TrackIQ
Performance validation platform for edge and distributed AI
Why I Built This
The real problem behind performance validation for edge and distributed AI
Everyone Measures Performance Differently
We kept running into the same frustrating loop: one team benchmarks inference, another validates training, someone else compares baselines, and none of the outputs line up. People spend hours translating results instead of fixing real issues. When the numbers don't speak the same language, regressions turn into debates and slip past CI/CD.
We Keep Rebuilding the Same Scripts
Every new project starts with good intentions, then someone ends up writing one-off scripts to parse logs, compute percentiles, and compare runs. A few weeks later, a different team does it all again. That repetition quietly burns 5-10 hours a week that should go into improving models and systems.
Most Tools Are Either Too Heavy or Too Manual
The options usually fall into two extremes: powerful frameworks that are hard to adopt, or low-level tools that need lots of glue code. Teams get stuck choosing between complexity and fragility. What’s missing is a practical middle ground that works in real CI/CD workflows.
The Vision: Beyond Just Automotive
TrackIQ started with automotive and edge inference because latency and reliability failures are visible immediately. The core problem is broader: teams need one consistent performance validation workflow across inference, distributed training, and cross-platform comparison.
LLM inference on edge devices: Healthcare AI chatbots running on hospital infrastructure need to be fast AND consistent. You can't have P99 latency spikes when a doctor is waiting for a diagnosis suggestion.
Medical imaging: Cancer detection models running on edge devices in clinics need rigorous performance validation. Lives depend on these systems being both accurate and fast.
Industrial IoT: Predictive maintenance models on factory floors, quality inspection systems, robotics-all need the same thing: automated performance validation with regression detection.
TrackIQ is the system I wanted as an engineer: a shared core contract, tool-specific runners, and reproducible reports that are comparable across hardware and workloads.
What I'm Actually Solving
The Core Problem: Performance validation on edge devices is manual, inconsistent, and lacks systematic rigor. Teams waste 5-10 hours per week running custom scripts. Regressions slip through to production. There's no "pytest for performance engineering."
My Solution: TrackIQ is a production-ready validation platform with a canonical result schema (`TrackiqResult`), shared comparison/reporting infrastructure (`trackiq_core`), and focused tools for inference (`autoperfpy`), distributed training (`minicluster`), and result comparison (`trackiq-compare`).
Long-term Vision: Make performance validation as routine as unit testing across platforms (NVIDIA, AMD, Intel, Apple Silicon, CPU) and workflows (inference, training, baseline comparison, and health monitoring).
The Problem
Performance validation is broken for edge AI systems
Wasted Per Week
Performance engineers manually run benchmarks, parse logs, calculate percentiles, and compare baselines. Custom scripts per project. No reusable toolkit. This is 20-25% of their time.
To Debug Regressions
Regressions caught in production take days to root-cause. Was it the model? The platform? The compiler? No baseline history. No automated detection. Just panic debugging.
Budget for Tools
Teams can't justify $50K for commercial tools. They need open-source, production-ready solutions. But nothing exists for automotive-grade edge AI performance.
User Pain Points by Segment
Performance Engineers
Pain: No unified tool for NVIDIA Jetson/DRIVE benchmarking across devices, precisions, and batch sizes.
Workaround: Custom scripts per project. 5-10 hours/week manual work.
Cost: 20-25% of engineering time wasted on tooling that should be commoditized.
MLOps Teams
Pain: No regression detection in CI/CD for edge models. Performance drops slip to production.
Workaround: Manual baseline comparisons in spreadsheets. Hope for the best.
Cost: Production incidents, customer escalations, emergency debugging sessions.
Automotive Engineers
Pain: Can't validate ADAS latency requirements (P99 <33.3ms) systematically.
Workaround: Manual profiling, ad-hoc testing, fingers crossed at certification.
Cost: Safety risks, late-cycle failures, re-certification costs.
Market Landscape
Why existing solutions don't solve this problem
"MLPerf is cloud-focused, Nsight is GUI-only, TensorRT is too low-level. There's a $0 -> $50K gap in edge AI performance tools."
The TrackIQ positioning
| Solution | Strengths | Weaknesses | TrackIQ Advantage |
|---|---|---|---|
| MLPerf Inference | Industry standard benchmarks | Cloud-focused, complex setup, no edge CI/CD | Lightweight, edge-first, CI-ready in 5 minutes |
| NVIDIA Nsight Systems | Deep GPU profiling | GUI-only, expert users, no automation | CLI automation, batch analysis, Python API |
| Custom Scripts | Flexible, project-specific | Fragmented, no regression tracking, reinvent wheel | Production-ready, tested, documented, reusable |
| TensorRT OSS | Official NVIDIA tools | Low-level APIs, lacks analytics layer | High-level abstractions, visualizations, reports |
Market Opportunity
NVIDIA edge AI market: $10B+ (automotive, robotics, retail, healthcare)
50,000+ Jetson/DRIVE developers (based on NVIDIA GTC attendance, forum activity)
10,000+ performance engineers and MLOps teams at automotive OEMs and tier-1 suppliers
Product Vision & Strategy
Shared validation layer for inference, training, and cross-platform comparison
North Star Metric
Target: 5 minutes
(vs. 2-3 days industry average)
If we can compress the performance validation cycle from days to minutes, we transform how teams develop edge AI systems. Fast feedback loops = faster iteration, fewer regressions, higher quality.
Core Value Propositions
Automated Benchmarking
One command runs benchmarks across all devices (GPU, CPU, Jetson), all precisions (FP32, FP16, INT8), all batch sizes. No manual scripting.
Regression Detection
Configurable thresholds per metric. Fails CI/CD if P99 latency >10% or throughput <5%. Catches regressions before production.
Automotive Profiles
Pre-built profiles enforce ADAS safety requirements: P99 <33.3ms, 50W power budget, 80°C thermal limit. Validates compliance automatically.
Interactive Reports
HTML/PDF reports with Plotly visualizations. Light/dark themes. Auto-runs benchmarks if no data provided. Share with stakeholders instantly.
Advanced Analytics
DNN pipeline layer-by-layer analysis. Tegrastats thermal monitoring. Efficiency metrics (perf/watt). Identify bottlenecks in minutes.
Extensible Architecture
Two-layer design: reusable core library (trackiq_core) + application layer (autoperfpy). Use as CLI or Python library.
Technical Architecture
Shared core + specialized tools enables flexibility and scale
Tool Apps: autoperfpy, minicluster, trackiq-compare
Purpose: Focused user workflows built on one shared core
- autoperfpy: edge inference benchmarking, analysis, and reporting
- minicluster: distributed training validation with live health monitoring
- trackiq-compare: cross-result and baseline comparison reports
- Tool-owned Streamlit apps + unified launcher
- Consistent `TrackiqResult` output across all tools
Core Library: trackiq_core
Purpose: Shared schema, platform integrations, and reusable UI components
- Canonical schema (`TrackiqResult`) + serializer/validator
- Power profiler (ROCm SMI, tegrastats, simulation fallback)
- Hardware detection (NVIDIA, AMD, Intel, Apple Silicon, CPU)
- Baseline management and report generation
- Library-first UI layer (`TrackiqDashboard` + components)
Why This Architecture Matters
This architecture is now a true platform pattern: one stable core contract, multiple specialized tools, and shared UI/reporting primitives.
Flexibility
Library users (advanced): Import trackiq_core for programmatic control. Build custom tools on top.
CLI users (everyday): Run autoperfpy run --auto for instant results. No code required.
Adoption Strategy
Start with CLI for quick wins (80% of users). Graduate power users to library for custom workflows. Mirrors kubectl + Kubernetes client libs.
Platform Play
trackiq_core becomes the de facto performance library for edge AI. Platform teams build custom dashboards, MLOps tools, CI/CD integrations on top.
User Stories & Prioritization
Ruthlessly scoped to deliver value fast
P0 - Must Have (MVP)
✅ Auto-Benchmarking
User Story: "As a performance engineer, I need to run benchmarks across all available devices with one command, so I can quickly identify performance regressions."
Acceptance: autoperfpy run --auto detects GPUs/CPU, runs 5+ configs in <2 min
✅ Regression Detection
User Story: "As an MLOps engineer, I need automated regression detection in CI/CD, so I catch performance drops before production."
Acceptance: autoperfpy compare exits non-zero if P99 >10% regression
✅ Automotive Profiles
User Story: "As an automotive engineer, I need to validate ADAS latency requirements (P99 <33.3ms, 50W, 80°C), so I meet safety certifications."
Acceptance: --profile automotive_safety enforces constraints, fails if violated
P1 - Should Have (Differentiation)
✅ Interactive Reports
User Story: "As a technical lead, I need interactive HTML reports with Plotly charts, so I can share results with stakeholders."
Status: Implemented with dark/light themes
✅ DNN Pipeline Analysis
User Story: "As a platform engineer, I need DNN pipeline layer-by-layer analysis, so I can identify bottleneck layers."
Status: Implemented with top-N layer ranking
✅ Streamlit Dashboard
User Story: "As a data scientist, I need an interactive UI to explore results without CLI, so I can iterate faster."
Status: Implemented with benchmark execution from browser
P2 - Nice to Have (Future)
🔲 LLM Metrics
User Story: "As a data scientist, I need LLM-specific metrics (TTFT, tokens/sec), so I optimize transformer models."
Status: Partially implemented, needs KV cache monitoring (v0.3.0)
🔲 Multi-Run Trends
User Story: "As a manager, I need multi-run trend analysis, so I track performance over time."
Status: Planned for v0.3.0 with time-series charts
🔲 Real-Time API
User Story: "As a platform team, I need REST API for real-time monitoring, so I integrate with dashboards."
Status: v1.0.0 feature, requires significant backend work
Feature Prioritization Framework
| Feature | User Impact | Dev Effort | Strategic Value | Priority |
|---|---|---|---|---|
| Auto-benchmarking | ⭐⭐⭐⭐⭐ | 3 weeks | Critical path blocker removal | P0 |
| Regression detection | ⭐⭐⭐⭐⭐ | 2 weeks | CI/CD integration = adoption | P0 |
| Automotive profiles | ⭐⭐⭐⭐ | 1 week | Unique differentiator | P0 |
| HTML reports | ⭐⭐⭐⭐ | 2 weeks | Shareability -> virality | P1 |
| Streamlit UI | ⭐⭐⭐ | 1 week | Lower barrier to entry | P1 |
| Multi-run trends | ⭐⭐⭐ | 3 weeks | Nice-to-have, not urgent | P2 |
Product Trade-offs
Key decisions that shaped the product
| Decision | What I Chose | What I Rejected | Rationale |
|---|---|---|---|
| Architecture | Two-layer (core + app) | Monolithic CLI tool | Balances ease-of-use (CLI) with flexibility (library). Mirrors Docker CLI + containerd. Broader use cases. |
| Demo Data | Synthetic data | Proprietary automotive data | GitHub repo can't include proprietary data. Synthetic enables reproducible examples without legal issues. |
| User Interface | CLI-first, UI second | Web dashboard first | CLI = CI/CD integration (critical for adoption). Streamlit UI = discoverability. 80/20 split is right for DevTools. |
| Regression Metrics | P99 latency + throughput | Average latency only | Averages hide tail latency. P99 = SLO standard (Google SRE, AWS). 1/100 failures unacceptable in automotive. |
| Device Support | NVIDIA platforms only | All accelerators (AMD, Intel) | 80% of edge AI is NVIDIA. Focus on depth > breadth. Multi-vendor = 3x complexity for 20% market. |
| Testing | 42+ tests, pytest | Manual testing only | Production-ready = comprehensive tests. CI/CD requires test automation. No compromise on quality. |
| Precision Support | FP32, FP16, INT8 | All precisions (FP64, BF16, INT4) | These 3 cover 95% of edge AI use cases. BF16 = NVIDIA Ampere+ only. INT4 = niche. Ship fast, expand later. |
Biggest Trade-off: Depth vs Breadth
I chose to go deep on NVIDIA platforms (Jetson, DRIVE, GPUs) instead of supporting all accelerators (AMD, Intel, Qualcomm). Why? Because 80% of edge AI is NVIDIA, and multi-vendor support would have delayed launch by 6+ months.
This is classic product strategy: own a niche completely before expanding. Better to be the #1 tool for NVIDIA edge AI than a mediocre tool for everything.
The architecture supports extension (collectors for other platforms can be added), but I'm not building them until NVIDIA adoption proves the model works.
Success Metrics
How I measure product success
The Metrics That Actually Matter
📊 Adoption Metrics
- ▸ GitHub stars/forks (virality signal)
- ▸ PyPI downloads (actual usage)
- ▸ Issues/PRs (community engagement)
- ▸ CLI vs Library usage split
⚡ Performance Metrics
- ▸ Time to run full benchmark suite
- ▸ Regression detection accuracy
- ▸ Model R² scores (>0.85 target)
- ▸ CI/CD integration success rate
📈 Product-Market Fit
- ▸ User testimonials/case studies
- ▸ Enterprise adoption (OEMs, tier-1s)
- ▸ Feature requests (unmet needs)
- ▸ Retention: weekly active users
North Star Metric (Reminder)
Time from model change to validated performance results: 5 minutes (vs 2-3 days industry average)
Product Development Journey
From problem to production in 4 months
Month 1: Problem Validation
Identified pain points from observing edge AI system development across autonomous driving, 5G infrastructure, and industrial applications. Researched existing tools (MLPerf, Nsight, profiling frameworks). Validated gap through developer forums, Reddit (r/MachineLearning), and conversations with performance engineers. Confirmed: no unified, production-ready tool for edge AI performance validation across domains.
Month 2: Core Library Build
Built trackiq_core: collectors (NVML, psutil, synthetic), benchmark runners, config management, regression detection. Chose interpretable models over complexity for trust and explainability. Wrote comprehensive unit tests (pytest). Documented assumptions and design decisions. Focused on automotive use case first (strictest requirements = good baseline).
Month 3: Tool Expansion
Expanded from a single app into a multi-tool system: `autoperfpy` for inference benchmarking, `minicluster` for distributed training validation, and `trackiq-compare` for canonical result comparisons. Standardized outputs via `TrackiqResult` and added HTML/terminal reporting paths.
Month 4: Platformization
Built shared UI components in `trackiq_core.ui`, added tool-owned dashboards plus unified launcher, integrated power profiling and multi-platform hardware support, and introduced live MiniCluster health monitoring with checkpoint + anomaly reporting.
Key Learnings
What worked, what I'd change
✅ What Worked
- Two-layer architecture (enabled library + CLI use cases)
- Documentation-first approach (README, CONCEPTS, examples)
- Domain-specific profiles (automotive = strictest requirements, good starting point)
- Synthetic data (enables reproducible demos without legal/NDA issues)
- Comprehensive testing (42+ tests = production-ready signal)
- CLI-first strategy (DevTools need automation, not just GUIs)
- Regression detection (CI/CD integration is critical for adoption)
- Starting narrow (NVIDIA platforms) before expanding to all accelerators
-> What I'd Change
- Talk to domain experts earlier (validate automotive/healthcare/LLM profile assumptions)
- Build Streamlit UI sooner (visual demos help explain value faster)
- Document trade-offs in real-time (easy to forget reasoning months later)
- Set up user feedback loop earlier (metrics aren't everything, need qualitative)
- Create video demos (code alone doesn't sell to non-technical stakeholders)
- Build multi-run trends sooner (users asked for this immediately)
- Focus more on go-to-market (great product != automatic adoption)
- Start LLM-specific features earlier (TTFT, KV cache) given growing market
Biggest Lesson
PM work is 80% communication, 20% building. Too much time optimizing technical implementation and not enough explaining why TrackIQ matters across different domains. The most technically impressive feature is worthless if engineers cannot map it to their actual validation pain points.
Next time: Build domain-specific pitch decks alongside the code (automotive version, LLM version, healthcare version). Validate messaging with target users in each vertical. Document trade-offs as decisions are made, not after the fact. Test cross-domain applicability earlier.
Go-to-Market Strategy
How I plan to get this into users' hands
🎯 Phase 1: Developer Community
Target: Developer forums (edge AI, MLOps), Reddit (r/MachineLearning, r/embedded, r/LocalLLaMA), Hacker News, Twitter (#EdgeAI, #LLMs)
Tactics: Technical blog posts, "Show HN" launch, demo videos, GitHub README optimization for SEO, showcase automotive + LLM use cases
Goal: 100 GitHub stars, 500 PyPI downloads, 10 issues/PRs in first month, feedback from multiple domains
📣 Phase 2: Platform Ecosystem
Target: NVIDIA Developer Program, edge AI conferences (Embedded Vision Summit, tinyML), LLM inference communities, medical AI forums
Tactics: Conference demos, domain-specific tutorials (automotive benchmarking, LLM TTFT optimization, medical imaging latency), integration guides
Goal: Featured in platform newsletters, 3+ case studies across domains, community contributions
🚗 Phase 3: Enterprise Domains
Target: Automotive OEMs, healthcare AI companies, LLM inference providers, industrial IoT teams, robotics companies
Tactics: Direct outreach to performance engineering teams, domain-specific case studies, pilot programs, consulting partnerships
Goal: 5+ enterprise users across verticals, testimonials, potential support/consulting revenue, domain-specific feature requests
Distribution Strategy
Open Source
MIT license = maximum adoption. No barriers to enterprise use. Build trust through transparency. Community contributions = free R&D + domain-specific features.
Documentation
7,000+ word README, CONCEPTS guide, 7 working examples across domains. Make onboarding effortless. Searchable, SEO-optimized. Documentation IS marketing for DevTools.
Viral Loops
CI/CD integration = team adoption (1 user -> entire team). HTML reports = shareable artifacts. Cross-domain success stories = market expansion. GitHub exposure = organic discovery.
Monetization Path (Long-Term)
While TrackIQ is open-source, there are several monetization paths once adoption is proven across domains:
- 💼 Domain-Specific Consulting: Custom profiles for automotive safety, healthcare compliance, LLM optimization. On-site training, priority support SLAs.
- ☁️ SaaS Dashboard: Hosted version with team collaboration, multi-domain benchmarking, historical trending, compliance reporting (FDA, ISO 26262, etc.)
- 🔌 Platform Partnerships: Integration with CI/CD platforms (Jenkins, GitLab, GitHub Actions), LLM serving frameworks (vLLM, TensorRT-LLM), medical AI platforms
- 📚 Training & Certification: "Performance Engineering for Edge AI" courses covering automotive, healthcare, LLM domains. Industry-specific certification programs.
Product Roadmap
From foundational tooling to a multi-platform validation platform
Core Features
- ✓ Canonical schema + shared `trackiq_core` foundation
- ✓ Three tool workflows: `autoperfpy`, `minicluster`, `trackiq-compare`
- ✓ Regression detection with configurable thresholds
- ✓ Automotive profiles (ADAS requirements)
- ✓ Interactive HTML/PDF reports
- ✓ Comprehensive tests and documentation across tools
UX & Analytics
- -> MiniCluster live health monitor + anomaly detector/reporter
- -> Power profiling and efficiency metrics fully standardized
- -> Shared UI layer with tool-owned dashboards and unified launcher
- -> Additional precisions (BF16, INT4)
- -> User feedback collection system
Production Scale
- -> Deeper hardware integrations and production telemetry hooks
- -> CI/CD platform integrations (Jenkins, GitLab, GitHub Actions)
- -> Qualcomm integration path and broader device telemetry support
- -> SaaS dashboard (hosted version with team collab)
- -> Enterprise support & consulting offerings