resources/ — your model lives here
Everything in this folder is copied into the container image at build time and is available at inference. This is where your trained model belongs.
| File | What it is | Replace it? |
|---|---|---|
model.py |
the model definition (DummyModel, a trivial CNN) |
yes — with your architecture |
dummy_weights.pt |
a tiny checkpoint so the template runs out of the box | yes — with your trained weights |
inference.py imports from here:
from resources.model import DummyModel
WEIGHTS_PATH = RESOURCES_PATH / "dummy_weights.pt"
Keep that wiring, or point it at whatever you name your own files.
Things to get right
There is no internet at inference time. The container runs with networking
disabled, so nothing can be downloaded at runtime — no model hubs, no checkpoints,
no tokenizer files, no fonts. Everything your model touches must be inside the
image. If you use a library that lazily fetches weights on first call (many
transformers, timm, and open_clip entry points do), pre-download during the
Docker build and load from a local path instead.
Load your model once. One container run answers a whole batch of questions, so
the checkpoint is loaded once and reused across every question in that batch. Keep
model loading outside the per-question loop in inference.py — that is where the
setup allowance in the latency budget comes from.
Large checkpoints need splitting. A single image layer cannot exceed 50 GB,
and each Docker COPY is one layer. If your weights are bigger than that, split
them into chunks, copy them with several COPY instructions, and reassemble at
runtime — see the comment in the Dockerfile.
Big weights make big images. The whole image is pulled before your code starts. That pull is infrastructure overhead and is not charged against your latency budget, but it does slow every submission cycle, so keep the image only as large as it needs to be.