b5da424dfe
The portfolio data, risk and mean-CVaR optimisation service, prepared for students who run it on their own DGX Spark without root, Docker or Tailscale. - deploy/apptainer/qc.sh: install, start/stop/status/logs, cli/api/python, data import/export and Yahoo setup/update. cuOpt and quantcore run as two Apptainer instances of the user and talk over 127.0.0.1; the API and the test bench listen on 127.0.0.1 only. Builds quantcore.sif without root, or falls back to a plain Python image plus a virtualenv when the Spark allows no unprivileged builds. Detects taken ports and suggests free ones. - deploy/apptainer/quantcore.def + constraints.txt: the image, with the library versions of the verified production container pinned. - cuOpt under Apptainer: NVIDIA's image sets HOME=/opt/cuopt, read-only in a SIF, so the solver died on its first job; qc.sh gives it a writable home. - solvers: a cuOpt that disappears during a solve now falls back to HiGHS instead of answering 500 (requests' ConnectionError escaped the fallback). - client: idempotent GETs retry a dropped keep-alive connection, which made `quantcore-api update --wait` fail when a poll met uvicorn's 5 s timeout. - config: cuOpt defaults to http://127.0.0.1:5000 and `quantcore serve` to 127.0.0.1; the Docker images keep passing their own values. - docs/SPARK_APPTAINER.md: the students' guide; README, USAGE, ARCHITECTURE and SPARK_DEPLOYMENT point to it. Tested on a DGX Spark (GB10, Apptainer 1.5.4, unprivileged user) in both modes: selftest on every endpoint, optimisation on cuOpt, Yahoo update, port conflicts; 139 tests pass in the container against the live cuOpt. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
25 lines
1.1 KiB
Docker
25 lines
1.1 KiB
Docker
# quantcore -- portfolio quant service for the DGX Spark (aarch64) and any x86-64 host.
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# Build context is the repository root: docker build -f deploy/Dockerfile -t local/quantcore:0.1 .
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FROM python:3.12-slim
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ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 PIP_NO_CACHE_DIR=1 \
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QUANTCORE_DATA_DIR=/data QUANTCORE_CUOPT_URL=http://cuopt:5000 \
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OPENBLAS_NUM_THREADS=4 OMP_NUM_THREADS=4 MKL_NUM_THREADS=4
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# BLAS threading: the innovation filter runs thousands of small (N x N) eigendecompositions;
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# OpenBLAS with all 20 cores of the Spark is ~50x slower on those than with 1-4 threads.
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RUN apt-get update && apt-get install -y --no-install-recommends curl tini \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY pyproject.toml README.md ./
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COPY quantcore ./quantcore
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RUN pip install .
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VOLUME ["/data"]
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EXPOSE 5001
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HEALTHCHECK --interval=60s --timeout=10s --retries=2 CMD curl -sf http://localhost:5001/health >/dev/null || exit 1
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ENTRYPOINT ["/usr/bin/tini", "--"]
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CMD ["uvicorn", "quantcore.api.app:app", "--host", "0.0.0.0", "--port", "5001", "--workers", "1"]
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