Hardware and framework version tracking in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is hardware version tracking in MLOps?

Hardware version tracking means keeping a record of the exact physical devices (like GPUs, CPUs) used during model training or deployment. This helps ensure results can be repeated and problems can be traced.

beginner

Why is framework version tracking important in machine learning projects?

Framework version tracking records the exact versions of software libraries (like TensorFlow, PyTorch) used. This avoids unexpected errors from updates and helps reproduce model results exactly.

intermediate

Name two common tools or methods to track hardware and framework versions.

1. Using environment files like requirements.txt or conda.yml for software versions. 2. Logging hardware details automatically during training with scripts or tools like MLflow.

intermediate

How does tracking hardware versions help in debugging model performance issues?

If a model behaves differently, knowing the hardware version helps check if differences in devices (like GPU models) caused the change. This narrows down the cause faster.

beginner

What is a simple way to capture framework versions in a Python project?

Run the command `pip freeze > requirements.txt` to save all installed package versions. This file can be shared to recreate the same environment.

What does hardware version tracking help with in MLOps?

A Reproducing results and debugging

B Increasing model accuracy automatically

C Reducing training time by half

D Changing model architecture

Hardware version tracking helps reproduce results and debug by knowing the exact devices used.

Which file commonly stores Python package versions for framework tracking?

A requirements.txt

B hardware.log

C model.py

D data.csv

requirements.txt lists installed Python packages and their versions.

Why should you track framework versions in machine learning projects?

A To speed up data loading

B To reduce hardware costs

C To avoid errors from software updates

D To increase dataset size

Tracking framework versions prevents unexpected errors caused by software updates.

Which tool can help log hardware and software details automatically?

A Slack

B Excel

C Photoshop

D MLflow

MLflow can log hardware and software environment details during experiments.

What is NOT a reason to track hardware versions?

A To reproduce training results

B To change the model's loss function

C To debug performance differences

D To understand device compatibility

Changing the model's loss function is unrelated to hardware version tracking.

Explain why tracking both hardware and framework versions is crucial in MLOps.

Think about what can change results between runs.

Describe simple methods to capture hardware and framework versions during a machine learning project.

Consider both software and physical device tracking.