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.

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
Service account API key

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

Log in to W&B, then click your user profile icon > User Settings.
Click Create new API key.
Provide a descriptive name for your API key.
Click Create.
Copy the displayed API key immediately and store it securely.

W&B shows the full API key only once, when you create it. After you close the dialog, you cannot view the full API key again. Your settings display only the key ID (the first part of the key). If you lose the full API key, you must create a new one.

For secure storage options, see Store API keys securely.

Install the `wandb` library and log in

Command line
Python
Python notebook

Set the WANDB_API_KEY environment variable:
export WANDB_API_KEY=<your_api_key>
Install the wandb library and log in:
pip install wandb wandb login

Install the W&B Python SDK with pip:
pip install wandb
Import the W&B Python SDK and log in to W&B from your Python script with wandb.login():
import wandb wandb.login()

Use shell escape (!) to install the W&B Python SDK:
!pip install wandb
Import the W&B Python SDK and log in to W&B from your notebook with wandb.login():
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})

For a complete example that simulates a training run and logs accuracy and loss metrics to W&B, see Create a machine learning training experiment.

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

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 your W&B home page to view recorded metrics such as accuracy and loss, and to see how they change 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 a generated name.

Next steps

Explore more W&B features:

Learn about and create your first run.
Track models, datasets, and other files with W&B Artifacts.
Automate hyperparameter searches and optimize models with W&B Sweeps.
Share models, prompts, and datasets with W&B Registry.
Analyze runs, visualize model predictions, and view artifacts in your project’s dashboard.
Summarize findings and share updates with collaborators with W&B Reports.
Trace and evaluate LLM applications with W&B Weave.

Guides

Integrations

Reference

Install the `wandb` library and log in

Initialize a run and track hyperparameters

Create a machine learning training experiment

Next steps

​Sign up and create an API key

​Install the wandb library and log in

​Initialize a run and track hyperparameters

​Create a machine learning training experiment

​Next steps

Sign up and create an API key

Install the `wandb` library and log in

Initialize a run and track hyperparameters

Create a machine learning training experiment

Next steps