Python Library
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.
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(...)
: 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.
Other Members | |
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__version__ | '0.16.0' |
config | |
summary |