Project 11: Multi-GPU Quantization Orchestrator

Build a tool that orchestrates quantization across multiple GPUs and aggregates results.

Quick Reference

Attribute Value
Difficulty Level 5: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites Quantization basics, multi-GPU systems
Key Topics distributed execution, orchestration, metrics

1. Learning Objectives

By completing this project, you will:

  1. Distribute quantization jobs across GPUs.
  2. Track per-GPU performance metrics.
  3. Aggregate results into a unified report.
  4. Handle failures and retries.
  5. Compare throughput scaling.

2. Theoretical Foundation

2.1 Multi-GPU Orchestration

Large-scale quantization workloads need coordinated execution and consolidated reporting.

3. Project Specification

3.1 What You Will Build

A coordinator service that schedules quantization tasks across multiple GPUs and collects metrics.

3.2 Functional Requirements

  1. Job scheduler for GPU tasks.
  2. Worker nodes that run quantization.
  3. Metrics collection for time/memory.
  4. Retry logic for failed jobs.
  5. Aggregated report of results.

3.3 Non-Functional Requirements

  • Deterministic task configs.
  • Clear logs for each worker.
  • Configurable concurrency.

4. Solution Architecture

4.1 Components

Component Responsibility
Scheduler Dispatch jobs
Worker Run quantization
Metrics Store Collect results
Reporter Aggregate outputs

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P11-multi-gpu/
├── src/
│   ├── scheduler.py
│   ├── worker.py
│   ├── metrics.py
│   └── report.py

5.2 Implementation Phases

Phase 1: Scheduler (6-10h)

  • Dispatch jobs to workers.
  • Checkpoint: tasks assigned to GPUs.

Phase 2: Worker + metrics (6-10h)

  • Run quantization and collect metrics.
  • Checkpoint: per-GPU stats recorded.

Phase 3: Aggregation (6-10h)

  • Combine results into reports.
  • Checkpoint: scaling report produced.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit scheduler job assignment
Integration worker quantization run
Regression report stable metrics aggregation

6.2 Critical Test Cases

  1. Jobs distribute evenly across GPUs.
  2. Failed worker retries and logs errors.
  3. Aggregated report includes all tasks.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
GPU imbalance idle devices add load balancing
Missing metrics incomplete reports enforce metric schema
Worker crashes job loss add retry logic

8. Extensions & Challenges

Beginner

  • Add a simple CLI dashboard.
  • Add job priority support.

Intermediate

  • Add distributed queue (Redis).
  • Add per-layer quantization stats.

Advanced

  • Add autoscaling workers.
  • Add multi-node orchestration.

9. Real-World Connections

  • Large model quantization uses multi-GPU orchestration.
  • MLOps pipelines require aggregated reporting.

10. Resources

  • Distributed job scheduling references
  • GPU profiling docs

11. Self-Assessment Checklist

  • I can orchestrate quantization jobs across GPUs.
  • I can aggregate results into reports.
  • I can handle worker failures.

12. Submission / Completion Criteria

Minimum Completion:

  • Multi-GPU job scheduling
  • Metrics collection

Full Completion:

  • Retry logic + aggregated report

Excellence:

  • Autoscaling or multi-node orchestration

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.