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test-pr #106
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update pom.xml with yaml parsing dependency and Dockerfile to have DVC cli installation.
-Add 60-second timeout to DVC process execution
validate each of nvr type to make sure all items are added as string
Signed-off-by: Jude Niroshan <[email protected]>
Implement new /api/v1/mlops-batch endpoint for automated testing of NVRs
fetched from DVC (Data Version Control). This feature is completely
independent from regular job batches and designed for ML operations workflows.
Database Changes:
- Add mlops_batch table for batch tracking with DVC version metadata
- Add mlops_job table for individual NVR-based jobs
- Add mlops_job_metrics table for storing aggregated metrics and confusion matrix
- Create indexes for optimal query performance
Core Features:
- Fetch NVR lists from DVC using version tags
- Automatic NVR resolution (package name, version, source URL, false positives URL)
- Parallel job execution with configurable rolling window (default: 3 concurrent jobs)
- Ground truth sheet URL generation per NVR (MinIO integration)
- Pipeline metrics extraction (accuracy, precision, recall, F1, confusion matrix)
- Comprehensive error handling and graceful degradation
New Entities & Models:
- MlOpsBatch, MlOpsJob, MlOpsJobMetrics entities
- Repositories for data access layer
- Request/Response DTOs with validation
Services:
- MlOpsBatchService: Core orchestration with DVC integration
- MlOpsJobService: Job lifecycle and status management
- MlOpsMetricsService: Extract and store pipeline metrics from workflow results
- PipelineParameterMapper: Enhanced with MLOps-specific parameters
Pipeline Parameters (MLOps-specific):
- CONTAINER_IMAGE: Custom SAST AI workflow image
- PROMPTS_VERSION: DVC version for prompts configuration
- KNOWN_NON_ISSUES_VERSION: DVC version for known non-issues
- INPUT_REPORT_FILE_PATH: NVR-specific ground truth sheet URL
- Standard parameters: source URL, false positives, LLM configuration
REST Endpoints:
- POST /api/v1/mlops-batch - Submit new MLOps batch
- GET /api/v1/mlops-batch - List all batches (paginated)
- GET /api/v1/mlops-batch/{id} - Get batch summary
- GET /api/v1/mlops-batch/{id}/detailed - Get batch with all jobs and metrics
Platform Integration:
- MlOpsPipelineRunWatcher: Monitor Tekton pipelines for MLOps jobs
- Automatic status updates and batch progress tracking
- Metrics extraction from workflow-metrics result
- Rolling window semaphore for controlled parallelism
Configuration:
- mlops.batch.max.parallel.jobs: Control concurrent job execution (default: 3)
Key Design Decisions:
- Complete database isolation from regular batches per requirements
- No INPUT_SOURCE_TYPE needed - MLOps jobs work directly with NVRs
- Graceful handling of missing metrics when pipelines fail
- Transaction-safe status polling to avoid context exceptions
- Semaphore-based rolling window for optimal resource utilization
update pom.xml with yaml parsing dependency and Dockerfile to have DVC cli installation.
Co-authored-by: ikrispin <[email protected]>
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AI Code Review Skipped: This PR is too large for automated review (diff size exceeds 100KB). |
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