preprocess_PIL_image() — PIL → float32 [0,1] tensor.
  • normalize_image_torchvision() — ImageNet mean/std normalization.
  • pil_resize_pad() / pil_undo_resize_pad() — aspect-preserving resize with padding, and inverse.
  • torch_tensor_to_PIL_image() — model output → PIL.
• args.py — demo CLI:
  • get_model_cli_parser() / model_from_cli_args() — standard demo arg parsing and model instantiation.
  • get_on_device_demo_parser() / validate_on_device_demo_args() — on-device demo args.
  • add_output_dir_arg() — adds --output-dir to any parser.
• asset_loaders.py — asset fetching:
  • CachedWebModelAsset — download + cache weights from URLs.
  • load_image() — load a PIL image from URL or local path.
  • SourceAsRoot — legacy — do not use for new recipes. Making an upstream GitHub repo importable now goes through external_repos:.
• display.py, draw.py — demo output:
  • display_or_save_image() — show in GUI/notebook or save to disk (used in every demo.py).
  • draw_box_from_xyxy(), draw_points(), create_color_map() — overlays for detection / pose / segmentation.
• compare.py — compute_psnr() between two arrays (denoising / super-res evaluators).
• bounding_box_processing.py — batched_nms(), get_iou(), box_xywh_to_xyxy() for detection postprocessing.

Workflow

1. Create <model_id>/ in the user's CWD. Standalone recipes don't live inside the installed package.
2. Author __init__.py, model.py, app.py, demo.py, test.py, manifest.yaml.
3. Add requirements.txt if needed.
4. Declare deps in manifest.yaml: templates:, datasets:, external_repos:, pre/post pip commands.
5. qai-hub-models generate-files <model_id> — writes README.md and external_repos/__init__.py.
6. qai-hub-models install <model_id>, then run validate (see below).

CLI

Every command takes a <target>: either a folder name or path (my_model) or a bare model id (installed under qai_hub_models). Display names are not accepted. Ids are lowercase snake_case.

Command	What it does
qai-hub-models install <target>	Walks the dep graph, installs each node's requirements.txt and pre/post pip commands leaf-first. --dry-run previews.
qai-hub-models generate-files <target>	Writes auto-generated files (README.md, external_repos/__init__.py). Run after editing manifest.
qai-hub-models export <target> [args...]	Compile + profile on AI Hub. Flags: --target-runtime, --device, --precision, … --help for the full list.
qai-hub-models evaluate <target> [args...]	Accuracy eval using get_evaluator() and get_eval_dataset_classes().
qai-hub-models demo <target> [args...]	Runs the recipe's demo.py. Locally in PyTorch by default; --eval-mode on-device runs it on a hosted device.

Legacy python -m qai_hub_models.models.<id>.export / .evaluate paths are being phased out.

Authoring correctness — qai-hub-models validate

qai-hub-models validate <model_id> is the authoring gate. Not "done" until N passed, 0 failed. Exits nonzero on any FAIL. WARN rows are informational (missing demo / evaluator etc.) and don't need clearing.

Categories:

• Folder shape — required: __init__.py, model.py, manifest.yaml; optional: demo.py, test.py. __init__.py exports MODEL_ID and Model; App optional. external_repos/__init__.py required when manifest declares external_repos:. No from qai_hub_models.models.<id> self-imports.
• Manifest — universal Pydantic schema (id shape, license_type ↔ license URL, python-version reason coupling, orchestrator_runtimes sanity, LLM ↔ llm_details consistency, no mixed precision on non-collection models). id matches folder name. Standalone recipes omit status:. Every external_repos.<name>.commit_sha is a full 40-char SHA.
• Requirements — every entry version-pinned, no conflict with base global_requirements.txt. Same for pre_pip_install_commands / post_pip_install_commands.
• Model code — perform_runtime_model_validation (I/O naming, mixed-precision litemp, eval-dataset wiring). Torch from_pretrained() + forward pass produces the expected number of outputs, all finite, matching declared shapes, deterministic under fixed seed. model.py/app.py have no unguarded .cuda(. model.py has no sys.modules[...] monkey-patches. All imports at the top of every file — the single most-violated rule.
• App — if declared, App(model) or App(**model.components) instantiates cleanly and exposes predict / __call__.
• Datasets / evaluator — WARN if get_eval_dataset_classes() empty (still valid for export).
• URL reachability — HEAD-check license, source_repo, research_paper, every external_repos.<name>.repo_url. Cached 24h.

Pre-validate self-check

Before your first validate run, verify:

1. All imports at the top? Open each .py — no import inside a function/method. Most-violated rule.
2. No self-referential imports? No from qai_hub_models.models.<id>... in a standalone recipe. Use relative (.external_repos, .templates).
3. External-repos bootstrap present? If manifest declares external_repos:, external_repos/__init__.py exists — qai-hub-models generate-files <path> writes it.
4. No status: in manifest.yaml? grep '^status:' manifest.yaml returns nothing.
5. supported_precisions is [float]? No w8a8 / w8a16 here — that's add-quantization.
6. No technical_details: in manifest.yaml? grep '^technical_details:' manifest.yaml returns nothing.
7. Dataset __init__ takes input_spec? Never raw input_height / input_width / image_size / resolution.
8. Dataset + evaluator wired? Unless the user declined or the model has no accuracy notion: model.py implements get_evaluator() / get_eval_dataset_classes() / get_calibration_dataset_cls(). If not, wire it now — do NOT defer to add-quantization, which only flips the precision list.

Iterate-until-green

Read each FAIL row's detail, apply a targeted fix, rerun with --no-install. Repeat until 0 failed.

Progress rule (anti-runaway): two consecutive attempts on the same FAIL row producing the same detail → stop. Usually means an incompatible base-pin conflict, a genuinely broken upstream URL, or a decision only the user can make. Report what was tried and the persistent row.

No bypassing. No commenting out check code, no # noqa in the recipe, no stub swaps. If a check only passes with a suppression, that IS the dead-end — report it.

What validate does NOT check

• Numerical parity with the reference. Forward pass confirms shapes and no-crash, not correctness. Run the reference on a fixed input and compare against your model.py.
• License classification. The validator confirms license_type matches its license URL. Any license is accepted — document it under license_type / license and warn the user in the final summary if it's non-commercial or copyleft. Do not hard-block. Same treatment applies to dataset licenses.
• Qualitative demo correctness. Classifier picks the right class, detector boxes align, mask matches the scene, restored image is visibly cleaner.
• technical_details. Autofilled post-compile — do not hand-write.

Operational validation of a written recipe (install cold+warm, pytest, pre-commit, export, on-device evaluate) is out of scope for this skill — this skill teaches authoring, not operating. See /ai-hub-models:validate-on-device.

Preprocessing verification

Verify the model's expected input range before writing any code. The single most common source of silently-wrong results — model will trace, export, and produce plausible-looking output with the wrong normalization.

• Read weights.transforms() source or the official demo code.
• Common ranges: [0, 1], [-1, 1] (mean=0.5, std=0.5), [0, 255], ImageNet mean/std.
• Verify input resolution against the model's default config.
• Image inputs to the model at the App boundary are [0, 1]. Convert to native format inside forward().
• After implementing, numerically compare your output against the reference on the same input. "Looks reasonable" is not a check.

On-device deployment

Compilation / profiling / on-device eval are NOT part of this skill. Once qai-hub-models validate reports 0 failed, hand off to /ai-hub-models:validate-on-device.

Iterative/recurrent models likely need CollectionModel up front — profiling failures downstream will confirm, but the pattern belongs in the recipe from day one. See .claude/docs/collection-models.md.

Adding a dataset + evaluator

Do this during onboarding as the default path — see intake item 2. Skip only when the user explicitly declines or the model has no accuracy notion. This is NOT the quantization step — quantization is a separate skill (add-quantization) and never author w8a8 / w8a16 here. The reason to wire the dataset+evaluator now is torch accuracy verification against a reference, which is orthogonal to quantization.

Dataset

Two acceptable homes for the dataset class, both inheriting BaseDataset:

• Shared — under qai_hub_models/datasets/<name>/. Use this when another recipe is likely to reuse the dataset. List it in the model's manifest.yaml under datasets: so the CLI resolves any deps it declares.
• Local — as dataset.py in the recipe folder. Fine for model-specific datasets. No manifest entry needed.

Either way:

• __init__ takes input_spec: InputSpec | None = None — never raw input_height / input_width / image_size / resolution. Derive width/height from the tensor shape in input_spec. When None, fall back to a default InputSpec inside __init__ and extract dims from that. Hard rule — raw dim parameters diverge from the model's declared shape and break calibration/eval, which passes input_spec from the model automatically.
• Do NOT override configure() unless the dataset genuinely needs external setup (files behind a license wall, large archives requiring manual download). Default raises a helpful NotImplementedError for auto-downloadable data. A configure() that just forwards to __init__ or re-runs _download_data()'s work is boilerplate.

Evaluator

Under the model folder (single-consumer) or qai_hub_models/models/templates/<name>/ (multi-consumer). Inherit the base evaluator; implement task-appropriate metrics.

Model.py wiring

@classmethod
def get_eval_dataset_classes(cls) -> Sequence[type[BaseDataset]]:
    return [YourDataset]

def get_calibration_dataset_cls(self) -> type[BaseDataset] | None:
    return YourCalibrationDataset

def get_evaluator(self) -> BaseEvaluator:
    return YourEvaluator(num_classes=80, score_threshold=0.05)

get_evaluator() returns an instance with hyperparameters bound — evaluator is instantiated once and reused across batches.

Reinstall (deps may have changed): qai-hub-models install <target>. Torch-verify: qai-hub-models evaluate <target> --precision float --num-samples 100 --skip-device-accuracy.

See .claude/docs/onboarding/datasets-and-evaluators.md for extended guidance.

Model assets

Three patterns, pick whichever fits:

A. Shared asset store (what most repo models do). Fetches on demand via CachedWebModelAsset.from_asset_store(MODEL_ID, MODEL_ASSET_VERSION, "test_images/input.jpg"), resolving to a URL under Qualcomm's public S3 bucket. Weights and test fixtures are uploaded there separately (out of scope for this skill); the model folder stays tiny. Declare MODEL_ASSET_VERSION = 1 at module scope; reference assets by relative path. Bump when uploading a new asset set — old versions stay pinned.

B. Ship inside the folder (fine for standalone recipes — folder is self-contained on a fresh checkout without depending on an asset-store upload). Put in <model_folder>/assets/, load by relative path in from_pretrained() / demo.py / test.py. Small fixtures (test images, tiny reference outputs) commit directly; large binaries (multi-MB checkpoints, video) that can't be regenerated from a public source → host at a public URL and load via CachedWebModelAsset URL form (not from_asset_store). Do NOT invent a MODEL_ASSET_VERSION here — the constant only makes sense with from_asset_store.

C. Reuse a sibling's fixture (bootstrap shortcut). For a new detector, any detection image works; new classifier, any classification image. Instead of uploading a fresh asset, point at the sibling's existing fixture:

DEMO_IMAGE = CachedWebModelAsset.from_asset_store("<sibling_id>", <version>, "<sibling_filename>")

Prefer C over uploading a fresh asset for the initial recipe. Swap in a follow-up.

Requirements

• Pinned exact versions in requirements.txt (e.g. torch==2.0.1).
• Check global_requirements.txt before adding new deps.
• Upstream research repos → external_repos:, never requirements.txt or pip commands.
• pre_pip_install_commands / post_pip_install_commands only for env-level needs (CPU vs GPU wheels, pip uninstall before an incompatible upgrade). Gate with machine: cpu / machine: gpu.