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.ai.
For scripts and interactive notebooks, see https://github.com/wandb/examples.
For reference documentation, see https://docs.wandb.com/ref/python.
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.
agent(...): Run a function or program with configuration parameters specified by server.
controller(...): Public sweep controller constructor.
finish(...): Mark a run as finished, and finish uploading all data.
init(...): Start a new run to track and log to W&B.
log(...): Log a dictionary of data to the current run's history.
login(...): Log in to W&B.
save(...): Ensure all files matching
glob_str are synced to wandb with the policy specified.
sweep(...): Initialize a hyperparameter sweep.
watch(...): Hook into the torch model to collect gradients and the topology.