| FROM --platform=linux/amd64 pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime AS segment-algorithm-amd64 | |
| # torch 2.11.0 + torchvision are pre-installed in the base image (CUDA 12.8). | |
| # requirements.txt only lists additional dependencies. | |
| # | |
| # This base image is the recommended default, not a hard requirement. It is chosen | |
| # because it is the ONE PyTorch build that runs on both evaluation GPUs unmodified: | |
| # an NVIDIA RTX PRO 6000 Blackwell (compute capability sm_120) and an NVIDIA L40S | |
| # (sm_89). Its CUDA 12.8 kernels cover sm_75/80/86/90/100/120 — Blackwell natively | |
| # and the L40S via its sm_86 kernels (a CUDA binary runs on any later minor | |
| # revision of the same major capability). So one image serves whichever GPU you | |
| # select on the platform, with no rebuild. | |
| # | |
| # If you change it, know the two limits it steers around: CUDA 12.4/12.6 wheels | |
| # stop at sm_90 and abort on Blackwell ("not compatible with the current PyTorch | |
| # installation"); CUDA 13.0 wheels cover both GPUs but need a host driver >= 580 — | |
| # the RTX PRO 6000 fleet has it (595), the L40S fleet does not. If your submission | |
| # targets ONLY the RTX PRO 6000 (offered on the SEGMENT and PROCEDURE tracks), a | |
| # newer CUDA/torch is a valid choice — see the guard below for the one line to relax. | |
| ENV PYTHONUNBUFFERED=1 | |
| # ── System packages ─────────────────────────────────────────────────────────── | |
| # The slim -runtime base image ships neither the shared libraries that | |
| # opencv-python (a dependency of orena-focus) needs at import time, nor any | |
| # compiler toolchain. Install system packages HERE, while the build still runs | |
| # as root — everything below runs as the unprivileged 'user'. If one of your | |
| # pip dependencies compiles from source (e.g. flash-attn), add build-essential. | |
| RUN apt-get update \ | |
| && apt-get install -y --no-install-recommends \ | |
| libgl1 \ | |
| libglib2.0-0 \ | |
| libxcb1 \ | |
| && rm -rf /var/lib/apt/lists/* | |
| RUN groupadd -r user && useradd -m --no-log-init -r -g user user | |
| USER user | |
| WORKDIR /opt/app | |
| COPY --chown=user:user requirements.txt /opt/app/ | |
| # If one of your dependencies compiles CUDA extensions during the build (e.g. | |
| # flash-attn from source), uncomment this so it emits kernels for both evaluation | |
| # GPUs rather than only for the card in your build machine: | |
| # ENV TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;9.0;12.0+PTX" | |
| # --break-system-packages: this base image's Python is Ubuntu's system Python, | |
| # which is marked externally-managed (PEP 668), so pip refuses to install without | |
| # it. Nothing is actually broken — `--user` still installs into the unprivileged | |
| # user's own ~/.local, leaving the system packages untouched. | |
| RUN python -m pip install \ | |
| --user \ | |
| --break-system-packages \ | |
| --no-cache-dir \ | |
| --no-color \ | |
| --requirement /opt/app/requirements.txt | |
| # ── Guard: is torch still the recommended CUDA 12.8 build? ──────────────────── | |
| # This enforces the recommendation above so an ACCIDENTAL swap cannot reach the | |
| # platform unnoticed. If one of your dependencies needs a torch version this image | |
| # does not have, pip quietly replaces the CUDA 12.8 build with the default PyPI | |
| # wheel (built against CUDA 13.0). That wheel needs a host driver >= 580.65.06; the | |
| # L40S fleet runs 570.211.01, so it cannot start there. Failing the build now beats | |
| # failing every job after you submit. | |
| # | |
| # Both halves matter. The CUDA version is what the driver floor depends on, and it | |
| # is NOT visible in the architecture list: the CUDA 13.0 wheel advertises the very | |
| # same sm_ list as the 12.8 one. The arch list is still checked because it is what | |
| # an older CUDA 12.4/12.6 wheel gets wrong. get_arch_list() returns [] with no GPU | |
| # visible — a docker build never has one — so the flags compiled into the wheel are | |
| # read directly. | |
| # | |
| # Targeting the RTX PRO 6000 only, and want a newer CUDA? Then this default no | |
| # longer applies to you: drop the `cuda.startswith('12.8')` assert below (keep the | |
| # sm_120 arch assert, so a build with no Blackwell kernels is still caught). | |
| RUN python -c "import torch; \ | |
| flags = torch._C._cuda_getArchFlags() or ''; \ | |
| cuda = torch.version.cuda or ''; \ | |
| print('torch', torch.__version__, '| cuda', cuda, '|', flags); \ | |
| assert cuda.startswith('12.8'), 'torch was replaced by a CUDA ' + cuda + ' build; the L40S driver cannot run it'; \ | |
| assert 'sm_120' in flags and 'sm_86' in flags, 'torch build misses a required GPU architecture: ' + repr(flags)" | |
| # ── Model definition + weights ──────────────────────────────────────────────── | |
| # resources/ holds the model definition (model.py) and a small committed dummy | |
| # checkpoint (dummy_weights.pt). For a real submission, replace dummy_weights.pt | |
| # with your trained checkpoint. | |
| # | |
| # ECR layer-size limit: a single image layer must not exceed 50 GB, and each | |
| # COPY instruction produces exactly one layer. A checkpoint large enough to push | |
| # one COPY past that limit must be split into chunks and copied with several COPY | |
| # instructions (one layer each), then reassembled at runtime — e.g. pre-split | |
| # with `split -b 45G weights.pt resources/weights.part-` and: | |
| # COPY --chown=user:user resources/weights.part-aa /opt/app/resources/ | |
| # COPY --chown=user:user resources/weights.part-ab /opt/app/resources/ | |
| # COPY --chown=user:user resources/weights.part-ac /opt/app/resources/ | |
| # reassembling with `cat resources/weights.part-* > weights.pt` before load. | |
| COPY --chown=user:user resources/ /opt/app/resources/ | |
| COPY --chown=user:user inference.py /opt/app/ | |
| ENTRYPOINT ["python", "inference.py"] | |