start
will be loggedWandbExampleFlow
will log datasets and models by default -- the same as decorating each step with @wandb_log(datasets=True, models=True)
@wandb_log
, you will override the flow-level decoration.start
and mid
will log datasets and models, butend
will not log datasets or models.wandb
client library, via the web app UI, or programmatically using our Public API. Parameter
s are saved to W&B's config
and can be found in the Overview tab. datasets
, models
, and others
are saved to W&B Artifacts and can be found in the Artifacts tab. Base python types are saved to W&B's summary
dict and can be found in the Overview tab. See our guide to the Public API for details on using the API to get this information programmatically from outside .Parameter(...)
wandb.config
datasets
, models
, others
wandb.use_artifact("{var_name}:latest")
dict
, list
, str
, etc.)wandb.summary
wandb_log
kwargsdatasets
True
: Log instance variables that are a datasetFalse
models
True
: Log instance variables that are a modelFalse
others
True
: Log anything else that is serializable as a pickleFalse
settings
wandb.Settings(...)
: Specify your own wandb
settings for this step or flowNone
: Equivalent to passing wandb.Settings()
settings.run_group
is None
, it will be set to {flow_name}/{run_id}
settings.run_job_type
is None
, it will be set to {run_job_type}/{step_name}
wandb_log
only logs instance variables. Local variables are NEVER logged. This is useful to avoid logging unnecessary data.dict, list, set, str, int, float, bool
datasets
pd.DataFrame
pathlib.Path
models
nn.Module
sklearn.base.BaseEstimator
others
self.accuracy
float
datasets=True
self.df
pd.DataFrame
datasets=False
self.df
pd.DataFrame
accuracy
float
df
pd.DataFrame