W&B Quickstart

Install W&B and start tracking your machine learning experiments in minutes.

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

1. Click your user profile icon in the upper right corner.
2. Select User Settings, then scroll to the API Keys section.
3. Click Reveal. Copy the displayed API key. To hide the API key, reload the page.

Install the wandb library and log in

To install the wandb library locally and log in:

• Command Line
• Python
• Python notebook

pip install wandb

import wandb
wandb.login()

!pip install wandb

import wandb
wandb.login()

Start a run and track hyperparameters

Initialize a W&B Run object in your Python script or notebook with wandb.init() and pass a dictionary to the config parameter with key-value pairs of hyperparameter names and values:

run = wandb.init(
    # Set the project where this run will be logged
    project="my-awesome-project",
    # Track hyperparameters and run metadata
    config={
        "learning_rate": 0.01,
        "epochs": 10,
    },
)

A run is the basic building block of W&B. You will use them often to track metrics, create logs, and more.

Put it all together

Putting it all together, your training script might look similar to the following code example. The highlighted code shows W&B-specific code. Note that we added code that mimics machine learning training.

# train.py
import wandb
import random  # for demo script

# highlight-next-line
wandb.login()

epochs = 10
lr = 0.01

# highlight-start
run = wandb.init(
    # Set the project where this run will be logged
    project="my-awesome-project",
    # Track hyperparameters and run metadata
    config={
        "learning_rate": lr,
        "epochs": epochs,
    },
)
# highlight-end

offset = random.random() / 5
print(f"lr: {lr}")

# simulating a training run
for epoch in range(2, epochs):
    acc = 1 - 2**-epoch - random.random() / epoch - offset
    loss = 2**-epoch + random.random() / epoch + offset
    print(f"epoch={epoch}, accuracy={acc}, loss={loss}")
    # highlight-next-line
    wandb.log({"accuracy": acc, "loss": loss})

# run.log_code()

That’s it. Navigate to the W&B App at https://wandb.ai/home to view how the metrics we logged with W&B (accuracy and loss) improved during each training step.

The image above (click to expand) shows the loss and accuracy that was tracked from each time we ran the script above. Each run object that was created is show within the Runs column. Each run name is randomly generated.

What’s next?

Explore the rest of the W&B ecosystem.

1. Check out W&B Integrations to learn how to integrate W&B with your ML framework such as PyTorch, ML library such as Hugging Face, or ML service such as SageMaker.
2. Organize runs, embed and automate visualizations, describe your findings, and share updates with collaborators with W&B Reports.
3. Create W&B Artifacts to track datasets, models, dependencies, and results through each step of your machine learning pipeline.
4. Automate hyperparameter search and explore the space of possible models with W&B Sweeps.
5. Understand your datasets, visualize model predictions, and share insights in a central dashboard.
6. Navigate to W&B AI Academy and learn about LLMs, MLOps and W&B Models from hands-on courses.