Python Library

​​
View source on GitHub​
Use wandb to track machine learning work.
The most commonly used functions/objects are:
wandb.init — initialize a new run at the top of your training script
wandb.config — track hyperparameters and metadata
wandb.log — log metrics and media over time within your training loop
For guides and examples, see https://docs.wandb.com/guides.
For scripts and interactive notebooks, see https://github.com/wandb/examples.
For reference documentation, see https://docs.wandb.com/ref/python.
Classes
​class Artifact: Flexible and lightweight building block for dataset and model versioning.
​class Run: A unit of computation logged by wandb. Typically, this is an ML experiment.
Functions
​agent(...): Generic agent entrypoint, used for CLI or jupyter.
​controller(...): Public sweep controller constructor.
​finish(...): Marks a run as finished, and finishes uploading all data.
​init(...): Starts a new run to track and log to W&B.
​log(...): Logs a dictonary of data to the current run's history.
​save(...): Ensure all files matching glob_str are synced to wandb with the policy specified.
​sweep(...): Initialize a hyperparameter sweep.
​watch(...): Hooks into the torch model to collect gradients and the topology.
Other Members
Text
__version__
'0.13.5'
config
​
summary
​

Other Members	Text
`__version__`	`'0.13.5'`
`config`
`summary`

wandb verify

Data Types

Last modified 3mo ago