W&B Quickstart

W&B Quickstart

Install W&B to track, visualize, and manage machine learning experiments of any size.

Are you looking for information on W&B Weave? See the Weave Python SDK quickstart or Weave TypeScript SDK quickstart.

Sign up and create an API key

To authenticate your machine with W&B, you need an API key.

To create an API key, select the Personal API key or Service Account API key tab for details.

Personal API key

To create a personal API key owned by your user ID:

1. Navigate to User Settings and log in if necessary.
2. Click Create new API key.
3. Provide a descriptive name for your API key.
4. Click Create.
5. Copy the displayed API key immediately and store it securely.

Service account API key

To create an API key owned by a service account:

1. Navigate to the Service Accounts tab in your team or organization settings.
2. Find the service account in the list.
3. Click the action menu (...), then click Create API key.
4. Provide a name for the API key, then click Create.
5. Copy the displayed API key immediately and store it securely.
6. Click Done.

You can create multiple API keys for a single service account to support different environments or workflows.

The full API key is only shown once at creation time. After you close the dialog, you cannot view the full API key again. Only the key ID (first part of the key) is visible in your settings. If you lose the full API key, you must create a new API key.

For secure storage options, see Store API keys securely.

Install the wandb library and log in

Command Line

1. Set the WANDB_API_KEY environment variable.

   export WANDB_API_KEY=<your_api_key>
   

2. Install the wandb library and log in.

   pip install wandb
   wandb login
   

Python

pip install wandb

import wandb

wandb.login()

Python notebook

!pip install wandb
import wandb
wandb.login()

Initialize a run and track hyperparameters

In your Python script or notebook, initialize a W&B run object with wandb.init(). Use a dictionary for the config parameter to specify hyperparameter names and values. Within the with statement, you can log metrics and other information to W&B.

import wandb

wandb.login()

# Project that the run is recorded to
project = "my-awesome-project"

# Dictionary with hyperparameters
config = {
    'epochs' : 10,
    'lr' : 0.01
}

with wandb.init(project=project, config=config) as run:
    # Training code here
    # Log values to W&B with run.log()
    run.log({"accuracy": 0.9, "loss": 0.1})

See the next section for a complete example that simulates a training run and logs accuracy and loss metrics to W&B.

A run is a core element of W&B. You use runs to track metrics, create logs, track artifacts, and more.

Create a machine learning training experiment

This mock training script logs simulated accuracy and loss metrics to W&B. Copy and paste the following code into a Python script or notebook cell and run it:

import wandb
import random

wandb.login()

# Project that the run is recorded to
project = "my-awesome-project"

# Dictionary with hyperparameters
config = {
    'epochs' : 10,
    'lr' : 0.01
}

with wandb.init(project=project, config=config) as run:
    offset = random.random() / 5
    print(f"lr: {config['lr']}")
    
    # Simulate a training run
    for epoch in range(2, config['epochs']):
        acc = 1 - 2**-config['epochs'] - random.random() / config['epochs'] - offset
        loss = 2**-config['epochs'] + random.random() / config['epochs'] + offset
        print(f"epoch={config['epochs']}, accuracy={acc}, loss={loss}")
        run.log({"accuracy": acc, "loss": loss})

Visit wandb.ai/home to view recorded metrics such as accuracy and loss and how they changed during each training step. The following image shows the loss and accuracy tracked from each run. Each run object appears in the Runs column with generated names.

Next steps

Explore more features of the W&B ecosystem:

1. Read the W&B Integration tutorials that combine W&B with frameworks like PyTorch, libraries like Hugging Face, and services like SageMaker.
2. Organize runs, automate visualizations, summarize findings, and share updates with collaborators using W&B Reports.
3. Create W&B Artifacts to track datasets, models, dependencies, and results throughout your machine learning pipeline.
4. Automate hyperparameter searches and optimize models with W&B Sweeps.
5. Analyze runs, visualize model predictions, and share insights on a central dashboard.
6. Visit W&B AI Academy to learn about LLMs, MLOps, and W&B Models through hands-on courses.
7. Visit weave-docs.wandb.ai to learn how to track track, experiment with, evaluate, deploy, and improve your LLM-based applications using Weave.