課程背景與深度學習可重現性危機

隨著我們從簡單、自包含的模型轉向里程碑專案1所需的複雜多階段架構，以試算表或本地檔案手動追蹤關鍵參數已完全不可持續。這種複雜的工作流程會對開發完整性帶來嚴重風險。

1. 識別重現性的瓶頸

深度學習的工作流程本質上因眾多變數（優化演算法、資料子集、正則化技術、環境差異）而具有高度變異性。若無系統性追蹤，重現特定過去結果——這對於除錯或改進已部署模型至關重要——往往不可能實現。

哪些內容必須被追蹤？

超參數： All configuration settings must be recorded (e.g., Learning Rate, Batch Size, Optimizer choice, Activation function).

環境狀態： Software dependencies, hardware used (GPU type, OS), and exact package versions must be fixed and recorded.

成果與結果： Pointers to the saved model weights, final metrics (Loss, Accuracy, F1 score), and training runtime must be stored.

The "Single Source of Truth" (SSOT)

Systematic experiment tracking establishes a central repository—a SSOT—where every choice made during model training is recorded automatically. This eliminates guesswork and ensures reliable auditability across all experimental runs.

TERMINALbash — tracking-env

> Ready. Click "Run Conceptual Trace" to see the workflow.

EXPERIMENT TRACE Live

Simulate the run to visualize the trace data captured.

Question 1

What is the root cause of the Deep Learning Reproducibility Crisis?

PyTorch's dependence on CUDA drivers.

The sheer number of untracked variables (code, data, hyperparameter, and environment).

The excessive memory usage of large models.

The computational cost of generating artifacts.

Question 2

In the context of MLOps, why is systematic experiment tracking essential for production?

It minimizes the total storage size of model artifacts.

It ensures that the model achieving the reported performance can be reliably reconstructed and deployed.

It speeds up the training phase of the model.

Question 3

Which element is necessary to reproduce a result but is most often forgotten in manual tracking?

The number of epochs run.

The specific versions of all Python libraries and the random seed used.

The name of the dataset used.

The time the training started.

Challenge: Tracking in Transition

Why the transition to formal tracking is non-negotiable.

You are managing 5 developers working on Milestone Project 1. Each developer reports their best model accuracy (88% to 91%) in Slack. No one can reliably tell you the exact combination of parameters or code used for the winning run.

Step 1

What immediate step must be implemented to halt the loss of critical information?

Solution:
Implement a mandatory requirement for every run to be registered with an automated tracking system before results are shared, capturing the full hyperparameter dictionary and Git hash.

Step 2

What benefit does structured tracking provide to the team that a shared spreadsheet cannot?

Solution:
Structured tracking allows automated comparison dashboards, visualizations of parameter importance, and centralized artifact storage, which is impossible with static spreadsheets.