creamRoutes DisCo Researcher to Cream-family vision NAS, compression, distillation, and relative-position-encoding workflows across the AutoFormer, AutoFormerV2, Cream, CDARTS, EfficientViT, MiniViT, TinyCLIP, TinyViT, and iRPE project families.
Independent GitHub snapshot · Automated classification · Original documentation governs
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.
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.
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 | 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" |
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.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 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.