Weights & Biases Quickstart

Install Weights & Biases 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.

Create an account

The first time you sign in, a short setup flow creates your account and connects you to an organization. Which steps you see depends on how you arrive.

Choose a sign-in method

Select Continue with Google, Continue with GitHub, Continue with Apple, or Continue with Microsoft, or sign up with an email address and password.If you already have Weights & Biases credentials, they work here. Select Log in instead of Sign up.

Verify your email

Check the inbox of the email address on your account for a verification link. If it doesn’t arrive, check your spam folder or select Resend email.

Enter your full name, your organization or institution, and a username. Your username becomes your profile URL, so it must be unique. A username can use only letters, numbers, underscores, and hyphens.For How will you use Forge?, select Professional for personal or business projects, or Academic for research or education.To continue, accept the Terms of Service and Privacy Policy.

Join your team

If your email domain matches an organization whose teams are open to you, you can join one of those teams.If you arrived through a team invitation or already belong to a team, you don’t see this step.

Create your organization

Name your organization. The name becomes part of its URL, as in forge.coreweave.com/my-organization.If you joined a team in the previous step, arrived through a team invitation, or already belong to a team, you don’t see this step.

Choose what you're working on

Select one of Train and fine-tune models, Build and evaluate agents, Run inference at scale, or I’m exploring. Your choice determines which page you see next, but it doesn’t restrict which products you can use.

When you finish the setup flow, your account is ready to use.

Create an API key

To authenticate your machine, create 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 Forge, then click your user profile icon and select User settings.
Within API keys, select New key.
Provide a descriptive name for your API key.
Click Create API key.
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. Replace YOUR_API_KEY with the API key you created.
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 Weights & Biases 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 Weights & Biases 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 Weights & Biases.

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 Weights & Biases, see Create a machine learning training experiment.

A run is a core element of Weights & Biases. 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 Weights & Biases. 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 Weights & Biases dashboard 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.

A project workspace in Forge, with seven runs listed in the Runs column and their loss and accuracy charts

Next steps

Explore more Weights & Biases 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 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.

​Create an account

​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

Create an account

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