Weights and Biases overview in MLOps - Commands & Configuration

Introduction

When you train machine learning models, you want to keep track of how well they perform and what settings you used. Weights and Biases helps you save this information automatically so you can compare and improve your models easily.

When you want to record your model training results without writing extra code.

When you need to compare different versions of your machine learning models.

When you want to share your training progress and results with your team.

When you want to visualize metrics like accuracy and loss over time.

When you want to save your model files and training data in one place.

Commands

This command installs the Weights and Biases library so you can use it in your Python projects.

Terminal

pip install wandb

Expected Output

Collecting wandb

  Downloading wandb-0.15.5-py3-none-any.whl (2.0 MB)

Installing collected packages: wandb

Successfully installed wandb-0.15.5

This command lets you log in to your Weights and Biases account from the command line so your data can be saved online.

Terminal

wandb login

Expected Output

wandb: Currently logged in as: example-user (use `wandb login --relogin` to force relogin)

→

--relogin - Force logging in again if you want to switch accounts

Run your training script that uses wandb to track metrics and save model information automatically.

Terminal

python train.py

Expected Output

wandb: Tracking run with ID abc123

wandb: Syncing metrics and model checkpoints

Training complete: accuracy=0.92, loss=0.15

Key Concept

If you remember nothing else, remember: Weights and Biases automatically tracks and saves your machine learning experiments so you can see and compare results easily.

Code Example

MLOps

import wandb

wandb.init(project="example-project")

for epoch in range(3):
    accuracy = 0.8 + epoch * 0.05
    loss = 0.5 - epoch * 0.1
    wandb.log({"epoch": epoch, "accuracy": accuracy, "loss": loss})

print("Training simulation complete")

Common Mistakes

Not running 'wandb login' before starting training

Without logging in, your training data won't be saved to your online account.

Always run 'wandb login' once before running your training scripts.

Forgetting to add 'wandb.init()' in the training script

Weights and Biases won't start tracking your run without this initialization.

Add 'wandb.init(project="my-project")' at the start of your training code.

Summary

Install the wandb library to use Weights and Biases in your projects.

Log in once using 'wandb login' to connect your local machine to your online account.

Add wandb.init() and wandb.log() in your training code to track metrics automatically.

Practice

1. What is the main purpose of Weights and Biases (W&B) in machine learning?

easy

A. To deploy machine learning models to production automatically

B. To write machine learning algorithms from scratch

C. To replace the need for training data

D. To track experiments, log metrics, and visualize results

Solution

1. Step 1: Understand W&B's role

   W&B is designed to help track experiments and log metrics during ML development.

2. Step 2: Identify main features

   It provides visualization and comparison of results in a dashboard, not algorithm creation or deployment.

3. Final Answer:

   To track experiments, log metrics, and visualize results -> Option D

4. Quick Check:

   W&B tracks and visualizes experiments [OK]

Hint: Remember W&B is for tracking and visualizing experiments [OK]

Common Mistakes:

• Confusing W&B with model deployment tools
• Thinking W&B writes ML algorithms
• Assuming W&B replaces training data

2. Which of the following is the correct command to initialize a W&B run in Python?

easy

A. wandb.init(project="my-project")

B. wandb.start(project="my-project")

C. wandb.run(project="my-project")

D. wandb.create(project="my-project")

Solution

1. Step 1: Recall W&B initialization syntax

   The correct function to start a run is wandb.init() with project name as argument.

2. Step 2: Check other options

   Functions like start(), run(), or create() do not exist in W&B API for initialization.

3. Final Answer:

   wandb.init(project="my-project") -> Option A

4. Quick Check:

   Use wandb.init() to start runs [OK]

Hint: Use wandb.init() to start tracking runs [OK]

Common Mistakes:

• Using wandb.start() instead of wandb.init()
• Confusing run() or create() as initialization methods
• Missing the project argument

3. Given this Python snippet using W&B:

import wandb
wandb.init(project="test")
for epoch in range(2):
    wandb.log({"accuracy": 0.8 + 0.1 * epoch})

What will be the logged accuracy values after the loop?

medium

A. [0.9, 1.0]

B. [0.8, 0.9]

C. [0.8, 0.85]

D. [0.7, 0.8]

Solution

1. Step 1: Analyze the loop iterations

   The loop runs for epoch = 0 and epoch = 1 (2 iterations).

2. Step 2: Calculate logged accuracy values

   For epoch 0: 0.8 + 0.1*0 = 0.8; for epoch 1: 0.8 + 0.1*1 = 0.9.

3. Final Answer:

   [0.8, 0.9] -> Option B

4. Quick Check:

   Accuracy logged = [0.8, 0.9] [OK]

Hint: Calculate values for each epoch carefully [OK]

Common Mistakes:

• Miscomputing the accuracy formula
• Confusing loop range endpoints
• Assuming only one iteration

4. You wrote this code snippet to log loss values with W&B but no data appears in the dashboard:

import wandb
wandb.init(project="demo")
for i in range(3):
    loss = 0.5 / (i+1)
    wandb.log(loss)

What is the error?

medium

A. wandb.log() requires a dictionary, not a single value

B. wandb.init() is missing the entity parameter

C. The loop range should start from 1, not 0

D. loss variable is not defined before wandb.log()

Solution

1. Step 1: Check wandb.log() usage

   wandb.log() expects a dictionary with metric names as keys, not a single float value.

2. Step 2: Identify correct logging format

   It should be wandb.log({"loss": loss}) to log the loss metric properly.

3. Final Answer:

   wandb.log() requires a dictionary, not a single value -> Option A

4. Quick Check:

   Always log metrics as dict in wandb.log() [OK]

Hint: Log metrics as dictionaries: wandb.log({"metric": value}) [OK]

Common Mistakes:

• Passing a float directly to wandb.log()
• Forgetting to name the metric in a dict
• Assuming wandb.init() needs entity always

5. You want to compare two models' training runs side-by-side using W&B. Which approach best helps you achieve this?

hard

A. Use separate projects for each model and avoid comparing runs

B. Train models without logging and manually compare saved files

C. Log both models in the same project and use the W&B dashboard to compare runs

D. Log only the best model to reduce clutter

Solution

1. Step 1: Understand W&B project and run organization

   Logging both models in the same project allows easy side-by-side comparison in the dashboard.

2. Step 2: Evaluate other options

   Not logging or using separate projects makes comparison harder; logging only one model loses data.

3. Final Answer:

   Log both models in the same project and use the W&B dashboard to compare runs -> Option C

4. Quick Check:

   Same project logging enables run comparison [OK]

Hint: Use one project for related runs to compare easily [OK]

Common Mistakes:

• Not logging runs for comparison
• Using separate projects unnecessarily
• Logging only one model run