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candle-annotator/openspec/changes/code-review-fix/specs/ml-training/spec.md
Marko Djordjevic c327ba3370 bind: MLflow port to 127.0.0.1:5000:5000 in docker-compose.yml 
Changes:
- Updated docker-compose.yml MLflow service port binding from 5000:5000 to 127.0.0.1:5000:5000
  to restrict access to localhost only for security
- Marked task 1.7 as complete in tasks.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-18 10:58:11 +01:00

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ADDED Requirements

Requirement: Training resource limits

The POST /training/start endpoint SHALL enforce resource limits: the training dataset file size SHALL not exceed 500MB, and the training thread SHALL have a configurable timeout (default: 30 minutes). If the timeout is exceeded, the training thread SHALL be marked as failed.

Scenario: Dataset too large

  • WHEN the training dataset exceeds 500MB
  • THEN training fails immediately with { "detail": "Dataset too large. Maximum 500MB." }

Scenario: Training timeout

  • WHEN a training run exceeds the 30-minute timeout
  • THEN the training status is set to "failed" with reason "Training timed out"

Requirement: run_id validation on training endpoints

The FastAPI training endpoints (DELETE /training/runs/{run_id}, GET /training/runs/{run_id}) SHALL validate that run_id matches /^[a-zA-Z0-9_-]+$/ before any database or file operation.

Scenario: Valid run_id

  • WHEN DELETE /training/runs/run-2024-01-15_v3 is called
  • THEN the request proceeds normally

Scenario: Invalid run_id

  • WHEN DELETE /training/runs/../../admin is called
  • THEN the endpoint returns HTTP 400 with { "detail": "Invalid run_id format" }