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>
31 lines
1.2 KiB
YAML
31 lines
1.2 KiB
YAML
# quantcore for a platform team: the JSON API and /docs, no test bench.
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#
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# docker compose -f deploy/docker-compose.students.yml up -d --build
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# curl http://localhost:5001/health
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#
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# The MILP solver is optional. Point QUANTCORE_CUOPT_URL at a cuOpt server if you have one (on a DGX
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# Spark: http://<host>:5000); if it is unreachable, every optimisation falls back to HiGHS on the CPU
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# -- slower on large problems, same model, same answer, and the result says which solver produced it.
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#
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# The data directory is mounted, not baked in: run the setup once (POST /jobs/setup, ~15 min) or copy
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# a prepared ./data next to this file.
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services:
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quantcore:
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build:
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context: ..
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dockerfile: deploy/Dockerfile
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image: quantcore:0.1
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container_name: quantcore
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restart: unless-stopped
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ports: ["5001:5001"]
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environment:
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QUANTCORE_DATA_DIR: /data
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QUANTCORE_CUOPT_URL: ${QUANTCORE_CUOPT_URL:-http://cuopt:5000}
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QUANTCORE_CUOPT_TIME_LIMIT: "300"
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QUANTCORE_ENABLE_UI: "false" # the test bench is not part of the deliverable
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QUANTCORE_LOG_LEVEL: INFO
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OPENBLAS_NUM_THREADS: "4" # small-matrix linear algebra is much slower with all cores
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OMP_NUM_THREADS: "4"
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volumes:
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- ../data:/data
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