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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.

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Available Functions

FunctionDescription
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.

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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})