Computational Lab Notebook Record

Template Overview

Computational science and machine learning research frequently suffer from reproducibility crises caused by undocumented code tweaks, missing seeds, and opaque hardware environments. The Computational Lab Notebook Record provides a rigorous, immutable experiment ledger tracking code commits, environment snapshots, execution logs, and benchmark tables in plain Markdown.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Lab Notebook: [Experiment Name / ID]"
date: [YYYY-MM-DD]
experimenter: "[[Scientist / Engineer Name]]"
git_commit: "[commit-sha-7-chars]"
python_version: "3.12.6"
hardware_platform: "Apple M3 Max (16-core CPU, 40-core GPU, 64GB Unified RAM)"
dataset_hash: "sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
experiment_verdict: "Success / Inconclusive / Failed"
tags: [computational-science, lab-notebook, reproducibility, benchmarks, experiments]
---

# Computational Lab Notebook — [Experiment Name / ID]

## 1. Experiment Objective & Scientific Hypothesis
- **Hypothesis:** Quantizing the whisper acoustic encoder from FP16 to Q4_K will reduce inference latency by >= 40% on Apple Silicon with < 1.0% degradation in transcription accuracy.
- **Dependent Variables:** Real-time factor (RTF), memory consumption (RSS), word error rate (WER).
- **Independent Variables:** Quantization precision tier (FP16, Q8_0, Q5_K_M, Q4_K_S).

## 2. Experimental Environment & Setup
- **OS & Kernel:** macOS Sequoia 15.1 (Darwin Kernel 24.1.0).
- **Toolchain:** Clang 16.0, Accelerate Framework, Metal Performance Shaders (MPS).
- **Random Seed:** `42` (Fixed across all validation runs).
- **Execution Command:**
```bash
./bin/bench-whisper -m models/ggml-large-v3-q4_k.bin -f data/audio/librispeech_clean_100.wav --threads 8

3. Empirical Results & Benchmark Matrix

Run ID	Model Precision	Latency (ms/chunk)	Real-Time Factor (RTF)	Peak RAM (MB)	Measured WER (%)
RUN-01	FP16 (Baseline)	482 ms	0.032	3,120 MB	4.12%
RUN-02	Q8_0	314 ms	0.021	1,840 MB	4.18%
RUN-03	Q5_K_M	245 ms	0.016	1,220 MB	4.45%
RUN-04	Q4_K_S	198 ms	0.013	980 MB	4.82%

4. Anomalies & Observations

• Thermal Behavior: Peak die temperature stayed steady at 54°C throughout the 30-minute benchmark loop; zero thermal throttling observed.
• Outlier Audio Sample: Audio sample #48 experienced repetition hallucination on background keyboard typing noise; mitigated by raising entropy threshold parameter from 2.4 to 2.8.

5. Scientific Conclusion & Next Experiment

[!IMPORTANT] Finding Hypothesis confirmed. Q4_K quantization delivers a 58.9% reduction in latency and 68.5% reduction in RAM footprint, incurring only a 0.70% WER trade-off. Recommended for default production shipping.

6. Action Items

• Commit benchmark parsing script to repository (scripts/bench_parser.py).
• Tag Git release candidate with benchmark commit SHA.

## Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

```markdown
---
title: "Lab Notebook: EXP-882 Metal Kernel Tuning"
date: 2026-10-08
experimenter: "[[Devin Zhao]]"
git_commit: "9fa42c1"
python_version: "3.12.5"
hardware_platform: "Apple M2 Pro (32GB RAM)"
experiment_verdict: "Success"
tags: [benchmarks, metal, apple-silicon]
---

# Computational Lab Notebook — EXP-882 Metal Kernel Tuning

## 1. Objective
Devin Zhao benchmarked vectorized SIMD dot-product kernels against Apple Metal shaders for audio spectrogram processing.

## 2. Key Outcome
Metal compute shaders reduced spectrogram processing time from 14.2ms to 2.8ms per 30-second window, freeing CPU threads for real-time tokenizer parsing. Full reproducibility log archived in Git.

When to Use and When to Avoid

When to Use

• Use during computational experiments, algorithmic optimizations, and machine learning research.
• Use to ensure complete reproducibility of scientific claims and academic papers.
• Use when tracking hardware benchmarks across sequential software versions.

When to Avoid

• Avoid using for informal quick brainstorms or non-quantitative meeting agendas.
• Avoid using for pure front-end visual CSS styling revisions.

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the computational experiment log and terminal outputs:
1. Extract hardware environment details, software versions, and random seeds.
2. Compile a structured Markdown benchmark table comparing latency, memory, and accuracy metrics.
3. Formulate an evidence-based scientific conclusion and next experiment steps.

Your data remains securely on your Mac with zero cloud exposure.