Cream

Use this skill when the user is working with the Cream monorepo or one of its vision-model subprojects. It is a router, not a manual: pick the matching sub-skill first, then read the bundled references and run the bundled checks.

What this skill covers

• AutoFormer and AutoFormerV2 search / evaluation / subImageNet workflows.
• Cream and CDARTS NAS, retrain, test, and dataset-prep workflows.
• EfficientViT classification plus downstream detection / segmentation.
• MiniViT Mini-DeiT and Mini-Swin distillation / compression workflows.
• TinyCLIP OpenCLIP-based inference, evaluation, and pretraining.
• TinyViT model creation, evaluation, sparse-logit saving, and training.
• iRPE integration for DeiT and DETR.

Quick setup

Use a CUDA-capable inspection environment with the shared packages validated for this repo family:

python -m pip install torch torchvision timm==0.4.12 yacs easydict termcolor ftfy regex tqdm huggingface_hub webdataset braceexpand pandas psutil tensorboard tensorboardX graphviz scikit-image opencv-python thop fvcore submitit onnx onnxruntime pytest open_clip_torch

For legacy NAS routes, the bundled compatibility notes explain the torch._six shim and other historical-dependency caveats.

Minimal environment check

python scripts/check_environment.py --modules torch,torchvision,timm,open_clip,yacs,easydict,ftfy,regex,webdataset,huggingface_hub,submitit,fvcore
python scripts/check_dataset_layout.py --help

Route map

Route	Read this sub-skill for	Typical user phrases
sub-skills/nas-search/	AutoFormer, AutoFormerV2, Cream, and CDARTS search / retrain / test / dataset prep	"AutoFormer", "S3", "Cream NAS", "CDARTS", "subImageNet", "search architecture"
sub-skills/efficientvit/	EfficientViT classification and downstream detection / segmentation	"EfficientViT", "ImageNet eval", "COCO downstream", "RetinaNet", "Mask R-CNN"
sub-skills/minivit/	Mini-DeiT and Mini-Swin distillation / compression workflows	"MiniViT", "Mini-DeiT", "Mini-Swin", "weight multiplexing"
sub-skills/tinyclip/	TinyCLIP inference, evaluation, and pretraining	"TinyCLIP", "OpenCLIP", "zero-shot", "pretrain", "weight inheritance"
sub-skills/tinyvit/	TinyViT evaluation, sparse-logit saving, finetuning, and training	"TinyViT", "save logits", "22k to 1k", "higher resolution"
sub-skills/irpe/	iRPE for DeiT and DETR	"iRPE", "relative position encoding", "DeiT with iRPE", "DETR with iRPE"

Shared helpers

• scripts/check_environment.py — import and backend smoke check for the shared Python environment.
• scripts/check_dataset_layout.py — validate ImageNet, ImageNet-22k, subImageNet, and COCO-style layouts.
• scripts/check_custom_ops.py — report whether the optional rpe_ops extensions are built.
• scripts/check_legacy_imports.py — probe the legacy NAS modules under a modern-torch compatibility shim.

Cross-cutting references

• references/compatibility.md — read when you need the verified environment baseline, legacy-torch caveats, or the TinyCLIP package note.
• references/dataset-layouts.md — read when you need the canonical ImageNet, ImageNet-22k, subImageNet, or COCO folder shapes.
• references/troubleshooting.md — read first for repo-wide failure patterns before you narrow the issue to a specific sub-skill.

Read provenance before refreshing

Read references/repo-provenance.md when you need to decide whether this skill still matches a Cream checkout. If the repository commit, dirty state, or major evidence paths changed, refresh the skill instead of reusing it blindly.

Notes

• The generated skill is self-contained. Do not rely on the original repository checkout at runtime.
• Project-specific commands, API signatures, model names, and troubleshooting details live in the sub-skill references.
• Do not run the source repository's heavy training, search, or download commands unless the user explicitly asks for them.