# ML Training Pipeline — manually triggered
#
# Trigger via GitLab UI: CI/CD → Pipelines → Run pipeline
#   Set CI configuration file to: .gitlab-ci-training.yml
#   Set variables as needed (MODELS, SYMBOLS, PHASE, etc.)
#
# Or via API:
#   curl -X POST --fail \
#     -F "token=$TRIGGER_TOKEN" \
#     -F "ref=main" \
#     -F "variables[MODELS]=all" \
#     -F "variables[SYMBOLS]=ES.FUT" \
#     -F "variables[PHASE]=full" \
#     "$CI_API_V4_URL/projects/$CI_PROJECT_ID/trigger/pipeline"

stages:
  - prepare
  - trigger

variables:
  SYMBOLS: "ES.FUT"
  MODELS: "all"
  PHASE: "full"
  MAX_PARALLEL: "10"
  EPOCHS: "50"
  HYPEROPT_TRIALS: "20"
  RUN_ID: ""
  REGISTRY: rg.fr-par.scw.cloud/foxhunt-ci

workflow:
  rules:
    - if: $CI_PIPELINE_SOURCE == "web"
    - if: $CI_PIPELINE_SOURCE == "trigger"
    - if: $CI_PIPELINE_SOURCE == "api"

generate-jobs:
  stage: prepare
  image: alpine:3.19
  tags:
    - kapsule
  script:
    - apk add --no-cache bash coreutils
    - |
      if [ -z "$RUN_ID" ]; then
        export RUN_ID=$(date +%Y%m%d-%H%M%S)
      fi
    - bash scripts/generate-training-pipeline.sh
    - echo "--- Generated pipeline ---"
    - cat .training-generated.yml
  artifacts:
    paths:
      - .training-generated.yml
    expire_in: 1 day

run-training:
  stage: trigger
  trigger:
    include:
      - artifact: .training-generated.yml
        job: generate-jobs
    strategy: depend