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title Log

wandb.sdk.wandb_run

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Run Objects

class Run(object)

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The run object corresponds to a single execution of your script, typically this is an ML experiment. Create a run with wandb.init().

In distributed training, use wandb.init() to create a run for each process, and set the group argument to organize runs into a larger experiment.

Currently there is a parallel Run object in the wandb.Api. Eventually these two objects will be merged.

Attributes:

  • history History - Time series values, created with wandb.log(). History can contain scalar values, rich media, or even custom plots across multiple steps.
  • summary Summary - Single values set for each wandb.log() key. By default, summary is set to the last value logged. You can manually set summary to the best value, like max accuracy, instead of the final value.

log

 | log(data, step=None, commit=None, sync=None)

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Log a dict to the global run's history.

wandb.log can be used to log everything from scalars to histograms, media and matplotlib plots.

The most basic usage is wandb.log({'train-loss': 0.5, 'accuracy': 0.9}). This will save a history row associated with the run with train-loss=0.5 and accuracy=0.9. The history values can be plotted on app.wandb.ai or on a local server. The history values can also be downloaded through the wandb API.

Logging a value will update the summary values for any metrics logged. The summary values will appear in the run table at app.wandb.ai or a local server. If a summary value is manually set with for example wandb.run.summary["accuracy"] = 0.9 wandb.log will no longer automatically update the run's accuracy.

Logging values don't have to be scalars. Logging any wandb object is supported. For example wandb.log({"example": wandb.Image("myimage.jpg")}) will log an example image which will be displayed nicely in the wandb UI. See https://docs.wandb.com/library/reference/data_types for all of the different supported types.

Logging nested metrics is encouraged and is supported in the wandb API, so you could log multiple accuracy values with wandb.log({'dataset-1': {'acc': 0.9, 'loss': 0.3} ,'dataset-2': {'acc': 0.8, 'loss': 0.2}}) and the metrics will be organized in the wandb UI.

W&B keeps track of a global step so logging related metrics together is encouraged, so by default each time wandb.log is called a global step is incremented. If it's inconvenient to log related metrics together calling wandb.log({'train-loss': 0.5, commit=False}) and then wandb.log({'accuracy': 0.9}) is equivalent to calling wandb.log({'train-loss': 0.5, 'accuracy': 0.9})

wandb.log is not intended to be called more than a few times per second. If you want to log more frequently than that it's better to aggregate the data on the client side or you may get degraded performance.

Arguments:

  • row dict, optional - A dict of serializable python objects i.e str, ints, floats, Tensors, dicts, or wandb.data_types
  • commit boolean, optional - Save the metrics dict to the wandb server and increment the step. If false wandb.log just updates the current metrics dict with the row argument and metrics won't be saved until wandb.log is called with commit=True.
  • step integer, optional - The global step in processing. This persists any non-committed earlier steps but defaults to not committing the specified step.
  • sync boolean, True - This argument is deprecated and currently doesn't change the behaviour of wandb.log

Examples:

Basic usage

- `wandb.log({'accuracy'` - 0.9, 'epoch': 5})

Incremental logging

- `wandb.log({'loss'` - 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
- `wandb.log({'accuracy'` - 0.8})

Histogram

- `wandb.log({"gradients"` - wandb.Histogram(numpy_array_or_sequence)})

Image

- `wandb.log({"examples"` - [wandb.Image(numpy_array_or_pil, caption="Label")]})

Video

- `wandb.log({"video"` - wandb.Video(numpy_array_or_video_path, fps=4,
format="gif")})

Matplotlib Plot

- `wandb.log({"chart"` - plt})

PR Curve

- `wandb.log({'pr'` - wandb.plots.precision_recall(y_test, y_probas, labels)})

3D Object

wandb.log({"generated_samples":
[wandb.Object3D(open("sample.obj")),
wandb.Object3D(open("sample.gltf")),
wandb.Object3D(open("sample.glb"))]})

For more examples, see https://docs.wandb.com/library/log

Raises:

wandb.Error - if called before wandb.init ValueError - if invalid data is passed