Global Functions overview - Weights & Biases Documentation

Global functions in W&B are top-level functions that you call directly, such as wandb.init() or wandb.login(). Unlike methods that belong to specific classes, these functions provide direct access to W&B’s core functionality without needing to instantiate objects first.

Available functions

Function	Description
`init()`	Start a new run to track and log to W&B. This is typically the first function you’ll call in your ML training pipeline.
`login()`	Set up W&B login credentials to authenticate your machine with the platform.
`setup()`	Prepare W&B for use in the current process and its children. Useful for multi-process applications.
`teardown()`	Clean up W&B resources and shut down the backend process.
`sweep()`	Initialize a hyperparameter sweep to search for optimal model configurations.
`agent()`	Create a sweep agent to run hyperparameter optimization experiments.
`controller()`	Manage and control sweep agents and their execution.
`restore()`	Restore a previous run or experiment state for resuming work.
`finish()`	Finish a run and clean up resources.

Example

The most common workflow begins with authenticating with W&B, initializing a run, and logging values (such as accuracy and loss) from your training loop. The first steps are to import wandb and use the global functions login() and init():

import wandb

# Authenticate with W&B
wandb.login()

# Hyperparameters and metadata
config = {
   "learning_rate": 0.01,
   "epochs": 10,
}

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

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

​Available functions

​Example

Available functions

Example