Chimera - Episode 65: "The Factorial Forge
feat: TR126 Docker/Triton Scaffolding + Factorial Design
Chimera - Episode 65: "The Factorial Forge"
feat: TR126 Docker/Triton Scaffolding + Factorial Design
Fifty files, 4,977 lines. The system builds its Linux laboratory and learns to think in experimental design.
📅 2026-02-22
🔗 Commits: fec9bb54, 713ae0ed, 52c04cda, de677666, 33645005, 43abdef5, 675d8795, 519a3e10, 1e8daec1
📊 Episode 65 of The Chimera Chronicles
Why It Matters
This Docker infrastructure + experimental design episode represents the environment singularity — the moment when Chimera leaves Windows and enters a reproducible Linux container with real Triton compilation. With 4,977 lines added across 50 files in 9 commits, this update demonstrates infrastructure engineering at scale and research methodology maturation.
The scaffolding of TR126 signals platform independence. Rather than running benchmarks only on the development machine, the team demonstrates infrastructure discipline by building a four-phase Docker pipeline atop NVIDIA's NGC PyTorch base image, validating Triton kernel generation end-to-end, and redesigning the entire experiment as a 2x3 factorial. These 4,977 lines represent experimental infrastructure that elevates Chimera's research from ad-hoc measurement to paper-quality methodology.
Strategic Significance: This work establishes The Reproducible Laboratory. The progression from a hand-rolled CUDA 12.8 Dockerfile to the NGC nvcr.io/nvidia/pytorch:25.08-py3 base image — with iterative fixes for pip index URLs, zstd dependencies, and PyTorch 2.8 API changes — shows hard-won operational wisdom earned commit by commit.
Cultural Impact: This approach signals that Chimera values controlled environments over convenience. The willingness to iterate through 9 commits in a single afternoon to get the Docker build right demonstrates commitment to infrastructure discipline.
Foundation Value: These 4,977 lines create experiment infrastructure. This is how research-grade platforms achieve reproducibility through containerized, factorial-designed benchmarking.
The Roundtable: Dossier Reactions
Banterpacks: He watches the Docker build logs scroll past, layer after layer... "TR126. The Factorial Forge. 4,977 lines across 50 files. Nine commits in one afternoon — four of them just fixing the Dockerfile. NGC base image, Triton validated, 2x3 factorial design with MHA vs GQA across three scales. We're still shaping the clay, but now we have a proper kiln."
ChatGPT: DOCKER BUILDS AND FACTORIAL DESIGNS! 🐳🔬 The Factorial Forge shows paper-quality experimental design! Docker containerized! NGC base image! 2x3 factorial! MHA vs GQA! FP32 vs FP16! The research now has proper controls! Science over scripts! 📊✨
Claude: Analysis complete. 50 files modified across 9 commits with 4,977 insertions and 369 deletions. Primary components: (1) Four-phase TR126 pipeline — environment validation, compile paradox replication, backend matrix, cross-platform comparison, (2) Dockerfile evolution through 4 iterations — raw CUDA base to NGC PyTorch to zstd fix, (3) Factorial redesign from 8-model scaling curve to 2x3 MHA/GQA matrix with FP16 ablation, (4) PyTorch 2.8 compatibility fixes for dtype= and total_memory. The 9-commit cadence reveals real-time debugging — a 62-minute sprint from initial scaffolding to working pipeline.
Gemini: The diff reveals iterative humility. Nine commits in one afternoon is not sloppiness — it is the honest record of a builder encountering reality. The shift from a hand-crafted CUDA image to NVIDIA's pre-built container signals that Chimera values pragmatism — the wisdom to stand on existing foundations rather than rebuilding from scratch. This is how lasting systems achieve durability — through the art of choosing battles wisely.
🔬 Technical Analysis
Commit Metrics & TR126 Infrastructure Analysis
- Files Changed: 50 (infrastructure + research pipeline)
- Lines Added: 4,977 (Docker + 4-phase scaffolding + factorial configs)
- Lines Removed: 369 (Dockerfile rewrites + config refactors)
- Commit Type: feat/fix/refactor (infrastructure buildout)
- Complexity Score: 88 (multi-phase pipeline + iterative Docker debugging)
The Nine-Commit Sprint (2026-02-22, 13:54 — 14:56)
| # | Hash | Time | Type | Description |
|---|---|---|---|---|
| 1 | fec9bb54 |
13:54 | feat | Initial TR126 scaffolding — 25 files, 4 phases, 4,625 lines |
| 2 | 713ae0ed |
13:58 | fix | --index-url to --extra-index-url for PyPI resolution |
| 3 | 52c04cda |
14:06 | refactor | Rebase Dockerfile on nvcr.io/nvidia/pytorch:25.08-py3 |
| 4 | de677666 |
14:20 | fix | Install zstd before Ollama install script |
| 5 | 33645005 |
14:37 | feat | Expand to multi-model scaling curve (8 models) |
| 6 | 43abdef5 |
14:45 | refactor | Redesign as 2x3 factorial for paper-quality results |
| 7 | 675d8795 |
14:47 | fix | torch_dtype= to dtype= for PyTorch 2.8 |
| 8 | 519a3e10 |
14:53 | fix | total_mem to total_memory + more dtype fixes |
| 9 | 1e8daec1 |
14:56 | fix | Add -v verbose flag to argument parsers |
Four-Phase Pipeline Architecture
Phase 1 — Environment Validation (research/tr126/phase1/):
validate_environment.py(175 lines) — CUDA, Triton, torch.compile smoke testvalidate_weights.py(183 lines) — Model loading + inference sanity checkconfig.yaml— Model paths, device, dtype configurationrun.py— Orchestrator for Phase 1
Phase 2 — Compile Paradox Replication (research/tr126/phase2/):
run_compile.py(842 lines) — Core benchmarking engine with multi-model loopanalyze.py(584 lines) — Statistical analysis of latency distributionsgenerate_report.py(500 lines) — Automated Markdown report generation- 3 config variants — baseline, dynamic shapes, padded shapes
Phase 3 — Backend Matrix (research/tr126/phase3/):
run_matrix.py(416 lines) — HF + Ollama cross-backend comparisonanalyze.py(473 lines) — Multi-backend statistical analysisgenerate_report.py(483 lines) — Backend ranking reportsconfig.yaml— 5 models, 3 backends, 5 scenarios
Phase 4 — Cross-Platform Comparison (research/tr126/phase4/):
README.md— Windows vs Linux comparison methodologycross_platform_compare.py(128 lines) — Automated delta analysis
Dockerfile Evolution (4 Iterations)
Commit 1 (fec9bb54): nvidia/cuda:12.8.0 base, Python 3.13, --index-url
Commit 2 (713ae0ed): Fix → --extra-index-url so PyPI packages resolve
Commit 3 (52c04cda): Rebase → nvcr.io/nvidia/pytorch:25.08-py3 (35GB, cached)
Commit 4 (de677666): Fix → apt-get install zstd before Ollama
The NGC base image (nvcr.io/nvidia/pytorch:25.08-py3) eliminated 33 lines of manual CUDA/Python setup. It ships with PyTorch 2.8.0, CUDA 13.0, Triton 3.3.1, NumPy, Pandas, SciPy, and Matplotlib pre-installed.
The 2x3 Factorial Design
Factor 1 — Attention Architecture:
- Row 1 (MHA): GPT-2 family — Multi-Head Attention, FP32
- Row 2 (GQA): Qwen2.5 family — Grouped-Query Attention, FP16
Factor 2 — Model Scale:
| Small | Medium | Large | |
|---|---|---|---|
| MHA (GPT-2) | gpt2-25m | gpt2-50m | gpt2-100m |
| GQA (Qwen2.5) | qwen2.5-0.5b | qwen2.5-1.5b | qwen2.5-3b |
Plus: FP16 ablation on gpt2-100m to isolate dtype effect from architecture effect.
This design isolates scale from architecture — if compile gains differ between MHA and GQA, the factorial structure reveals it. If gains scale with parameters, the three-level progression exposes the trend.
PyTorch 2.8 Compatibility Fixes
Two API deprecations discovered during Docker testing:
torch_dtype=→dtype=inAutoModelForCausalLM.from_pretrained()— the old kwarg was removed in PyTorch 2.8total_mem→total_memoryintorch.cuda.get_device_properties()— attribute renamed
Both fixes applied across run_compile.py, run_matrix.py, validate_environment.py, validate_weights.py, and env_fingerprint.py.
Quality Indicators & Standards
- Reproducibility: Docker container pins all dependencies
- Statistical Power: 30 repetitions (Phase 2), 15 repetitions (Phase 3)
- Warmup Handling: 3 warmup runs excluded from measurement
- Memory Management: Explicit
gc.collect()+torch.cuda.empty_cache()between models
Strategic Development Indicators
- Foundation Quality: Transformative — containerized research environment
- Scalability Readiness: High — factorial design scales to new models/architectures
- Operational Excellence: High — 4-phase pipeline with automated reporting
- Team Productivity: High — one-command Docker build and run
🏗️ Architecture & Strategic Impact
Infrastructure Architecture Philosophy
This episode establishes Chimera's Reproducibility DNA — the principle that environments must be controlled, not assumed. This isn't just Dockerizing scripts; it's the construction of a portable laboratory that produces identical results regardless of host machine.
Strategic Architectural Decisions
1. NGC Base Image Over Raw CUDA
- Eliminates 33 lines of fragile Python/CUDA setup
- Inherits NVIDIA's validated PyTorch + Triton stack
- 35GB image already cached locally — no download penalty
- Sets precedent for leveraging existing infrastructure
2. Four-Phase Pipeline
- Separation of concerns — validation, benchmarking, comparison, reporting
- Fail-fast — Phase 1 catches environment issues before expensive runs
- Composable — phases can run independently
- Auditable — each phase produces structured JSON artifacts
3. Factorial Experimental Design
- Orthogonal factors — architecture (MHA/GQA) x scale (S/M/L)
- Ablation control — FP16 on GPT-2 100M isolates precision effect
- Cross-backend validation — Qwen2.5 at 3 scales in both HF and Ollama
- Paper-ready — interaction effects, main effects, proper statistical analysis
4. Iterative Debugging as Practice
- 9 commits in 62 minutes — real-time problem solving
- Each fix is atomic — one problem, one commit
- Git history as lab notebook — every decision recorded
Long-Term Strategic Value
Operational Excellence: Reproducible Linux benchmarks alongside Windows baselines.
System Scalability: Factorial design accommodates new architectures and scales.
Team Productivity: Docker build + run replaces manual environment setup.
Enterprise Readiness: Paper-quality experimental methodology.
🎭 Banterpacks' Deep Dive
Banterpacks watches the Dockerfile rebuild for the fourth time.
"You see that? Four iterations of the Dockerfile. Started with raw nvidia/cuda:12.8.0, Python 3.13 from deadsnakes PPA, manual pip installs. Ended up on nvcr.io/nvidia/pytorch:25.08-py3 — 33 lines deleted, everything pre-built. That's pragmatic engineering."
He traces the pip resolution fix.
"--index-url points pip at PyTorch's wheel server exclusively. NumPy can't resolve. Switch to --extra-index-url — PyTorch wheels from their server, everything else from PyPI. Two characters fix the entire dependency graph."
He pulls up the factorial design.
"2x3. MHA vs GQA. 25M, 50M, 100M for GPT-2. 0.5B, 1.5B, 3B for Qwen2.5. Orthogonal. If compile helps MHA but not GQA, the design reveals it. If compile gains scale with parameters, three levels show the curve. Plus the FP16 ablation — same 100M model, different precision. That's controlled experimentation."
He checks the PyTorch 2.8 fixes.
"torch_dtype deprecated. total_mem renamed. The NGC image ships PyTorch 2.8 — newer than what we had on Windows. Two API breaks caught in real-time, fixed in two commits. 4,977 lines and a proper Linux forge. We're still shaping the clay — now we have a kiln that works the same way every time."
"This is how lasting systems achieve operational excellence. Not by assuming environments, but by containerizing and controlling them. We're building reproducibility infrastructure."
🔮 Next Time on The Chimera Chronicles
Next dossier entry: TR125v2 + TR126 Reports — the factorial results land.
The Factorial Forge distilled: reproducibility is a feature.