3.4. Experiment Tracking & Reproducibility
🪄 Step 1: Intuition & Motivation
- Core Idea: Modern machine learning experiments involve dozens of hyperparameters, gigabytes of data, and countless random factors. If you can’t reproduce your results, your research or product can’t be trusted — period.
Experiment tracking ensures every training run can be: ✅ Reproduced exactly, ✅ Compared fairly, and ✅ Debugged quickly.
- Simple Analogy: Imagine you’re baking a perfect loaf of bread — but forget what flour, temperature, or yeast you used. Next time, you can’t replicate it! Tracking experiments is your recipe book for machine learning — logging every detail so great results are never accidental.
🌱 Step 2: Core Concept
Let’s break this into three essential parts:
1️⃣ Seed Control — Ensuring Deterministic Behavior
Machine learning models rely on random initialization — from weight generation to data shuffling. But randomness can cause non-deterministic results, meaning rerunning the same code can yield different models.
To ensure reproducibility, we fix “random seeds” across all libraries:
import random, numpy as np, torch
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = FalseWhy It Matters:
- Prevents stochastic variations.
- Enables fair hyperparameter comparison.
- Helps reproduce published results or production models.
2️⃣ Experiment Logging — The Black Box Recorder
When you train models, you tweak parameters, data, and architectures — often hundreds of times. Without structured logging, you’ll forget which combination worked best.
Key Items to Log:
- Hyperparameters: learning rate, optimizer, batch size, epochs.
- Model Configs: number of layers, hidden units, dropout rates.
- Data Info: dataset version, split method, preprocessing steps.
- Checkpoints: weight files, validation metrics at each epoch.
- Environment: OS, GPU type, library versions, seed.
Tools:
- 🧭 Weights & Biases (W&B): Rich dashboards, comparisons, collaborative logging.
- 🧪 MLflow: Industry-standard for lifecycle management and model versioning.
- 📊 TensorBoard: Visual logs for metrics, losses, and computational graphs.
3️⃣ Dataset & Model Versioning — The Forgotten Hero
Reproducibility doesn’t just mean fixing seeds — it also means locking down the exact data and code used.
Dataset Versioning Tools:
- 🧱 DVC (Data Version Control): Tracks datasets and models like Git tracks code.
- 🤗 Hugging Face Datasets: Use fixed splits (
train,validation,test) for consistency.
Why It’s Critical:
- Datasets evolve — examples get added, removed, or re-labeled.
- Using the wrong version means your results may no longer match published metrics.
📐 Step 3: Mathematical & Conceptual Foundations
Determinism in Computation
A computation is deterministic if:
Given the same input, code, and hardware, it always produces the same output.
However, GPU kernels (especially in CUDA, cuDNN) often include non-deterministic optimizations for speed. Even floating-point addition can differ slightly between runs due to parallel order of operations.
To achieve deterministic behavior:
- Use fixed seeds.
- Disable nondeterministic GPU algorithms.
- Fix library versions (PyTorch, NumPy, CUDA).
- Avoid mixed-precision randomness when exact reproducibility is critical.
Key Command:
torch.use_deterministic_algorithms(True)🧠 Step 4: Practical Debugging for Reproducibility
When You Rerun and Get Different Results
If the same script yields different results, check these likely causes:
- ❌ Seed mismatch: Not all libraries (e.g., CuBLAS) use the same RNG.
- ⚙️ Non-deterministic kernels: GPU ops like convolution can have random parallelization paths.
- 🔢 Mixed Precision Noise: Tiny rounding differences from FP16 arithmetic.
- 🧮 Batch order variation: Different random shuffles in DataLoaders.
- 💾 Data corruption or random splits: Not fixing dataset partitioning seeds.
Solution Checklist: ✅ Fix seeds in all frameworks. ✅ Use deterministic flags in CUDA/cuDNN. ✅ Log dataset versions and shuffle states. ✅ Use consistent environments via Docker or Conda.
requirements.txt) and save exact package versions — minor version changes can subtly alter results.⚖️ Step 5: Strengths, Limitations & Trade-offs
✅ Strengths
- Enables reproducible research and debugging.
- Facilitates collaboration and model comparisons.
- Reduces wasted compute by avoiding redundant runs.
⚠️ Limitations
- Full determinism slows performance (some optimizations disabled).
- Logging frameworks can be heavy in distributed setups.
- Over-tracking (too much logging) clutters analysis.
⚖️ Trade-offs
- Choose between speed vs. reproducibility — full determinism adds overhead.
- Light logging is fine for exploration; heavy logging is essential for final runs.
- The goal is consistent conclusions, not byte-for-byte identical weights.
🚧 Step 6: Common Misunderstandings
🚨 Common Misunderstandings (Click to Expand)
- “Random seeds alone guarantee reproducibility.” ❌ GPU nondeterminism can still sneak in.
- “Reproducibility means exact same weights.” ❌ It often means same behavior, not identical tensors.
- “Logging is optional.” ❌ Unlogged experiments are lost forever — especially in large teams.
🧩 Step 7: Mini Summary
🧠 What You Learned: Reproducibility and tracking ensure every experiment’s outcome can be explained, compared, and replicated.
⚙️ How It Works: Seed control, experiment logging, and dataset versioning form the trifecta of consistent ML workflows.
🎯 Why It Matters: In large-scale LLM development, unreproducible results aren’t progress — they’re noise.