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Sync: Why MI300X, Business Value, LICENSE, taxonomy fix, UI overhaul

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metadata

title: ReplayLab
emoji: 🔬
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: true
license: mit
short_description: GPU experiment flight recorder — failure to recovery agent
tags:
  - amd
  - gpu
  - agents
  - rocm
  - experiment-tracking
  - debugging

ReplayLab

GPU experiment flight recorder that autonomously detects failures, diagnoses root causes, and replays corrected experiments — in a single closed-loop cycle costing $0.14.

Built for the AMD Developer Hackathon 2026 · Track 1: AI Agents & Agentic Workflows

The Problem

Every ML engineer running GPU workloads hits the same wall: experiments fail silently (OOM, bad configs, timeouts), debugging takes hours of manual log-diving, and there's no reproducible trail from failure to recovery. Existing tools alert you that something broke — none of them fix it.

The Solution

ReplayLab is the black box flight recorder for GPU experiments. It:

Records the failed experiment (command, config, metrics, GPU telemetry, stderr)
Diagnoses the root cause using a 10-pattern vLLM/ROCm failure taxonomy + optional LLM reasoning via Qwen2.5-7B
Generates a minimum-parameter fix (e.g., reduce batch_size from 64→8)
Replays the corrected experiment automatically
Verifies recovery with before/after evidence

This is a closed-loop agentic system — not a dashboard, not a log viewer.

Architecture

User runs experiment
        ↓
┌──────────────────────────────────────────────────┐
│              REPLAYLAB AGENT LOOP                │
│                                                  │
│  [Runner]         Record command, stdout/stderr, │
│                   exit code, metrics, artifacts  │
│       ↓                                          │
│  [Taxonomy]       Match stderr against 10 known  │
│                   vLLM/ROCm failure patterns     │
│       ↓                                          │
│  [Diagnoser]      Rule-based comparison of       │
│                   failed vs baseline run          │
│       ↓                                          │
│  [LLM Diagnoser]  Qwen2.5-7B via vLLM provides  │
│                   natural-language explanation    │
│       ↓                                          │
│  [Planner]        Generate minimum fix           │
│       ↓                                          │
│  [Verifier]       Execute fix, confirm recovery  │
│       ↓                                          │
│  [Reporter]       HTML timeline + cost analysis  │
└──────────────────────────────────────────────────┘
        ↓
Recovered experiment with full evidence trail

Three Failure Scenarios

ReplayLab handles three distinct GPU failure patterns:

Scenario	Cause	Fix Applied	Recovery
GPU OOM	`max_model_len=65536` exceeds VRAM	Reduce to `max_model_len=32768`	✅ Real MI300X recovery
Batch Size Overflow	`batch_size=64` exceeds VRAM	Reduce to `batch_size=8`	✅ 648k items/sec
Processing Timeout	20 heavy prompts exceed 30s budget	Reduce concurrent load	✅ 13/20 → all complete

vLLM Failure Taxonomy (Domain Knowledge)

10 expert-level patterns modeled from real MI300X failure modes:

Pattern ID	Severity	Cause
`kv_cache_oom`	critical	KV cache memory exhaustion
`model_too_large`	critical	Model weights exceed GPU VRAM
`context_length_exceeded`	high	Context exceeds max_model_len
`rocm_version_mismatch`	critical	ROCm version incompatible
`triton_attention_fallback`	warning	FlashAttention unavailable
`tokenizer_timeout`	medium	HF tokenizer download failed
`port_in_use`	medium	Server port already occupied
`engine_dead_after_warmup`	critical	vLLM engine init failed
`batch_too_large_runtime`	high	Runtime batch exceeds capacity
`gpu_not_detected`	critical	No AMD GPU found by ROCm

Why AMD MI300X

ReplayLab is built for the one GPU that can run diagnostics and the experiment simultaneously:

Constraint	MI300X (192 GB HBM3)	H100 (80 GB HBM3)
Diagnostic sidecar (Qwen2.5-7B)	14.35 GiB — fits trivially	14.35 GiB — fits, but...
Remaining for experiment model	155+ GiB free (70B+ models)	~55 GiB free (≤34B models)
Simultaneous debug + workload	✅ Single card, no sharding	❌ Requires multi-GPU or model swap
Native GPU telemetry	`rocm-smi` / `amd-smi` — first-class	N/A (different toolchain)
ROCm-specific failure patterns	3 of 10 taxonomy patterns are ROCm-native (`rocm_version_mismatch`, `triton_attention_fallback`, `gpu_not_detected`)	N/A

The core design insight: a diagnostic agent must never compete for VRAM with the experiment it's debugging. MI300X's 192 GB means the 7B sidecar is invisible to the main workload — no model swapping, no sharding, no second GPU. On H100, you'd need to unload the experiment model to load the debugger, defeating the purpose of live diagnosis.

Why Qwen2.5-7B (Not 70B+)

ReplayLab's LLM agent is a diagnostic sidecar, not the main workload. The experiment being debugged is the main GPU consumer. A 7B model:

Loads in 8.42s cold / 2.58s warm (14.35 GiB)
Leaves 155+ GiB free for the experiment's model + KV cache
Provides sufficient reasoning for structured diagnosis prompts
Costs $0.14 per full recovery cycle vs $150+ manual debugging

Performance (Verified on AMD MI300X)

Metric	Value
GPU	AMD Instinct MI300X (192 GB HBM3)
ROCm	7.2.0
vLLM	0.17.1
Model load (cold)	14.35 GiB in 8.42s
Model load (warm)	14.35 GiB in 2.58s
torch.compile (cold)	16.28s
torch.compile (warm)	5.95s
KV cache allocation	155.31 GiB / 2,908,128 tokens
Max concurrency (32K ctx)	88 sequences
Inference throughput	227 tok/sec (sustained)
TTFT (short prompt)	283 ms
TTFT (long prompt)	1,131 ms
LLM diagnosis latency	604 ms
Full recovery cycle	~4 min
Cost per recovery	$0.14
Manual debug cost (est.)	$150.00
Speedup	1,071× cost reduction

Throughput Sweep (Qwen2.5-7B-Instruct, max_model_len=32768)

Prompt Length	Batch Size	Tokens/sec	TTFT (s)	Avg Latency (s)
Short (~10 tok)	1	226.1	0.283	0.283
Short (~10 tok)	4	225.9	0.283	0.283
Short (~10 tok)	8	226.0	0.283	0.283
Medium (~50 tok)	1	227.8	0.562	0.562
Medium (~50 tok)	4	228.6	0.560	0.560
Medium (~50 tok)	8	228.0	0.561	0.562
Long (~200 tok)	1	226.4	1.131	1.131
Long (~200 tok)	4	227.2	1.126	1.127
Long (~200 tok)	8	226.9	1.126	1.128

Concurrency Stress Test

Concurrent Requests	Aggregate tok/s	p50 Latency (s)	p95 Latency (s)	Scaling
1	222.9	0.287	0.287	1.0×
2	414.8	0.308	0.308	1.9×
4	758.6	0.337	0.337	3.4×
8	1,514.2	0.336	0.337	6.8×
16	2,930.9	0.345	0.348	13.1×

Near-linear scaling to 16 concurrent requests — p50 latency increases only 20% (287ms → 345ms) while aggregate throughput grows 13.1×. Zero failures across all tests.

rocm-smi During Benchmark

GPU[0] : GPU use (%): 25
GPU[0] : VRAM Total Memory (B): 205,822,885,888 (192 GB)
GPU[0] : VRAM Total Used Memory (B): 187,005,718,528 (174 GB, 91%)

LLM Diagnosis (Real MI300X, 604ms)

When fed an OOM crash log, Qwen2.5-7B running on MI300X diagnosed the failure in 604ms:

{
  "root_cause": "max_model_len (65536) exceeds derived max (32768)",
  "failure_category": "context_length_exceeded",
  "recommended_fix": "Reduce max_model_len or set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1",
  "confidence": 1.0
}

Agent Reasoning Trace

Each recovery produces a full reasoning chain:

[detect_failure]    Check exit code and run status → confirmed failure
[taxonomy_match]    Matched stderr against 10 known vLLM/ROCm patterns
[diagnose]          Compared failed vs baseline run metrics
[llm_diagnosis]     Qwen model provides NL explanation (if available)
[plan_fix]          Generated minimum parameter change
[verify_success]    Fix succeeded — recovery confirmed
[cost_estimate]     $0.14 GPU vs $150 manual (1,071× faster)

Evidence (Real AMD MI300X)

All data below was captured on AMD Developer Cloud — MI300X x1, $1.99/GPU/hour.

vLLM OOM Crash → Recovery

vLLM crashes when max_model_len=65536 exceeds the model's 32K context limit. ReplayLab catches this, diagnoses it, and recovers automatically.

rocm-smi During Benchmark

91% VRAM utilization (174 GB / 192 GB) during sustained inference benchmark — diagnostic sidecar coexists with the main workload.

Full Recovery Pipeline with LLM Diagnosis

End-to-end pipeline: failure detection → taxonomy match → LLM diagnosis (604ms) → fix generation → verified recovery.

AMD Developer Cloud VM

MI300X instance on AMD Developer Cloud used for all benchmarks and evidence collection.

Business Value

Target Customers

Segment	Pain Point	ReplayLab Value
ML engineers at GPU startups	OOM/config failures cost 2+ hours of manual debugging per incident	Automated diagnosis + fix in 604ms, $0.14/cycle
MLOps / platform teams	No automated incident response for GPU workloads	Closed-loop recovery with full evidence trail
AMD Developer Cloud users	No debugging tooling purpose-built for ROCm/MI300X	10-pattern ROCm-native taxonomy, rocm-smi telemetry
Research labs	Failed experiments break reproducibility	Before/after evidence trail with config diffs

Differentiation

ReplayLab	Alternatives
Closed-loop: detect → diagnose → fix → verify	Open-loop: alert only, human fixes
GPU-native taxonomy (10 vLLM/ROCm patterns)	Generic log parsers
Sub-second diagnosis at $0.14/cycle	Manual debugging at $150/incident
Full evidence trail (before/after)	Dashboard metrics without context
Built for AMD ROCm stack	Mostly NVIDIA-focused tooling

Quickstart

# Clone and install
git clone https://github.com/Roopalgn/AMD-DeveloperHack
cd AMD-DeveloperHack
pip install -r requirements.txt

# Run full recovery demo (no GPU required)
python replaylab/backend/full_demo.py

# Run tests
pytest tests/ -v

# Launch Gradio interactive demo
python replaylab/frontend/gradio_app.py

Repo Layout

AMD-DeveloperHack/
├── README.md                          This file (HF Space + GitHub)
├── SUBMISSION.md                      Hackathon submission details
├── requirements.txt
├── Dockerfile                         HF Space deployment
├── tests/                             38 tests, all passing
│   ├── test_diagnoser.py              Rule-based diagnosis tests
│   ├── test_planner.py                Fix generation tests
│   ├── test_report.py                 HTML report generation tests
│   ├── test_agent_loop.py             Multi-step agent tests
│   ├── test_vllm_taxonomy.py          10-pattern taxonomy tests
│   ├── test_llm_diagnoser.py          LLM fallback tests
│   ├── test_gpu_telemetry.py          GPU metrics collection tests
│   ├── test_runner.py                 Experiment runner tests
│   └── test_verifier.py              Replay verifier tests
├── replaylab/
│   ├── backend/
│   │   ├── agent.py                   Multi-step reasoning loop
│   │   ├── diagnoser.py               Rule-based failure diagnosis
│   │   ├── planner.py                 Fix generation engine
│   │   ├── runner.py                  Experiment runner/recorder
│   │   ├── verifier.py                Replay verification
│   │   ├── report.py                  HTML timeline report generator
│   │   ├── vllm_taxonomy.py           10 vLLM/ROCm failure patterns
│   │   ├── llm_diagnoser.py           Qwen-powered diagnosis agent
│   │   ├── gpu_telemetry.py           AMD GPU metrics (rocm-smi/amd-smi)
│   │   ├── app.py                     FastAPI web application
│   │   └── full_demo.py               End-to-end demo script
│   ├── frontend/
│   │   ├── gradio_app.py              Interactive Gradio demo
│   │   └── index.html                 Timeline UI
│   ├── demo/                          3 failure scenario configs
│   │   ├── config_bad.json            OOM trigger (batch_size=64)
│   │   ├── config_good.json           Recovered (batch_size=8)
│   │   ├── config_bad_model_path.json Model path error
│   │   ├── config_good_model_path.json Fixed model path
│   │   ├── config_bad_timeout.json    Timeout trigger (100k items)
│   │   ├── config_good_timeout.json   Fixed timeout (512 items)
│   │   └── demo_experiment.py         Controlled experiment simulator
│   └── runs/                          Pre-recorded GPU evidence
│       ├── gpu_oom/                   Real MI300X OOM crash data
│       ├── gpu_recovered/             Real MI300X recovery data
│       └── gpu_evidence/              vLLM startup, model, throughput

Submission Checklist

Public GitHub repository
HF Space deployed (Gradio interactive demo)
MIT License
3 failure scenarios (GPU OOM, batch overflow, timeout)
10-pattern vLLM/ROCm failure taxonomy
LLM-powered diagnosis agent (Qwen2.5-7B)
Multi-step agent reasoning loop with traces
HTML timeline report with VRAM chart
Cost analysis ($0.14 vs $150 per incident)
GPU telemetry collection (rocm-smi/amd-smi)
Real AMD MI300X evidence files committed
38 tests passing
SUBMISSION.md with market sizing

Links & Team


GitHub	https://github.com/Roopalgn/AMD-DeveloperHack
HF Space	https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/ReplayLab
Hackathon	lablab.ai AMD Developer Hackathon 2026
Team	Latency Locksmith
Track	Track 1: AI Agents & Agentic Workflows

Built for the AMD Developer Hackathon 2026.